NLP1/Nayve_Bayes_for_WSD_Dascalescu_Dana.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Nayve_Bayes_for_WSD_Dascalescu_Dana.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYgN4CYqXuQQ",
        "outputId": "5f64394b-f886-4dd7-8046-993200be38a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package senseval to /root/nltk_data...\n",
            "[nltk_data]   Package senseval is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('senseval')\n",
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from functools import reduce\n",
        "from collections import defaultdict\n",
        "\n",
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from nltk.corpus import senseval\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "PmZQyhaFyHQu"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "STOPWORDS_SET = set(nltk.corpus.stopwords.words('english'))"
      ],
      "metadata": {
        "id": "rpHhBji2snVL"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "line_instances = senseval.instances('line.pos')\n",
        "hard_instances = senseval.instances('hard.pos')\n",
        "serve_instances = senseval.instances('serve.pos')\n",
        "interest_instances = senseval.instances('interest.pos')"
      ],
      "metadata": {
        "id": "o4Dsm9pYzJcD"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def senses(word):\n",
        "    \"\"\"\n",
        "      Arguments:\n",
        "        - word: target word - string\n",
        "      Returns:\n",
        "        - list of possiblle senses of the target word `word` from senseval-2 Corpus\n",
        "    \"\"\"\n",
        "    return list(set(i.senses[0] for i in senseval.instances(word + '.pos')))"
      ],
      "metadata": {
        "id": "2vZt8Oqazt-m"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Senses (class labels) of the target word *line*:"
      ],
      "metadata": {
        "id": "ppKOHIQu2Ybo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "line_senses = senses('line')\n",
        "line_senses.sort()\n",
        "line_senses"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GpJIteMm0uv-",
        "outputId": "8f55af31-758d-4732-9bb3-3f7e2e334707"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['cord', 'division', 'formation', 'phone', 'product', 'text']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Senses (class labels) of the target word *hard*:"
      ],
      "metadata": {
        "id": "BtTilLtP2s3D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hard_senses = senses('hard')\n",
        "hard_senses.sort()\n",
        "hard_senses"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4SvOVNB0x5i",
        "outputId": "614a98c7-28ef-46de-b1f7-93519963fc0e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['HARD1', 'HARD2', 'HARD3']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Senses (class labels) of the target word *serve*:"
      ],
      "metadata": {
        "id": "--SF7Tp-2uzJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "serve_senses = senses('serve')\n",
        "serve_senses.sort()\n",
        "serve_senses"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4YaAAuO01do",
        "outputId": "48986cc3-6bb8-4f06-844d-d1a183e10e09"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SERVE10', 'SERVE12', 'SERVE2', 'SERVE6']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Senses (class labels) of the target word *interest*:"
      ],
      "metadata": {
        "id": "n6uI8r432w9Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "interest_senses = senses('interest')\n",
        "interest_senses.sort()\n",
        "interest_senses"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGGosSPs03uY",
        "outputId": "0c290ee0-7b54-4c42-bb81-d7f6795a9f72"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['interest_1',\n",
              " 'interest_2',\n",
              " 'interest_3',\n",
              " 'interest_4',\n",
              " 'interest_5',\n",
              " 'interest_6']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for idx, instance in enumerate(line_instances[:500]):\n",
        "  if idx % 25 == 0:\n",
        "    pos = instance.position\n",
        "    left_context = ' '.join(w for (w,_) in instance.context[pos-7:pos])\n",
        "    word = instance.context[pos][0]\n",
        "    right_context = ' '.join(w for (w,_) in instance.context[pos+1:pos+8])\n",
        "    senses = ' '.join(instance.senses)\n",
        "\n",
        "    print('%25s | %5s | %25s -> %s' % (left_context, word, right_context, senses))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jaOuV_lZ3EEM",
        "outputId": "ebe227b2-eb4f-41ca-d6aa-c268d28d8e73"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "suggestion that the company install special safety | lines | inside the a-frame structure it was building -> cord\n",
            "company makes glass-fiber pleasure boats , monofilament | lines |            and antennas . -> cord\n",
            "carlos turned his back and rebaited his |  line |                         . -> cord\n",
            ". instead , a series of harp-like | lines | were secured from the top of the -> cord\n",
            "making short portages and letting down with | lines |                         . -> cord\n",
            "once he felt a tug on his |  line |                         . -> cord\n",
            "bait on the hooks dangling from the | lines |    of the fishing poles . -> cord\n",
            "swing out to the ends of our | lines |    as if we were flying . -> cord\n",
            "\" he cried , reeling in his |  line |                         . -> cord\n",
            "her wash of many colors on the |  line | a shirt , another shirt , overalls -> cord\n",
            "\" men struggled to position the heavy |  line | ; once more the capstan clanked its -> cord\n",
            "white pennants and hoisted them on a |  line | fastened to the protruding periscope head . -> cord\n",
            "exercise , so we rigged up a |  line | between two trees about 40 feet apart -> cord\n",
            "the waves subjected it . if that |  line | should part , everything else , including -> cord\n",
            ". jesse stood up and took the | lines |             from george . -> cord\n",
            "service \" that would cut across traditional | lines |   of military authority . -> division\n",
            "either . in light of the blurred |  line | between the banking and securities industries , -> division\n",
            ", may be about to cross the |  line | between writing history and making it . -> division\n",
            "substantial number of political districts along racial | lines | has gone virtually unnoticed by congress and -> division\n",
            "    \" it 's a fine , fine |  line |   you 've got to walk , \" -> division\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for idx, instance in enumerate(hard_instances[:500]):\n",
        "  if idx % 25 == 0:\n",
        "    pos = instance.position\n",
        "    left_context = ' '.join(w for (w,_) in instance.context[pos-7:pos])\n",
        "    word = instance.context[pos][0]\n",
        "    right_context = ' '.join(w for (w,_) in instance.context[pos+1:pos+8])\n",
        "    senses = ' '.join(instance.senses)\n",
        "\n",
        "    print('%25s | %5s | %25s -> %s' % (left_context, word, right_context, senses))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikfFqCSy54Ob",
        "outputId": "285ff6ce-336e-488b-d9a1-31a9ab11c9bb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "him to defeat him and that 's |  hard |                to do . '' -> HARD1\n",
            "you run by yourself , it 's |  hard |     to get motivated . '' -> HARD1\n",
            "are different and the choice is far | harder |                         . -> HARD1\n",
            "                          |  hard | to rest him , but when you -> HARD1\n",
            "                          |  hard | to know which psychological complex -- the -> HARD1\n",
            "                          |  hard |    to get used to , but i -> HARD1\n",
            "says , `` and that 's really |  hard | to do when you 're on your -> HARD1\n",
            "                        a |  hard |                   match . -> HARD1\n",
            "                          |  hard | to build consistency when players come and -> HARD1\n",
            "                          |  hard | to feel sorry for brown and his -> HARD1\n",
            "backup performer , that makes it doubly |  hard |          to choose them . -> HARD1\n",
            "                          |  hard | , because they ask me to do -> HARD1\n",
            "steps companies are taking to deal with | harder |          economic times . -> HARD1\n",
            "such stocks , but it is not |  hard | to spot rallies among stocks that had -> HARD1\n",
            "                          |  hard | for me to answer , \" moss -> HARD1\n",
            "-- eyeing expansion , it 's not |  hard | to imagine a day in the far -> HARD1\n",
            "                          |  hard | to believe that , according to recent -> HARD1\n",
            "refusing to extend credit because it 's |  hard | to take off the personal hat and -> HARD1\n",
            "but i came back and that 's |  hard |            , \" she adds . -> HARD1\n",
            "ways than the back problem , but | harder | in other ways , \" montana said -> HARD1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for idx, instance in enumerate(serve_instances[:500]):\n",
        "  if idx % 25 == 0:\n",
        "    pos = instance.position\n",
        "    left_context = ' '.join(w for (w,_) in instance.context[pos-7:pos])\n",
        "    word = instance.context[pos][0]\n",
        "    right_context = ' '.join(w for (w,_) in instance.context[pos+1:pos+8])\n",
        "    senses = ' '.join(instance.senses)\n",
        "\n",
        "    print('%25s | %5s | %25s -> %s' % (left_context, word, right_context, senses))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tnQoqHqD5-Ui",
        "outputId": "392ee848-9676-43be-85f8-5be34db760ce"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sweet fruit , it seems wiser to | serve | it plain with a good sharp cheese -> SERVE10\n",
            "top with slices of the meat . | serve | with boiled potatoes steamed dry , hot -> SERVE10\n",
            "my carriage and horses . don 't | serve | the dinner till seven o 'clock . -> SERVE10\n",
            "minutes . remove from the oven and | serve |              piping hot . -> SERVE10\n",
            "eaten at the start are quite correctly | served |      in their open tins . -> SERVE10\n",
            "browned potatoes and yorkshire pudding you might | serve |         rosemary butter . -> SERVE10\n",
            "tablespoon chopped parsley , and just before | serving | add the 2 chopped hard-boiled eggs . -> SERVE10\n",
            ". red carpets were spread and champagne | served | underground to visiting notables . -> SERVE10\n",
            "put back in place . meals were | served | punctually and all the children were expected -> SERVE10\n",
            "way into a greek 's where they | served | both liquor and ice cream . -> SERVE10\n",
            "   ( 15 -- 20 minutes ) . | serve | wrapped in a napkin to retain heat -> SERVE10\n",
            "for large mold , 4 hours . | serve | hot with or without custard sauce . -> SERVE10\n",
            "is most often a boned stuffed chicken | served | only with fruit salad . \" filipinos -> SERVE10\n",
            "'s more , \" the giant blueberries | served | in ritzy hotels and restaurants are puny -> SERVE10\n",
            "sold out by the time he was | served |                         . -> SERVE10\n",
            "sugar after you turn the pineapple . | serve | plain or flavored with a little rum -> SERVE10\n",
            "down on cake plate or rack . | serve |            warm or cold . -> SERVE10\n",
            "again . the colonel 's breakfast was | served | to him in the same dingy and -> SERVE10\n",
            "don 't let it stand around before | serving | . it must be served cold . -> SERVE10\n",
            "                          | serve | in a big bowl with arrangements to -> SERVE10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for idx, instance in enumerate(interest_instances[:500]):\n",
        "  if idx % 25 == 0:\n",
        "    pos = instance.position\n",
        "    left_context = ' '.join(w for (w,_) in instance.context[pos-7:pos])\n",
        "    word = instance.context[pos][0]\n",
        "    right_context = ' '.join(w for (w,_) in instance.context[pos+1:pos+8])\n",
        "    senses = ' '.join(instance.senses)\n",
        "\n",
        "    print('%25s | %5s | %25s -> %s' % (left_context, word, right_context, senses))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzIuwe4_6HG_",
        "outputId": "f3398a7b-b7d1-44b2-bac8-ac0a1a81a1d9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "that portfolio managers expect further declines in | interest |                   rates . -> interest_6\n",
            "   . : `` we have a great | interest | in making investments , particularly in new -> interest_1\n",
            "its common stock to the mcalpine family | interests |    , for $ 1 million , or -> interest_5\n",
            "the judge 's loan , lowered its | interest | rate and accepted a trade-in that had -> interest_6\n",
            "totally different '' from problems in the | interest |       rate options unit . -> interest_6\n",
            "just days before the federal reserve raised | interest | rates , ibm offered $ 1 billion -> interest_6\n",
            "                          | interest | that nyu is host to the first -> interest_1\n",
            "cray research will retain a 10 % | interest | in the new company , which will -> interest_5\n",
            "emphasizing financial flows in response to high | interest |                   rates . -> interest_6\n",
            "it would not be in the best | interest | of cbs news to comment on a -> interest_4\n",
            ", apparently stems from japanese investors ' | interest | in buying dollars against the yen to -> interest_1\n",
            "about $ 2 million a year in | interest |                 expense . -> interest_6\n",
            "market , that might create some investor | interest |      in precious metals . -> interest_1\n",
            "                          | interests | outweighed the * interests of the copyright -> interest_4\n",
            "whose total return ( bond-price changes plus | interest | ) has amounted to a negative 6 -> interest_6\n",
            "to cover its fixed charges , including | interest | on debt securities , '' by approximately -> interest_6\n",
            "while the student was in school , | interest | costs would either be paid by the -> interest_6\n",
            "and the implication of lower inflation and | interest | rates tend to bolster bond prices . -> interest_6\n",
            "that the dollar will remain strong and | interest |      rates will decline . -> interest_6\n",
            "                          | interest | rates paid depositors were lowered , thrift -> interest_6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Naive Bayes Classifier"
      ],
      "metadata": {
        "id": "DPhzYDKDYadS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "3c7fiqW-hSsx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Training**\n",
        "\n",
        "According to the Naive Bayes classifier assumption, each attribute of a feature vector will be treated as an independent attribute in order to determine the likelihood that the feature vector X belongs to class c.\n",
        "\n",
        "$$ P(c|X) = P(c) \\prod_{i=1}^{I} {P(x_i|c)} \\hspace{4mm} | \\hspace{1mm} _\\text{apply logarithm} \\hspace{1mm} \\Leftrightarrow $$ \n",
        "$$ log(P(c|X)) \\simeq log(P(c)) + \\sum_{i=1}^{I} log(P(x_i|c)) $$\n",
        "\n",
        "To apply the Naive Bayes rule we need:\n",
        "\n",
        "\n",
        "1.   the probability that an example is in class c\n",
        "$$ P(c) = \\frac{\\text{number of examples in class `c`}}{\\text{total number of examples}} $$\n",
        "2.   the probability of having attribute x in class c\n",
        "$$ P(X|c) = \\frac{\\text{the number of examples in class `c` that are equal to `X`}}{\\text{number of examples in class `c`}}$$\n",
        "\n",
        "\n",
        "\n",
        "**2. Predicting labels based on the learned classifier**\n",
        "$$ P(c|X) = P(c) \\times \\prod_{i=1}^{I} {P(x_i | c)} \\hspace{1mm}, $$\n",
        "\n",
        "$\\hspace{4mm}$ where $ X = \\{x_1, x_2, ..., x_n\\} $ with $x_1, \\dots, x_n$ independent attributes.\n",
        "\n",
        "The probability that example $X$ is in class $c$, is obtained by multiplying (or adding logarithms) the individual probabilities of its attributes conditioned by class $c$. For each class $c$, we'll calculate $P(c | X)$, and the assigned label will be determined by class with the highest probability."
      ],
      "metadata": {
        "id": "AwTL7G4chWoT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Naive Bayes Classifier for Word Sense Disambiguation"
      ],
      "metadata": {
        "id": "80Dq7uHbYc1H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Notations:*\n",
        "\n",
        "*   $ alphas \\stackrel{\\text{not}}{=} P (s_k) = \\frac{|\\text{occurences of sense } s_k \\text{ in corpus } \\mathcal{C}|}{|\\text{occurences of } w \\text{ in corpus } \\mathcal{C}|} , \\hspace{1mm} \\text{ for } k=\\overline{1, K}$,\n",
        "\n",
        "$\\hspace{6mm}$ where $K$ is the number of ambiguous senses in the corpus; $s_k$ is the sense of class $k$.\n",
        "*   $ thetas \\stackrel{\\text{not}}{=} P(v_j|s_k) , \\hspace{1mm} \\text{ for } k=\\overline{1, K}, j =\\overline{1, J}$,\n",
        "\n",
        "$\\hspace{6mm}$ where $J$ is the number of feature vectors $v$.\n"
      ],
      "metadata": {
        "id": "ez_QM-0jwLcF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the frequency of words in the training data, we may estimate the likelihood of different senses of words occurring in a given context."
      ],
      "metadata": {
        "id": "_ENY4fSjtUxU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook contains statistics and results of all experiments for all four target words (\"line\", \"hard\", \"serve\", \"interest\"). Experiments include different feature extraction techniques (word frequency, surrounding context of target word, pos_tags of words surrounding target word)."
      ],
      "metadata": {
        "id": "1F9WKP7YMbSV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sense_instances(instances, sense):\n",
        "  \"\"\"\n",
        "    Returns the list of instances in `instances` that have the sense `sense`\n",
        "  \"\"\"\n",
        "  return [instance for instance in instances if instance.senses[0] == sense]"
      ],
      "metadata": {
        "id": "XBROD0NVHwxh"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Class distribution for word *line*:"
      ],
      "metadata": {
        "id": "kMcP_hrMHfya"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "line_instances_sense = [sense_instances(line_instances, sense) for sense in line_senses] # `line.pos` instances group by senses"
      ],
      "metadata": {
        "id": "QquwVjSYwPbw"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_samples_per_class_line = [len(instances_group) for instances_group in line_instances_sense]\n",
        "num_samples_per_class_line"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGA6moE7JmNM",
        "outputId": "6ca9ba6e-656c-49df-fe12-60015062d5e1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[373, 374, 349, 429, 2217, 404]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_num_line = pd.DataFrame({'label': line_senses, 'num_samples': num_samples_per_class_line})"
      ],
      "metadata": {
        "id": "uY57KdhvP7OC"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.barplot(x='num_samples', y='label', data=df_num_line,\n",
        "                 palette = \"Blues\",\n",
        "                 orient=\"h\")\n",
        "ax.set(xlabel='Number of samples with given sense', ylabel='Senses')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "9I7FE6wCNmEC",
        "outputId": "0b3e5074-0a0b-41b8-b720-079fe09d9919"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Class distribution for word *hard*:"
      ],
      "metadata": {
        "id": "9JQ3J1JsHnuD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hard_instances_sense = [sense_instances(hard_instances, sense) for sense in hard_senses] # `hard.pos` instances group by senses"
      ],
      "metadata": {
        "id": "3hRT1IZ9HqlN"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_samples_per_class_hard = [len(instances_group) for instances_group in hard_instances_sense]\n",
        "num_samples_per_class_hard"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pp5CbXeMEOk",
        "outputId": "da48f47d-358c-4937-d64e-660c2eae1f4b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3455, 502, 376]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_num_hard = pd.DataFrame({'label': hard_senses, 'num_samples': num_samples_per_class_hard})"
      ],
      "metadata": {
        "id": "sSMqOFKNQgsO"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.barplot(x='num_samples', y='label', data=df_num_hard,\n",
        "                 palette = \"Blues\",\n",
        "                 orient=\"h\")\n",
        "ax.set(xlabel='Number of samples with given sense', ylabel='Senses')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "RlZPwmu-QnVQ",
        "outputId": "8f49e28c-f163-4d38-9ea6-8becc9579190"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEGCAYAAABGnrPVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYfElEQVR4nO3deZQmdX3v8fdnhkXCImsUcRncLpCAyB0jLrhxRTQxeiMuc9z1XqLXqHgjLtHj0URFw9F4vKNxxVFDJBpFjEvEBUQ9CgIOw7CKQETcIy4YHQW+94/6tfPw0N3TPfPrfqaZ9+ucPl1Vv+ep+lY9Tz+frl/VU5WqQpKkXpZNugBJ0q2LwSJJ6spgkSR1ZbBIkroyWCRJXW036QImbe+9964VK1ZMugxJWjLOO++8n1TVPjO1b/PBsmLFCs4999xJlyFJS0aS/5it3a4wSVJXBoskqSuDRZLUlcEiSerKYJEkdWWwSJK62uZPN/6vDTdw7hU/nnQZkrRoVt59xq+gdOEeiySpK4NFktSVwSJJ6spgkSR1ZbBIkroyWCRJXRkskqSuDBZJUlcGiySpK4NFktSVwSJJ6spgkSR1ZbBIkroyWCRJXRkskqSuDBZJUlcGiySpK4NFktSVwSJJ6spgkSR1ZbBIkroyWCRJXRkskqSuDBZJUlcLFixJrh8bf0aS1WPT1iY5ZWzamiRXtbYLkhw50nZmksuSrEtyaZLVSXYfaT8pyY+SrF+o9ZIkzW5ieyxJDgSWA0ck2Xms+fiqOhQ4DnjHWNuTq+oQ4BBgA3DaSNsa4OiFqViSNBeT7ApbBXwQOB14zAyP+Rqw33QNVfVb4CXAnZPcq007C/hp/1IlSXO13QLOe6cka0fG9wQ+MTL+RODhwAHA84F/nmYeRwMfn2kBVXVjkgvaPC7Y4oolSVtsIYPl1607CxiOsQAr2/BK4CdV9Z0k1wInJdmzqqb2Nk5M8nrgjsD9NrGczLewJMcCxwLc/g53nO/TJUmzmFRX2CrggCRXA98GdgMeN9J+fFXdE3gpcNJMM0myHDgYuGQ+C6+qd1XVyqpauceee823dknSLBY9WJIsA54AHFxVK6pqBcMxllXTPHw1sCzJI6aZz/bACcA1VbVuAUuWJM3DJPZYjgCurarvjUw7Czgoyb6jD6yqAl7LcJB+yslJ1gHrgZ0ZOfCf5EMMB/z/W5LvJnn2Aq2DJGkGGT67t10HHXxofeDUz026DElaNCvvvs8WPT/JeVW1cqZ2v3kvSerKYJEkdWWwSJK6MlgkSV0ZLJKkrgwWSVJXBoskqSuDRZLUlcEiSerKYJEkdWWwSJK6MlgkSV0ZLJKkrgwWSVJXBoskqSuDRZLUlcEiSerKYJEkdWWwSJK6MlgkSV0ZLJKkrgwWSVJXBoskqSuDRZLU1XaTLmDS/mDH7Vh5930mXYYk3Wq4xyJJ6spgkSR1ZbBIkroyWCRJXRkskqSuDBZJUlcGiySpK4NFktSVwSJJ6spgkSR1ZbBIkroyWCRJXRkskqSutvmrG1973a94+UfOnnQZ83bC4+876RIkaVrusUiSujJYJEldGSySpK4MFklSVwaLJKkrg0WS1JXBIknqymCRJHVlsEiSujJYJEldGSySpK4MFklSVwaLJKkrg0WS1NWcgiXJC5PslsF7k5yf5KiFLk6StPTMdY/lWVX1C+AoYA/gqcAbFqwqSdKSNddgSfv9KOCDVXXRyDRJkn5vrsFyXpLTGYLls0l2BW5auLIkSUvVXG9N/GzgUODKqvqvJHsBz1y4siRJS9Vc91gKOAh4QRvfGbjNglQkSVrS5hosbwfuB6xq478E3rYgFUmSlrS5doXdt6oOS/JNgKq6LskOC1iXJGmJmusey++SLGfoEiPJPnjwXpI0jbkGy1uBU4E/TPI64CvA6xesKknSkjWnrrCqOjnJecCRDN9feWxVXbKglUmSlqS5XtLlbsBVVfU2YD3w8CS7b+I514+NPyPJ6rFpa5OcMjZtTZKrWtsFSY4caTszyWVJ1iW5NMnqqTqS3CnJGUkuTnJRkhfOZd0kSX3NtSvso8CNSe4OvBO4E/DPW7LgJAcCy4Ejkuw81nx8VR0KHAe8Y6ztyVV1CHAIsAE4rU2/AfjrqjoIOBx4XpKDtqRGSdL8zTVYbqqqG4C/AFZX1fHAvlu47FXAB4HTgcfM8JivAftN11BVvwVeAtw5yb2q6vtVdX5r+yVwyUzPlSQtnLmebvy7JKuApwGPbtO238RzdkqydmR8T+ATI+NPBB4OHAA8n+n3gI4GPj7TAqrqxiQXtHlcMDU9yQrg3sDZ0z0vybHAsQC77X37TayGJGk+5rrH8kyGL0i+rqquSrI/w97GbH5dVYdO/QCvmmpIshL4SVV9B/gCcO8ke44898QklzOEzRs3sZybXQwzyS4MXXfHtSsy30JVvauqVlbVyj/YbdZDRZKkeZpTsFTVxVX1gqr6UBu/qqo29YE/m1XAAUmuBr4N7AY8bqT9+Kq6J/BS4KSZZtK+W3MwQ7cXSbZnCJWTq+pjW1CfJGkzzfWssAck+VySy5Nc2c7aunJzFphkGfAE4OCqWlFVKxiOsaya5uGrgWVJHjHNfLYHTgCuqap1SQK8F7ikqt68ObVJkrbcXI+xvBd4EXAecOMWLvMI4Nqq+t7ItLOAg5Lc7ISAqqokr2U4SP/ZNvnkJBuAHYHPs/HA/wMYbkB24cixnb+pqk9vYb2SpHmYa7D8vKo+M58ZV9UuY+NrgDVt9PCxthuBqaPozxhr+yhD9xZV9ZBZlvcVvPmYJE3cXIPljCQnAh9j+O4IAFOn90qSNGXOVzduv1eOTCvgYX3LkSQtdXO9VthDF7oQSdKtw1zPCrtdkvcm+UwbPyjJsxe2NEnSUjTXL0iuYTgr6w5t/HKG63hJknQzcw2Wvavqw7Sbe7Xrhm3paceSpFuhuQbLr5LsxcY7SB4O/HzBqpIkLVlzPSvs/zJcQPJuSb4K7AMcs2BVSZKWrFn3WJLcJ8nt2/dVHgz8DcP3WE4HvrsI9UmSlphNdYW9E/htG74/8ArgbcB1wLsWsC5J0hK1qa6w5VX10zb8ROBdU5dYGbvXiiRJwKb3WJYnmQqfI4EvjrTN9fiMJGkbsqlw+BDwpSQ/AX4NfBkgyd3xrDBJ0jRmDZaqel2SLzDc3/70qqrWtIzhdsKSJN3MJruzqurr00y7fGHKkSQtdXP9gqQkSXNisEiSujJYJEldGSySpK4MFklSVwaLJKkrg0WS1JXBIknqapu/3td+e+zMCY+/76TLkKRbDfdYJEldGSySpK4MFklSVwaLJKkrg0WS1JXBIknqymCRJHVlsEiSujJYJEldGSySpK4MFklSVwaLJKkrg0WS1JXBIknqapu/bP4l1/wnK4/74KTL4Ny3PHXSJUhSF+6xSJK6MlgkSV0ZLJKkrgwWSVJXBoskqSuDRZLUlcEiSerKYJEkdWWwSJK6MlgkSV0ZLJKkrgwWSVJXBoskqSuDRZLUlcEiSerKYJEkdWWwSJK6MlgkSV0ZLJKkrgwWSVJXBoskqSuDRZLUlcEiSerKYJEkdbVgwZLk+rHxZyRZPTZtbZJTxqatSXJVa7sgyZEjbWcmuSzJuiSXJlmdZPfWdpsk57TnXJTkNQu1bpKkmU1sjyXJgcBy4IgkO481H19VhwLHAe8Ya3tyVR0CHAJsAE5r0zcAD6uqewGHAkcnOXzBVkCSNK1JdoWtAj4InA48ZobHfA3Yb7qGqvot8BLgzknuVYOpvaTt20/1LVmStCkLGSw7te6stUnWAn871v5E4BTgQwwhM52jgY/PtICquhG4ADgAIMnytqwfAZ+rqrO3cB0kSfO03QLO+9etOwsYjrEAK9vwSuAnVfWdJNcCJyXZs6p+2h5+YpLXA3cE7reJ5WRqoAXNoe24y6lJ/riq1t/iCcmxwLEAO+y612avoCTplibVFbYKOCDJ1cC3gd2Ax420H19V9wReCpw000ySLAcOBi4ZnV5VPwPOYNjjuYWqeldVrayqldvttOuWrIckacyiB0uSZcATgIOrakVVrWA4xjJdd9hqYFmSR0wzn+2BE4Brqmpdkn1GzhDbCXg4cOkCrYYkaQaT2GM5Ari2qr43Mu0s4KAk+44+sKoKeC3DQfopJydZB6wHdmbjgf99gTNa2zcYjrF8coHWQZI0gwyf3duunW+3fx24avy8gsV37lueOukSJGlOkpxXVStnaveb95KkrgwWSVJXBoskqSuDRZLUlcEiSerKYJEkdWWwSJK6MlgkSV0ZLJKkrgwWSVJXBoskqSuDRZLUlcEiSerKYJEkdWWwSJK6MlgkSV0ZLJKkrgwWSVJXBoskqSuDRZLUlcEiSerKYJEkdWWwSJK6MlgkSV1tN+kCJu3AO+3FuW956qTLkKRbDfdYJEldGSySpK4MFklSVwaLJKkrg0WS1JXBIknqKlU16RomKskvgcsmXcdm2Bv4yaSL2AzWvbise3Et1bphfrXfpar2malxm/8eC3BZVa2cdBHzleRc61481r24rHvx9azdrjBJUlcGiySpK4MF3jXpAjaTdS8u615c1r34utW+zR+8lyT15R6LJKkrg0WS1NU2GyxJjk5yWZIrkrxs0vWMS3J1kguTrE1ybpu2Z5LPJflW+71Hm54kb23rsi7JYYtY50lJfpRk/ci0edeZ5Ont8d9K8vQJ1v7qJNe27b42yaNG2l7ear8sySNGpi/aeynJnZKckeTiJBcleWGbvlVv81nq3qq3d1vebZKck+SCVvtr2vT9k5zd6viXJDu06Tu28Sta+4pNrdMi170myVUj2/zQNr3fe6WqtrkfYDnwbeCuwA7ABcBBk65rrMargb3Hpv098LI2/DLgjW34UcBngACHA2cvYp0PAg4D1m9uncCewJXt9x5teI8J1f5q4MXTPPag9j7ZEdi/vX+WL/Z7CdgXOKwN7wpc3mrbqrf5LHVv1du71RJglza8PXB225YfBp7Upr8DeG4b/j/AO9rwk4B/mW2dJlD3GuCYaR7f7b2yre6x/AlwRVVdWVW/BU4BHjPhmubiMcD72/D7gceOTP9ADb4O7J5k38UoqKrOAn66hXU+AvhcVf20qq4DPgccPaHaZ/IY4JSq2lBVVwFXMLyPFvW9VFXfr6rz2/AvgUuA/djKt/ksdc9kq9jerd6qquvb6Pbtp4CHAf/apo9v86nX4l+BI5NklnVa7Lpn0u29sq0Gy37ANSPj32X2N/kkFHB6kvOSHNum3a6qvt+GfwDcrg1vbesz3zq3tvr/qnUFnDTVpcRWWHvrYrk3w3+iS2abj9UNS2B7J1meZC3wI4YP1m8DP6uqG6ap4/c1tvafA3tNovbxuqtqapu/rm3zf0iy43jdY/XNu+5tNViWggdW1WHAI4HnJXnQaGMN+6hb/bniS6XOEf8I3A04FPg+8KbJljO9JLsAHwWOq6pfjLZtzdt8mrqXxPauqhur6lDgjgx7GQdMuKQ5Ga87yR8DL2eo/z4M3Vsv7b3cbTVYrgXuNDJ+xzZtq1FV17bfPwJOZXgz/3Cqi6v9/lF7+Na2PvOtc6upv6p+2P4YbwLezcauiq2m9iTbM3w4n1xVH2uTt/ptPl3dS2F7j6qqnwFnAPdj6Cqaut7iaB2/r7G13xb4TyZY+0jdR7duyaqqDcD7WIBtvq0GyzeAe7SzOnZgOMD2iQnX9HtJdk6y69QwcBSwnqHGqTMyng6c1oY/ATytndVxOPDzkW6RSZhvnZ8FjkqyR+sKOapNW3Rjx6b+J8N2h6H2J7UzfvYH7gGcwyK/l1pf/XuBS6rqzSNNW/U2n6nurX17txr3SbJ7G94JeDjDMaIzgGPaw8a3+dRrcQzwxbYXOdM6LWbdl478AxKG40Kj27zPe2VzzzhY6j8MZ0BcztBX+opJ1zNW210Zzh65ALhoqj6GftovAN8CPg/sWRvP/nhbW5cLgZWLWOuHGLowfsfQ9/rszakTeBbDwcwrgGdOsPYPttrWtT+0fUce/4pW+2XAIyfxXgIeyNDNtQ5Y234etbVv81nq3qq3d1veIcA3W43rgVe16XdlCIYrgI8AO7bpt2njV7T2u25qnRa57i+2bb4e+Cc2njnW7b3iJV0kSV1tq11hkqQFYrBIkroyWCRJXRkskqSuDBZJUlcGi+YlSSV508j4i5O8utO81yQ5ZtOP3OLlPD7JJUnOWOhlbaKOq5PsvcDL+PO0KwAneWySg0bazkyycgvm/Z7R+UlTDBbN1wbgLxb6A3G+Rr4BPRfPBv53VT10oerZWlTVJ6rqDW30sQxX2O017/9VVRf3mp9uPQwWzdcNDPfGftF4w/geR5Lr2++HJPlSktOSXJnkDUmenOFeERcmudvIbP5HknOTXJ7kz9rzlyc5Mck32oXz/nJkvl9O8gngFh9wSVa1+a9P8sY27VUMX9Z7b5ITxx6/b5KzMtyjYn2SI9r0f2w1/f6eFm361UlOaI8/N8lhST6b5NtJnjNS41lJPpXhHhzvSHKLv7skT2nbY22Sd7Z1Xt626fq2Hi8ae87yDPfVSJLdk9yYdk25tsx7JHlGktVJ7g/8OXBiW8bUNn98W+7lU+s7toxlSd6e5NIM93n59NRrPLXHk+Q5o9tyapkzrdfUeyPJ6zLcK+TrSW43zbIfnI33DPlmNl6N4viR98LUPUZWtL3Qd7fX6fQM3zYnyQsy3AdmXZJT2rSdM1z08pw276VwdfOlY6G/terPresHuB7YjeF+MbcFXgy8urWtYeQ+D8D17fdDgJ8x3JNjR4brDL2mtb0QeMvI8/+d4R+eezB8G/42wLHAK9tjdgTOZbifxUOAXwH7T1PnHYDvAPsA2zF82/ixre1Mprk6AfDXbLzKwXJg1za858i0M4FD2vjVbLwHxz8wfMN517bMH46s+28YvqW9nOHKuMeMPH9v4EDg34Dt2/S3A08D/jvDFWmn6tt9mpr/Hfgj4M8YLnfyiraNrmrtzwBWz/D6nAm8qQ0/Cvj8NPM/Bvh0e01uD1w3Uv+ZwMq2vleMPOczDOE97Xq14QIe3Yb/fur1HVv2vwEPaMO7tNfxKIZ/bNJq+iTDfXVWMPzTc2h7/IeBp7Th77HxW/G7t9+vH2nfneGb/DtP+u/r1vLjHovmrYar0n4AeME8nvaNGi5+t4HhkhGnt+kXMnwoTPlwVd1UVd9iuKHQAQwfJk/LcPnvsxkuX3KP9vhzari3xbj7AGdW1Y9ruHT5yQwfQLPWCDwzwzGjg2u4bwjAE5Kcz3B5jD/i5t1JU9epupDhxki/rKofAxvSrtPUaryyqm5kuIzMA8eWeyRDiHyjreORDEF0JXDXJP8vydHAL7ilL7f1ehBwQpv3fdq6zMXURSzP4+avw5QHAh9pr8kPGK6PdTNtfa9McniSvRhes6/Osl4Av2UIhdmW/VXgzUlewBAINzC8F45ieC3Ob8uaei9cVVVrp5nnOuDkJE9hCB/aPF7W6jqT4R+YO09TgzbDfPqlpVFvYfjDft/ItBto3autu2eHkbYNI8M3jYzfxM3fh+PXGCqG/06fX1U3u/Bdkocw7LF0UVVnta6kPwXWJHkzwwf3i4H7VNV1SdYwfAhNGV2P8XWcWq/p1mlUgPdX1cvHa0pyL4YbLT0HeALDNZtGnQU8l2EP7VXA8Qx7SV+ebV2nqf9Gtuzz4JRW36XAqVVVSWZcL+B31XYXZlp2Vb0hyacY9qa+muFWvgFOqKp3jj42wz1eRrf/jcBObfhPGYL30cArkhzc5vO4qrpsc1ZWs3OPRZulqn7K0N3w7JHJVzP8hwpDf/72mzHrx7d+/bsx/Hd7GcOVVJ+b4bLrJLlnhqs+z+Yc4MFJ9m79+quAL832hCR3YejCejfwHobbFu/GEF4/b8cBHrkZ6/QnGa7Guwx4IvCVsfYvAMck+cNWx55J7pLhBIllVfVR4JWtnunW8/7ATVX1G4aLO/4lQ+CM+yVDV918fBV4XHtNbscQWtM5leEOhKsYQmbG9ZrrgpPcraourKo3MuyBHcDwXnhWhvu6kGS/qfnPMI9lwJ2q6gyG+47clqFb7bPA81v4keTec61Lm+Yei7bEm4C/Ghl/N3BakgsY+v43Z2/iOwwflrsBz6mq3yR5D0O3xvntg+DHbLwN7LSq6vsZTrM9g+G/009V1WmzPYfhQ/P4JL9jOJb0tKq6Ksk3Gf4Tv4bhg3a+vgGsBu7e6jl1rNaLk7yS4Y6hyxiutvw84NfA+7LxYP8t/vOvqg1JrgG+3iZ9meHD/cJp6jgFeHfrWprrad0fZejCuphh/c9nuCPieB3XJbmE4f7z52xivf5jjss+LslDGfb+LgI+09b3QOBrLROuB57CsIcyneXAPyW5LcP74K1V9bMkf8ew172u1XYVw3EqdeDVjaUF1LrrXlxVS/ZDK8kuVXV9O35yDsMB9R9Mui5tvdxjkbQpn2wnIuwA/J2hok1xj0WS1JUH7yVJXRkskqSuDBZJUlcGiySpK4NFktTV/wfGeVCGJSLRPgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Class distribution for word *serve*:"
      ],
      "metadata": {
        "id": "5azaqrG5Hq2C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "serve_instances_sense = [sense_instances(serve_instances, sense) for sense in serve_senses] # `serve.pos` instances group by senses"
      ],
      "metadata": {
        "id": "alO0Yr7PHsk5"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_samples_per_class_serve = [len(instances_group) for instances_group in serve_instances_sense]\n",
        "num_samples_per_class_serve"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kapMbRE1MqiF",
        "outputId": "d16df08c-7737-4430-f609-039924f97175"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1814, 1272, 853, 439]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_num_serve = pd.DataFrame({'label': serve_senses, 'num_samples': num_samples_per_class_serve})"
      ],
      "metadata": {
        "id": "enGswXigQzV8"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.barplot(x='num_samples', y='label', data=df_num_serve,\n",
        "                 palette = \"Blues\",\n",
        "                 orient=\"h\")\n",
        "ax.set(xlabel='Number of samples with given sense', ylabel='Senses')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "WJ-xG2D-QuVF",
        "outputId": "6137c8ad-06f9-4123-b34a-393edea6dba1"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Class distribution for word *interest*:"
      ],
      "metadata": {
        "id": "pOqz2zD3Hsyd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "interest_instances_sense = [sense_instances(interest_instances, sense) for sense in interest_senses] # `interest.pos` instances group by senses"
      ],
      "metadata": {
        "id": "e0-6pGiYHvlL"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_samples_per_class_interest = [len(instances_group) for instances_group in interest_instances_sense]\n",
        "num_samples_per_class_interest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGEbLV-hNDmT",
        "outputId": "623005b0-bc93-4c65-b3c7-451d34184d51"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[361, 11, 66, 178, 500, 1252]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_num_interest = pd.DataFrame({'label': interest_senses, 'num_samples': num_samples_per_class_interest})"
      ],
      "metadata": {
        "id": "v7J4U-TuQ-wz"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.barplot(x='num_samples', y='label', data=df_num_interest,\n",
        "                 palette = \"Blues\",\n",
        "                 orient=\"h\")\n",
        "ax.set(xlabel='Number of samples with given sense', ylabel='Senses')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "fLBfjG3BRIca",
        "outputId": "7166b73c-781f-4065-abbb-c40b0a37dd75"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_vocab_frequency(instances, stopwords=STOPWORDS_SET, n=300):\n",
        "  \"\"\"\n",
        "    Given a list of senseval instances, return a list of the n most frequent words that appear in its context;\n",
        "    output is in descending order of frequency and includes also the number of instances in which that key\n",
        "    appears in the context of instances.\n",
        "  \"\"\"\n",
        "  fd = nltk.FreqDist()\n",
        "  for instance in instances:\n",
        "    (target, suffix) = instance.word.split('-')\n",
        "    words = (context[0] for context in instance.context if not context[0] == target)\n",
        "    for word in set(words) - stopwords:\n",
        "      fd[word] += 1\n",
        "\n",
        "  return fd.most_common()[:n+1]"
      ],
      "metadata": {
        "id": "IqPD48wZsDSc"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_vocabulary(instances, stopwords=STOPWORDS_SET, n=300):\n",
        "  return [w for (w,f) in extract_vocab_frequency(instances, stopwords, n)]"
      ],
      "metadata": {
        "id": "aaVr5mZ7vi3n"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def wsd_word_features(instance, vocabulary):\n",
        "  features = defaultdict(lambda: False)\n",
        "  try:\n",
        "    for (word, pos) in instance.context:\n",
        "      if word in vocabulary:\n",
        "        features[word] = True\n",
        "  except ValueError:\n",
        "    pass\n",
        "\n",
        "  return features"
      ],
      "metadata": {
        "id": "GBxKQbLPwKTp"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def wsd_context_features(instance, dist=3, include_pos=False):\n",
        "  features = {}\n",
        "  ind, con = instance.position, instance.context\n",
        "  for i in range(max(0, ind - dist), ind):\n",
        "    j = ind - i\n",
        "    if include_pos:\n",
        "      features['left-context-word-pos-%s' %j] = con[i]\n",
        "    else:\n",
        "      features['left-context-word-%s' %j] = con[i][0]\n",
        "\n",
        "  for i in range(ind + 1, min(ind + dist + 1, len(con))):\n",
        "    j = i -  ind\n",
        "    if include_pos:\n",
        "      features['right-context-word-pos-%s' %j] = con[i]\n",
        "    else:\n",
        "      features['right-context-word-%s' %j] = con[i][0]\n",
        "\n",
        "  return features"
      ],
      "metadata": {
        "id": "F_24wUiK6dsH"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "line_vocabulary = extract_vocabulary(line_instances)"
      ],
      "metadata": {
        "id": "1wAFEpVFx91K"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wsd_word_features(line_instances[0], line_vocabulary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZfRIpigSx5LO",
        "outputId": "312626de-a545-40a2-8e9e-a1bb7b729d1a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "defaultdict(<function __main__.wsd_word_features.<locals>.<lambda>>,\n",
              "            {'\"': True,\n",
              "             \"'s\": True,\n",
              "             \"'t\": True,\n",
              "             ',': True,\n",
              "             '.': True,\n",
              "             'company': True,\n",
              "             'earlier': True,\n",
              "             'however': True,\n",
              "             'lines': True,\n",
              "             'move': True})"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wsd_context_features(instance=line_instances[0], dist=3, include_pos=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NVKwaS1KAbbq",
        "outputId": "3a080e48-3a5d-4ef6-8a10-1f1484cd4008"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'left-context-word-1': 'safety',\n",
              " 'left-context-word-2': 'special',\n",
              " 'left-context-word-3': 'install',\n",
              " 'right-context-word-1': 'inside',\n",
              " 'right-context-word-2': 'the',\n",
              " 'right-context-word-3': 'a-frame'}"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def count_occurences(feature, instances):\n",
        "  \"\"\"\n",
        "    Count occurences of a feature vector in instances's context\n",
        "  \"\"\"\n",
        "\n",
        "  count = 0\n",
        "  for instance in instances:\n",
        "    target_pos = instance.position\n",
        "    words_instance = [c[0] for i, c in enumerate(instance.context) if i != target_pos]\n",
        "    count += words_instance.count(feature)\n",
        "  \n",
        "  return count"
      ],
      "metadata": {
        "id": "jzO1_tSyBAbb"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def count_occurences_context(feature, instances):\n",
        "  count = 0\n",
        "  for instance in instances:\n",
        "    target_pos = instance.position\n",
        "    sk_context = []\n",
        "    if type(feature) is tuple: # if the feature is composed from a word and its pos_tag\n",
        "      sk_context = [c for i, c in enumerate(instance.context) if i != target_pos]\n",
        "    else:\n",
        "      sk_context = [c[0] for i, c in enumerate(instance.context) if i != target_pos]\n",
        "    \n",
        "    count += sk_context.count(feature)\n",
        "\n",
        "  return count"
      ],
      "metadata": {
        "id": "LAmVEVh9YaXT"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.classify import accuracy, NaiveBayesClassifier"
      ],
      "metadata": {
        "id": "HAlhZoM4p9kp"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def wsd_classifier(train_instances, test_instances, senses, features_type='context', vocabulary_size=300, window_size=3, pos_tag=False,\n",
        "                   show_parameters_estimation=True, show_confusion_matrix=True, show_samples=True):\n",
        "  accuracy_test = None\n",
        "\n",
        "  # Parameters estimation\n",
        "  instances_sense = [sense_instances(train_instances, sense) for sense in senses] # instances group by senses\n",
        "  num_samples_per_class = [len(instances_group) for instances_group in instances_sense]\n",
        "  alphas = np.array([samples_per_class / len(train_instances) for samples_per_class in num_samples_per_class])\n",
        "\n",
        "  assert np.sum(alphas) == 1.\n",
        "\n",
        "  if show_parameters_estimation:\n",
        "    print('\\nClass prior probabilities:\\n', alphas)\n",
        "\n",
        "  if features_type == 'frequency':\n",
        "    # TRAIN\n",
        "    vocabulary = extract_vocabulary(train_instances, n=vocabulary_size)\n",
        "    features_train = [wsd_word_features(instance, vocabulary) for instance in train_instances]\n",
        "\n",
        "    thetas = np.zeros((len(senses), len(vocabulary)))\n",
        "    instances_sense_train = [sense_instances(train_instances, sense) for sense in senses]\n",
        "    for k, s_k in enumerate(instances_sense_train):\n",
        "      for j, v_j in enumerate(vocabulary):\n",
        "        thetas[k][j] = count_occurences(v_j, s_k)\n",
        "      thetas[k, :] = thetas[k, :] / np.sum(thetas[k, :])\n",
        "  \n",
        "    if show_parameters_estimation:\n",
        "      print('\\nLikelihood:\\n', thetas)\n",
        "\n",
        "    assert np.sum(thetas) == 1. * len(senses) \n",
        "\n",
        "    # CLASSIFY\n",
        "    features_test = [wsd_word_features(instance, vocabulary) for instance in test_instances]\n",
        "    labels_test =  np.array([instance.senses[0] for instance in test_instances])\n",
        "\n",
        "    log_probabilities = np.zeros((len(features_test), len(senses)))\n",
        "    for i, features in enumerate(features_test):\n",
        "      for j, sense in enumerate(senses):\n",
        "        log_probabilities[i,j] += np.log(alphas[j]) \n",
        "        for feature, _ in features.items():  \n",
        "          idx = vocabulary.index(feature)\n",
        "          log_probabilities[i,j] += np.log(thetas[j,idx])\n",
        "\n",
        "    predicted_labels_indexes = np.argmax(log_probabilities, axis=1)\n",
        "    predicted_labels = np.array([senses[i] for i in predicted_labels_indexes])\n",
        "    accuracy_test = np.sum(predicted_labels == labels_test) / len(labels_test)\n",
        "    print('\\nAccuracy: %6.4f' %accuracy_test)\n",
        "    if show_confusion_matrix:\n",
        "      cm = confusion_matrix(predicted_labels, labels_test)\n",
        "      print(cm)\n",
        "\n",
        "  elif features_type == 'context':\n",
        "    # TRAIN\n",
        "    features_train = [wsd_context_features(instance, window_size, include_pos=pos_tag) for instance in train_instances]\n",
        "    features_values = [list(feature_train.values()) for feature_train in features_train]\n",
        "    features_values_set = reduce(lambda x, y: x+y, features_values)\n",
        "    features_values_set = list(set(features_values_set))\n",
        "    instances_sense_train = [sense_instances(train_instances, sense) for sense in senses]\n",
        "    \n",
        "    thetas = np.zeros((len(senses), len(features_values_set)))\n",
        "    for k, s_k in enumerate(instances_sense_train):\n",
        "      for j, v_j in enumerate(features_values_set):\n",
        "        thetas[k, j] = count_occurences_context(v_j, s_k)\n",
        "      thetas[k, :] = thetas[k, :] / np.sum(thetas[k, :])\n",
        "\n",
        "    # CLASSIFY\n",
        "    features_test = [wsd_context_features(instance, window_size) for instance in test_instances]\n",
        "    features_test = [list(feature_test.values()) for feature_test in features_test]\n",
        "    labels_test =  np.array([instance.senses[0] for instance in test_instances])\n",
        "\n",
        "    log_probabilities = np.zeros((len(features_test), len(senses)))\n",
        "    for i, sample in enumerate(features_test):\n",
        "      for j, sense in enumerate(senses):\n",
        "        log_probabilities[i, j] += np.log(alphas[j])\n",
        "        for feature in sample:\n",
        "          if feature in features_values_set:\n",
        "            idx = features_values_set.index(feature)\n",
        "            log_probabilities[i, j] += np.log(thetas[j, idx])\n",
        "\n",
        "    predicted_labels_indexes = np.argmax(log_probabilities, axis=1)\n",
        "    predicted_labels = np.array([senses[i] for i in predicted_labels_indexes])\n",
        "    accuracy_test = np.sum(predicted_labels == labels_test) / len(labels_test)\n",
        "    print('\\nAccuracy: %6.4f' %accuracy_test)\n",
        "\n",
        "    if show_confusion_matrix:\n",
        "      cm = confusion_matrix(predicted_labels, labels_test)\n",
        "      print(cm)\n",
        "\n",
        "  if show_samples:\n",
        "    # Examples of errors\n",
        "    print()\n",
        "    wrong_predictions_indexes = np.where(predicted_labels != labels_test)\n",
        "    for i in wrong_predictions_indexes[0][:5]:\n",
        "      print(test_instances[i])\n",
        "  \n",
        "  return accuracy_test"
      ],
      "metadata": {
        "id": "j-prtUlpqmlu"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the instances into a training, validation and test set\n",
        "train_instances_line, test_instances_line = train_test_split(line_instances, test_size=0.2)\n",
        "train_instances_line, validation_instances_line = train_test_split(train_instances_line, test_size=0.1)\n",
        "print('Train set size:', len(train_instances_line))\n",
        "print('Validation set size:', len(validation_instances_line))\n",
        "print('Test set size:', len(test_instances_line))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQtXiZ58rFmE",
        "outputId": "9429a0ff-fcbf-4bc7-ff50-00d1bc1b09e4"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set size: 2984\n",
            "Validation set size: 332\n",
            "Test set size: 830\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wsd_classifier(train_instances_line, validation_instances_line, line_senses, 'frequency', 300, show_samples=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ViwSWjkG5nKn",
        "outputId": "0966a51f-d7c9-435c-b7a3-1b4ce58a2f89"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Class prior probabilities:\n",
            " [0.08780161 0.09316354 0.08210456 0.10522788 0.53786863 0.09383378]\n",
            "\n",
            "Likelihood:\n",
            " [[2.40983607e-01 2.51639344e-01 1.76229508e-02 ... 0.00000000e+00\n",
            "  0.00000000e+00 4.09836066e-04]\n",
            " [2.01296871e-01 1.90019735e-01 3.43952636e-02 ... 0.00000000e+00\n",
            "  2.81928390e-03 1.40964195e-03]\n",
            " [1.91159251e-01 1.95257611e-01 2.98594848e-02 ... 0.00000000e+00\n",
            "  1.75644028e-03 5.85480094e-04]\n",
            " [1.80799691e-01 1.66119374e-01 2.56905544e-02 ... 0.00000000e+00\n",
            "  1.93162063e-04 1.35213444e-03]\n",
            " [1.62566189e-01 1.47584915e-01 3.70980240e-02 ... 1.22691463e-03\n",
            "  5.16595635e-04 6.45744543e-04]\n",
            " [1.84747847e-01 2.00000000e-01 4.05904059e-02 ... 0.00000000e+00\n",
            "  1.23001230e-03 7.38007380e-04]]\n",
            "\n",
            "Accuracy: 0.6898\n",
            "[[ 18   2   1   5   0   2]\n",
            " [  2  10   1   0   5   2]\n",
            " [  0   4   9   1   4   6]\n",
            " [  1   1   7  16   2   4]\n",
            " [  3  11   7   6 157   9]\n",
            " [  2   6   3   3   3  19]]\n",
            "\n",
            "SensevalInstance(word='line-n', position=17, context=[('not', 'RB'), ('too', 'RB'), ('young', 'JJ'), (',', ','), ('i', 'PRP'), (\"'d\", 'MD'), ('say', 'VB'), ('.', '.'), ('a', 'DT'), ('new', 'JJ'), ('set', 'VBN'), ('of', 'IN'), ('nappies', 'NNS'), ('suddenly', 'RB'), ('appearing', 'VBG'), ('on', 'IN'), ('the', 'DT'), ('line', 'NN'), ('would', 'MD'), ('certainly', 'RB'), ('set', 'VBN'), ('the', 'DT'), ('neighbours', 'NNS'), ('chattering', 'VBG'), ('.', '.')], senses=('cord',))\n",
            "SensevalInstance(word='line-n', position=71, context=[('consultants', 'NNS'), ('have', 'VBP'), ('said', 'VBD'), ('that', 'IN'), ('it', 'PRP'), ('will', 'MD'), ('take', 'VB'), ('a', 'DT'), ('while', 'NN'), ('for', 'IN'), ('all', 'PDT'), ('the', 'DT'), ('pieces', 'NNS'), ('of', 'IN'), ('the', 'DT'), ('ibm', 'NNP'), ('strategy', 'NN'), ('to', 'TO'), ('fall', 'VB'), ('into', 'IN'), ('place', 'NN'), (',', ','), ('even', 'RB'), ('though', 'IN'), ('the', 'DT'), ('specific', 'JJ'), ('products', 'NNS'), ('ibm', 'NNP'), ('unveiled', 'VBD'), ('will', 'MD'), ('generally', 'RB'), ('be', 'VB'), ('available', 'JJ'), ('by', 'IN'), ('the', 'DT'), ('end', 'NN'), ('of', 'IN'), ('the', 'DT'), ('first', 'JJ'), ('quarter', 'NN'), ('.', '.'), ('sam', 'NNP'), ('albert', 'NNP'), (',', ','), ('a', 'DT'), ('consultant', 'NN'), ('in', 'IN'), ('scarsdale', 'NNP'), (',', ','), ('n', 'NN'), ('.', '.'), ('y', 'NNP'), ('.', '.'), (',', ','), ('said', 'VBD'), ('that', 'IN'), ('in', 'IN'), ('the', 'DT'), ('past', 'JJ'), ('ibm', 'NNP'), ('has', 'VBZ'), ('developed', 'VBN'), ('broad', 'JJ'), ('software', 'NN'), ('strategies', 'NNS'), ('only', 'RB'), ('for', 'IN'), ('problems', 'NNS'), ('that', 'WDT'), ('crossed', 'VBD'), ('industry', 'NN'), ('lines', 'NNS'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=39, context=[('in', 'IN'), ('france', 'NNP'), ('the', 'DT'), ('best', 'JJS'), ('athletes', 'NNS'), ('have', 'VBP'), ('jobs', 'NNS'), ('at', 'IN'), ('which', 'WDT'), ('they', 'PRP'), ('do', 'VBP'), ('little', 'JJ'), ('or', 'CC'), ('no', 'DT'), ('work', 'NN'), (',', ','), ('in', 'IN'), ('order', 'NN'), ('to', 'TO'), ('permit', 'VB'), ('them', 'PRP'), ('to', 'TO'), ('concentrate', 'VB'), ('on', 'IN'), ('training', 'NN'), ('.', '.'), ('even', 'RB'), ('australia', 'NNP'), (',', ','), ('one', 'CD'), ('of', 'IN'), ('the', 'DT'), ('few', 'JJ'), ('nations', 'NNS'), ('that', 'WDT'), ('still', 'RB'), ('hold', 'VB'), ('a', 'DT'), ('firm', 'NN'), ('line', 'NN'), ('between', 'IN'), ('amateurism', 'NN'), ('and', 'CC'), ('professionalism', 'NN'), (',', ','), ('sends', 'VBZ'), ('its', 'PRP$'), ('team', 'NN'), ('to', 'TO'), ('camp', 'VB'), ('for', 'IN'), ('an', 'DT'), ('extended', 'VBN'), ('pre-olympic', 'JJ'), ('period', 'NN'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=55, context=[('one', 'CD'), ('reason', 'NN'), ('optima', 'NNP'), ('could', 'MD'), ('reshape', 'VB'), ('the', 'DT'), ('credit-card', 'NN'), ('business', 'NN'), ('is', 'VBZ'), ('that', 'IN'), ('american', 'NNP'), ('express', 'NNP'), ('is', 'VBZ'), ('the', 'DT'), ('first', 'JJ'), ('high-profile', 'JJ'), ('card', 'NN'), ('issuer', 'NN'), ('with', 'IN'), ('national', 'JJ'), ('marketing', 'NN'), ('muscle', 'NN'), ('to', 'TO'), ('introduce', 'VB'), ('a', 'DT'), ('low-rate', 'JJ'), ('card', 'NN'), ('.', '.'), ('beyond', 'IN'), ('that', 'DT'), (',', ','), ('its', 'PRP$'), ('foray', 'NN'), ('into', 'IN'), ('revolving', 'VBG'), ('credit', 'NN'), ('--', ':'), ('in', 'IN'), ('which', 'WDT'), ('the', 'DT'), ('card', 'NN'), ('holder', 'NN'), ('must', 'MD'), ('make', 'VB'), ('only', 'RB'), ('a', 'DT'), ('minimal', 'JJ'), ('payment', 'NN'), ('each', 'DT'), ('month', 'NN'), ('--', ':'), ('further', 'JJ'), ('blurs', 'NNS'), ('the', 'DT'), ('once-well-defined', 'JJ'), ('line', 'NN'), ('between', 'IN'), ('banks', 'NNS'), ('and', 'CC'), ('other', 'JJ'), ('financial-service', 'NN'), ('companies', 'NNS'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=24, context=[('\"', '\"'), ('the', 'DT'), ('thing', 'NN'), ('that', 'WDT'), ('hit', 'VBD'), ('hardest', 'JJS'), ('was', 'VBD'), ('the', 'DT'), ('energy', 'NN'), ('crisis', 'NN'), (',', ','), ('\"', '\"'), ('mr', 'NNP'), ('.', '.'), ('atkins', 'NNP'), ('said', 'VBD'), ('.', '.'), ('if', 'IN'), ('someone', 'NN'), ('\"', '\"'), ('had', 'VBD'), ('to', 'TO'), ('wait', 'VB'), ('in', 'IN'), ('line', 'NN'), ('for', 'IN'), ('gas', 'NN'), ('for', 'IN'), ('his', 'PRP$'), ('car', 'NN'), (',', ','), ('he', 'PRP'), ('couldn', 'VBP'), (\"'t\", 'VBG'), ('justify', 'VB'), ('\"', '\"'), ('waiting', 'VBG'), ('for', 'IN'), ('gas', 'NN'), ('for', 'IN'), ('a', 'DT'), ('snowmobile', 'NN'), ('.', '.')], senses=('formation',))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:42: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6897590361445783"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocabulary_sizes = [i for i in range(100, 600, 100)]"
      ],
      "metadata": {
        "id": "MVi8ejuFL5KM"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# search for best parameters\n",
        "best_acc, accuracies = 0, []\n",
        "best_vocab_size = None\n",
        "for vocabulary_size in vocabulary_sizes:\n",
        "  acc = wsd_classifier(train_instances_line, validation_instances_line, line_senses, 'frequency', vocabulary_size, \n",
        "                       show_parameters_estimation=False, show_confusion_matrix=False)\n",
        "  accuracies.append(acc)\n",
        "  if acc > best_acc:\n",
        "    best_acc = acc\n",
        "    best_vocab_size = vocabulary_size                 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VLTafgcMJ72",
        "outputId": "fa1fc48d-95c5-430f-9796-f6a0c0e7bc09"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:42: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.6114\n",
            "\n",
            "SensevalInstance(word='line-n', position=17, context=[('not', 'RB'), ('too', 'RB'), ('young', 'JJ'), (',', ','), ('i', 'PRP'), (\"'d\", 'MD'), ('say', 'VB'), ('.', '.'), ('a', 'DT'), ('new', 'JJ'), ('set', 'VBN'), ('of', 'IN'), ('nappies', 'NNS'), ('suddenly', 'RB'), ('appearing', 'VBG'), ('on', 'IN'), ('the', 'DT'), ('line', 'NN'), ('would', 'MD'), ('certainly', 'RB'), ('set', 'VBN'), ('the', 'DT'), ('neighbours', 'NNS'), ('chattering', 'VBG'), ('.', '.')], senses=('cord',))\n",
            "SensevalInstance(word='line-n', position=71, context=[('consultants', 'NNS'), ('have', 'VBP'), ('said', 'VBD'), ('that', 'IN'), ('it', 'PRP'), ('will', 'MD'), ('take', 'VB'), ('a', 'DT'), ('while', 'NN'), ('for', 'IN'), ('all', 'PDT'), ('the', 'DT'), ('pieces', 'NNS'), ('of', 'IN'), ('the', 'DT'), ('ibm', 'NNP'), ('strategy', 'NN'), ('to', 'TO'), ('fall', 'VB'), ('into', 'IN'), ('place', 'NN'), (',', ','), ('even', 'RB'), ('though', 'IN'), ('the', 'DT'), ('specific', 'JJ'), ('products', 'NNS'), ('ibm', 'NNP'), ('unveiled', 'VBD'), ('will', 'MD'), ('generally', 'RB'), ('be', 'VB'), ('available', 'JJ'), ('by', 'IN'), ('the', 'DT'), ('end', 'NN'), ('of', 'IN'), ('the', 'DT'), ('first', 'JJ'), ('quarter', 'NN'), ('.', '.'), ('sam', 'NNP'), ('albert', 'NNP'), (',', ','), ('a', 'DT'), ('consultant', 'NN'), ('in', 'IN'), ('scarsdale', 'NNP'), (',', ','), ('n', 'NN'), ('.', '.'), ('y', 'NNP'), ('.', '.'), (',', ','), ('said', 'VBD'), ('that', 'IN'), ('in', 'IN'), ('the', 'DT'), ('past', 'JJ'), ('ibm', 'NNP'), ('has', 'VBZ'), ('developed', 'VBN'), ('broad', 'JJ'), ('software', 'NN'), ('strategies', 'NNS'), ('only', 'RB'), ('for', 'IN'), ('problems', 'NNS'), ('that', 'WDT'), ('crossed', 'VBD'), ('industry', 'NN'), ('lines', 'NNS'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=39, context=[('in', 'IN'), ('france', 'NNP'), ('the', 'DT'), ('best', 'JJS'), ('athletes', 'NNS'), ('have', 'VBP'), ('jobs', 'NNS'), ('at', 'IN'), ('which', 'WDT'), ('they', 'PRP'), ('do', 'VBP'), ('little', 'JJ'), ('or', 'CC'), ('no', 'DT'), ('work', 'NN'), (',', ','), ('in', 'IN'), ('order', 'NN'), ('to', 'TO'), ('permit', 'VB'), ('them', 'PRP'), ('to', 'TO'), ('concentrate', 'VB'), ('on', 'IN'), ('training', 'NN'), ('.', '.'), ('even', 'RB'), ('australia', 'NNP'), (',', ','), ('one', 'CD'), ('of', 'IN'), ('the', 'DT'), ('few', 'JJ'), ('nations', 'NNS'), ('that', 'WDT'), ('still', 'RB'), ('hold', 'VB'), ('a', 'DT'), ('firm', 'NN'), ('line', 'NN'), ('between', 'IN'), ('amateurism', 'NN'), ('and', 'CC'), ('professionalism', 'NN'), (',', ','), ('sends', 'VBZ'), ('its', 'PRP$'), ('team', 'NN'), ('to', 'TO'), ('camp', 'VB'), ('for', 'IN'), ('an', 'DT'), ('extended', 'VBN'), ('pre-olympic', 'JJ'), ('period', 'NN'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=55, context=[('one', 'CD'), ('reason', 'NN'), ('optima', 'NNP'), ('could', 'MD'), ('reshape', 'VB'), ('the', 'DT'), ('credit-card', 'NN'), ('business', 'NN'), ('is', 'VBZ'), ('that', 'IN'), ('american', 'NNP'), ('express', 'NNP'), ('is', 'VBZ'), ('the', 'DT'), ('first', 'JJ'), ('high-profile', 'JJ'), ('card', 'NN'), ('issuer', 'NN'), ('with', 'IN'), ('national', 'JJ'), ('marketing', 'NN'), ('muscle', 'NN'), ('to', 'TO'), ('introduce', 'VB'), ('a', 'DT'), ('low-rate', 'JJ'), ('card', 'NN'), ('.', '.'), ('beyond', 'IN'), ('that', 'DT'), (',', ','), ('its', 'PRP$'), ('foray', 'NN'), ('into', 'IN'), ('revolving', 'VBG'), ('credit', 'NN'), ('--', ':'), ('in', 'IN'), ('which', 'WDT'), ('the', 'DT'), ('card', 'NN'), ('holder', 'NN'), ('must', 'MD'), ('make', 'VB'), ('only', 'RB'), ('a', 'DT'), ('minimal', 'JJ'), ('payment', 'NN'), ('each', 'DT'), ('month', 'NN'), ('--', ':'), ('further', 'JJ'), ('blurs', 'NNS'), ('the', 'DT'), ('once-well-defined', 'JJ'), ('line', 'NN'), ('between', 'IN'), ('banks', 'NNS'), ('and', 'CC'), ('other', 'JJ'), ('financial-service', 'NN'), ('companies', 'NNS'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=24, context=[('\"', '\"'), ('the', 'DT'), ('thing', 'NN'), ('that', 'WDT'), ('hit', 'VBD'), ('hardest', 'JJS'), ('was', 'VBD'), ('the', 'DT'), ('energy', 'NN'), ('crisis', 'NN'), (',', ','), ('\"', '\"'), ('mr', 'NNP'), ('.', '.'), ('atkins', 'NNP'), ('said', 'VBD'), ('.', '.'), ('if', 'IN'), ('someone', 'NN'), ('\"', '\"'), ('had', 'VBD'), ('to', 'TO'), ('wait', 'VB'), ('in', 'IN'), ('line', 'NN'), ('for', 'IN'), ('gas', 'NN'), ('for', 'IN'), ('his', 'PRP$'), ('car', 'NN'), (',', ','), ('he', 'PRP'), ('couldn', 'VBP'), (\"'t\", 'VBG'), ('justify', 'VB'), ('\"', '\"'), ('waiting', 'VBG'), ('for', 'IN'), ('gas', 'NN'), ('for', 'IN'), ('a', 'DT'), ('snowmobile', 'NN'), ('.', '.')], senses=('formation',))\n",
            "\n",
            "Accuracy: 0.6627\n",
            "\n",
            "SensevalInstance(word='line-n', position=17, context=[('not', 'RB'), ('too', 'RB'), ('young', 'JJ'), (',', ','), ('i', 'PRP'), (\"'d\", 'MD'), ('say', 'VB'), ('.', '.'), ('a', 'DT'), ('new', 'JJ'), ('set', 'VBN'), ('of', 'IN'), ('nappies', 'NNS'), ('suddenly', 'RB'), ('appearing', 'VBG'), ('on', 'IN'), ('the', 'DT'), ('line', 'NN'), ('would', 'MD'), ('certainly', 'RB'), ('set', 'VBN'), ('the', 'DT'), ('neighbours', 'NNS'), ('chattering', 'VBG'), ('.', '.')], senses=('cord',))\n",
            "SensevalInstance(word='line-n', position=71, context=[('consultants', 'NNS'), ('have', 'VBP'), ('said', 'VBD'), ('that', 'IN'), ('it', 'PRP'), ('will', 'MD'), ('take', 'VB'), ('a', 'DT'), ('while', 'NN'), ('for', 'IN'), ('all', 'PDT'), ('the', 'DT'), ('pieces', 'NNS'), ('of', 'IN'), ('the', 'DT'), ('ibm', 'NNP'), ('strategy', 'NN'), ('to', 'TO'), ('fall', 'VB'), ('into', 'IN'), ('place', 'NN'), (',', ','), ('even', 'RB'), ('though', 'IN'), ('the', 'DT'), ('specific', 'JJ'), ('products', 'NNS'), ('ibm', 'NNP'), ('unveiled', 'VBD'), ('will', 'MD'), ('generally', 'RB'), ('be', 'VB'), ('available', 'JJ'), ('by', 'IN'), ('the', 'DT'), ('end', 'NN'), ('of', 'IN'), ('the', 'DT'), ('first', 'JJ'), ('quarter', 'NN'), ('.', '.'), ('sam', 'NNP'), ('albert', 'NNP'), (',', ','), ('a', 'DT'), ('consultant', 'NN'), ('in', 'IN'), ('scarsdale', 'NNP'), (',', ','), ('n', 'NN'), ('.', '.'), ('y', 'NNP'), ('.', '.'), (',', ','), ('said', 'VBD'), ('that', 'IN'), ('in', 'IN'), ('the', 'DT'), ('past', 'JJ'), ('ibm', 'NNP'), ('has', 'VBZ'), ('developed', 'VBN'), ('broad', 'JJ'), ('software', 'NN'), ('strategies', 'NNS'), ('only', 'RB'), ('for', 'IN'), ('problems', 'NNS'), ('that', 'WDT'), ('crossed', 'VBD'), ('industry', 'NN'), ('lines', 'NNS'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=34, context=[('on', 'IN'), ('an', 'DT'), ('office', 'NN'), ('wall', 'NN'), ('of', 'IN'), ('the', 'DT'), ('senate', 'NNP'), ('intelligence', 'NN'), ('committee', 'NN'), ('hangs', 'VBZ'), ('a', 'DT'), ('quote', 'NN'), ('from', 'IN'), ('chairman', 'NNP'), ('david', 'NNP'), ('boren', 'NNP'), (',', ','), ('\"', '\"'), ('don', 'NNP'), (\"'t\", 'NN'), ('hold', 'VB'), ('your', 'PRP$'), ('ticket', 'NN'), (\"'til\", 'VBG'), ('the', 'DT'), ('show', 'NN'), (\"'s\", 'POS'), ('over', 'IN'), ('.', '.'), ('\"', '\"'), ('he', 'PRP'), ('once', 'RB'), ('used', 'VBD'), ('that', 'DT'), ('line', 'NN'), ('in', 'IN'), ('a', 'DT'), ('closed-door', 'JJ'), ('meeting', 'NN'), ('on', 'IN'), ('panama', 'NNP'), (',', ','), ('meaning', 'NN'), ('don', 'NN'), (\"'t\", 'NN'), ('shrink', 'NN'), ('from', 'IN'), ('taking', 'VBG'), ('action', 'NN'), ('against', 'IN'), ('manuel', 'NNP'), ('noriega', 'NNP'), ('.', '.')], senses=('text',))\n",
            "SensevalInstance(word='line-n', position=39, context=[('in', 'IN'), ('france', 'NNP'), ('the', 'DT'), ('best', 'JJS'), ('athletes', 'NNS'), ('have', 'VBP'), ('jobs', 'NNS'), ('at', 'IN'), ('which', 'WDT'), ('they', 'PRP'), ('do', 'VBP'), ('little', 'JJ'), ('or', 'CC'), ('no', 'DT'), ('work', 'NN'), (',', ','), ('in', 'IN'), ('order', 'NN'), ('to', 'TO'), ('permit', 'VB'), ('them', 'PRP'), ('to', 'TO'), ('concentrate', 'VB'), ('on', 'IN'), ('training', 'NN'), ('.', '.'), ('even', 'RB'), ('australia', 'NNP'), (',', ','), ('one', 'CD'), ('of', 'IN'), ('the', 'DT'), ('few', 'JJ'), ('nations', 'NNS'), ('that', 'WDT'), ('still', 'RB'), ('hold', 'VB'), ('a', 'DT'), ('firm', 'NN'), ('line', 'NN'), ('between', 'IN'), ('amateurism', 'NN'), ('and', 'CC'), ('professionalism', 'NN'), (',', ','), ('sends', 'VBZ'), ('its', 'PRP$'), ('team', 'NN'), ('to', 'TO'), ('camp', 'VB'), ('for', 'IN'), ('an', 'DT'), ('extended', 'VBN'), ('pre-olympic', 'JJ'), ('period', 'NN'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=55, context=[('one', 'CD'), ('reason', 'NN'), ('optima', 'NNP'), ('could', 'MD'), ('reshape', 'VB'), ('the', 'DT'), ('credit-card', 'NN'), ('business', 'NN'), ('is', 'VBZ'), ('that', 'IN'), ('american', 'NNP'), ('express', 'NNP'), ('is', 'VBZ'), ('the', 'DT'), ('first', 'JJ'), ('high-profile', 'JJ'), ('card', 'NN'), ('issuer', 'NN'), ('with', 'IN'), ('national', 'JJ'), ('marketing', 'NN'), ('muscle', 'NN'), ('to', 'TO'), ('introduce', 'VB'), ('a', 'DT'), ('low-rate', 'JJ'), ('card', 'NN'), ('.', '.'), ('beyond', 'IN'), ('that', 'DT'), (',', ','), ('its', 'PRP$'), ('foray', 'NN'), ('into', 'IN'), ('revolving', 'VBG'), ('credit', 'NN'), ('--', ':'), ('in', 'IN'), ('which', 'WDT'), ('the', 'DT'), ('card', 'NN'), ('holder', 'NN'), ('must', 'MD'), ('make', 'VB'), ('only', 'RB'), ('a', 'DT'), ('minimal', 'JJ'), ('payment', 'NN'), ('each', 'DT'), ('month', 'NN'), ('--', ':'), ('further', 'JJ'), ('blurs', 'NNS'), ('the', 'DT'), ('once-well-defined', 'JJ'), ('line', 'NN'), ('between', 'IN'), ('banks', 'NNS'), ('and', 'CC'), ('other', 'JJ'), ('financial-service', 'NN'), ('companies', 'NNS'), ('.', '.')], senses=('division',))\n",
            "\n",
            "Accuracy: 0.6898\n",
            "\n",
            "SensevalInstance(word='line-n', position=17, context=[('not', 'RB'), ('too', 'RB'), ('young', 'JJ'), (',', ','), ('i', 'PRP'), (\"'d\", 'MD'), ('say', 'VB'), ('.', '.'), ('a', 'DT'), ('new', 'JJ'), ('set', 'VBN'), ('of', 'IN'), ('nappies', 'NNS'), ('suddenly', 'RB'), ('appearing', 'VBG'), ('on', 'IN'), ('the', 'DT'), ('line', 'NN'), ('would', 'MD'), ('certainly', 'RB'), ('set', 'VBN'), ('the', 'DT'), ('neighbours', 'NNS'), ('chattering', 'VBG'), ('.', '.')], senses=('cord',))\n",
            "SensevalInstance(word='line-n', position=71, context=[('consultants', 'NNS'), ('have', 'VBP'), ('said', 'VBD'), ('that', 'IN'), ('it', 'PRP'), ('will', 'MD'), ('take', 'VB'), ('a', 'DT'), ('while', 'NN'), ('for', 'IN'), ('all', 'PDT'), ('the', 'DT'), ('pieces', 'NNS'), ('of', 'IN'), ('the', 'DT'), ('ibm', 'NNP'), ('strategy', 'NN'), ('to', 'TO'), ('fall', 'VB'), ('into', 'IN'), ('place', 'NN'), (',', ','), ('even', 'RB'), ('though', 'IN'), ('the', 'DT'), ('specific', 'JJ'), ('products', 'NNS'), ('ibm', 'NNP'), ('unveiled', 'VBD'), ('will', 'MD'), ('generally', 'RB'), ('be', 'VB'), ('available', 'JJ'), ('by', 'IN'), ('the', 'DT'), ('end', 'NN'), ('of', 'IN'), ('the', 'DT'), ('first', 'JJ'), ('quarter', 'NN'), ('.', '.'), ('sam', 'NNP'), ('albert', 'NNP'), (',', ','), ('a', 'DT'), ('consultant', 'NN'), ('in', 'IN'), ('scarsdale', 'NNP'), (',', ','), ('n', 'NN'), ('.', '.'), ('y', 'NNP'), ('.', '.'), (',', ','), ('said', 'VBD'), ('that', 'IN'), ('in', 'IN'), ('the', 'DT'), ('past', 'JJ'), ('ibm', 'NNP'), ('has', 'VBZ'), ('developed', 'VBN'), ('broad', 'JJ'), ('software', 'NN'), ('strategies', 'NNS'), ('only', 'RB'), ('for', 'IN'), ('problems', 'NNS'), ('that', 'WDT'), ('crossed', 'VBD'), ('industry', 'NN'), ('lines', 'NNS'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=39, context=[('in', 'IN'), ('france', 'NNP'), ('the', 'DT'), ('best', 'JJS'), ('athletes', 'NNS'), ('have', 'VBP'), ('jobs', 'NNS'), ('at', 'IN'), ('which', 'WDT'), ('they', 'PRP'), ('do', 'VBP'), ('little', 'JJ'), ('or', 'CC'), ('no', 'DT'), ('work', 'NN'), (',', ','), ('in', 'IN'), ('order', 'NN'), ('to', 'TO'), ('permit', 'VB'), ('them', 'PRP'), ('to', 'TO'), ('concentrate', 'VB'), ('on', 'IN'), ('training', 'NN'), ('.', '.'), ('even', 'RB'), ('australia', 'NNP'), (',', ','), ('one', 'CD'), ('of', 'IN'), ('the', 'DT'), ('few', 'JJ'), ('nations', 'NNS'), ('that', 'WDT'), ('still', 'RB'), ('hold', 'VB'), ('a', 'DT'), ('firm', 'NN'), ('line', 'NN'), ('between', 'IN'), ('amateurism', 'NN'), ('and', 'CC'), ('professionalism', 'NN'), (',', ','), ('sends', 'VBZ'), ('its', 'PRP$'), ('team', 'NN'), ('to', 'TO'), ('camp', 'VB'), ('for', 'IN'), ('an', 'DT'), ('extended', 'VBN'), ('pre-olympic', 'JJ'), ('period', 'NN'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=55, context=[('one', 'CD'), ('reason', 'NN'), ('optima', 'NNP'), ('could', 'MD'), ('reshape', 'VB'), ('the', 'DT'), ('credit-card', 'NN'), ('business', 'NN'), ('is', 'VBZ'), ('that', 'IN'), ('american', 'NNP'), ('express', 'NNP'), ('is', 'VBZ'), ('the', 'DT'), ('first', 'JJ'), ('high-profile', 'JJ'), ('card', 'NN'), ('issuer', 'NN'), ('with', 'IN'), ('national', 'JJ'), ('marketing', 'NN'), ('muscle', 'NN'), ('to', 'TO'), ('introduce', 'VB'), ('a', 'DT'), ('low-rate', 'JJ'), ('card', 'NN'), ('.', '.'), ('beyond', 'IN'), ('that', 'DT'), (',', ','), ('its', 'PRP$'), ('foray', 'NN'), ('into', 'IN'), ('revolving', 'VBG'), ('credit', 'NN'), ('--', ':'), ('in', 'IN'), ('which', 'WDT'), ('the', 'DT'), ('card', 'NN'), ('holder', 'NN'), ('must', 'MD'), ('make', 'VB'), ('only', 'RB'), ('a', 'DT'), ('minimal', 'JJ'), ('payment', 'NN'), ('each', 'DT'), ('month', 'NN'), ('--', ':'), ('further', 'JJ'), ('blurs', 'NNS'), ('the', 'DT'), ('once-well-defined', 'JJ'), ('line', 'NN'), ('between', 'IN'), ('banks', 'NNS'), ('and', 'CC'), ('other', 'JJ'), ('financial-service', 'NN'), ('companies', 'NNS'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=24, context=[('\"', '\"'), ('the', 'DT'), ('thing', 'NN'), ('that', 'WDT'), ('hit', 'VBD'), ('hardest', 'JJS'), ('was', 'VBD'), ('the', 'DT'), ('energy', 'NN'), ('crisis', 'NN'), (',', ','), ('\"', '\"'), ('mr', 'NNP'), ('.', '.'), ('atkins', 'NNP'), ('said', 'VBD'), ('.', '.'), ('if', 'IN'), ('someone', 'NN'), ('\"', '\"'), ('had', 'VBD'), ('to', 'TO'), ('wait', 'VB'), ('in', 'IN'), ('line', 'NN'), ('for', 'IN'), ('gas', 'NN'), ('for', 'IN'), ('his', 'PRP$'), ('car', 'NN'), (',', ','), ('he', 'PRP'), ('couldn', 'VBP'), (\"'t\", 'VBG'), ('justify', 'VB'), ('\"', '\"'), ('waiting', 'VBG'), ('for', 'IN'), ('gas', 'NN'), ('for', 'IN'), ('a', 'DT'), ('snowmobile', 'NN'), ('.', '.')], senses=('formation',))\n",
            "\n",
            "Accuracy: 0.6867\n",
            "\n",
            "SensevalInstance(word='line-n', position=17, context=[('not', 'RB'), ('too', 'RB'), ('young', 'JJ'), (',', ','), ('i', 'PRP'), (\"'d\", 'MD'), ('say', 'VB'), ('.', '.'), ('a', 'DT'), ('new', 'JJ'), ('set', 'VBN'), ('of', 'IN'), ('nappies', 'NNS'), ('suddenly', 'RB'), ('appearing', 'VBG'), ('on', 'IN'), ('the', 'DT'), ('line', 'NN'), ('would', 'MD'), ('certainly', 'RB'), ('set', 'VBN'), ('the', 'DT'), ('neighbours', 'NNS'), ('chattering', 'VBG'), ('.', '.')], senses=('cord',))\n",
            "SensevalInstance(word='line-n', position=35, context=[('companies', 'NNS'), ('such', 'JJ'), ('as', 'IN'), ('gypsum', 'NN'), ('marketer', 'NN'), ('usg', 'NNP'), (',', ','), ('hotel', 'NN'), ('operator', 'NN'), ('holiday', 'NNP'), ('and', 'CC'), ('publisher', 'NN'), ('harcourt', 'NNP'), ('brace', 'NNP'), ('jovanovich', 'NNP'), ('have', 'VBP'), ('fended', 'VBN'), ('off', 'IN'), ('raiders', 'NNS'), ('by', 'IN'), ('borrowing', 'NN'), ('to', 'TO'), ('pay', 'VB'), ('big', 'JJ'), ('dividends', 'NNS'), ('.', '.'), ('either', 'CC'), ('way', 'NN'), (',', ','), ('the', 'DT'), ('stubs', 'NNS'), ('stand', 'VBP'), ('behind', 'IN'), ('a', 'DT'), ('long', 'JJ'), ('line', 'NN'), ('of', 'IN'), ('bondholders', 'NNS'), ('if', 'IN'), ('a', 'DT'), ('company', 'NN'), ('is', 'VBZ'), ('dissolved', 'VBN'), (';', ':'), ('only', 'RB'), ('after', 'IN'), ('debt', 'NN'), ('is', 'VBZ'), ('satisfied', 'VBN'), ('can', 'MD'), ('stubholders', 'NNS'), ('stake', 'VB'), ('a', 'DT'), ('claim', 'NN'), ('to', 'TO'), ('assets', 'NNS'), ('or', 'CC'), ('earnings', 'NNS'), ('.', '.')], senses=('formation',))\n",
            "SensevalInstance(word='line-n', position=71, context=[('consultants', 'NNS'), ('have', 'VBP'), ('said', 'VBD'), ('that', 'IN'), ('it', 'PRP'), ('will', 'MD'), ('take', 'VB'), ('a', 'DT'), ('while', 'NN'), ('for', 'IN'), ('all', 'PDT'), ('the', 'DT'), ('pieces', 'NNS'), ('of', 'IN'), ('the', 'DT'), ('ibm', 'NNP'), ('strategy', 'NN'), ('to', 'TO'), ('fall', 'VB'), ('into', 'IN'), ('place', 'NN'), (',', ','), ('even', 'RB'), ('though', 'IN'), ('the', 'DT'), ('specific', 'JJ'), ('products', 'NNS'), ('ibm', 'NNP'), ('unveiled', 'VBD'), ('will', 'MD'), ('generally', 'RB'), ('be', 'VB'), ('available', 'JJ'), ('by', 'IN'), ('the', 'DT'), ('end', 'NN'), ('of', 'IN'), ('the', 'DT'), ('first', 'JJ'), ('quarter', 'NN'), ('.', '.'), ('sam', 'NNP'), ('albert', 'NNP'), (',', ','), ('a', 'DT'), ('consultant', 'NN'), ('in', 'IN'), ('scarsdale', 'NNP'), (',', ','), ('n', 'NN'), ('.', '.'), ('y', 'NNP'), ('.', '.'), (',', ','), ('said', 'VBD'), ('that', 'IN'), ('in', 'IN'), ('the', 'DT'), ('past', 'JJ'), ('ibm', 'NNP'), ('has', 'VBZ'), ('developed', 'VBN'), ('broad', 'JJ'), ('software', 'NN'), ('strategies', 'NNS'), ('only', 'RB'), ('for', 'IN'), ('problems', 'NNS'), ('that', 'WDT'), ('crossed', 'VBD'), ('industry', 'NN'), ('lines', 'NNS'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=39, context=[('in', 'IN'), ('france', 'NNP'), ('the', 'DT'), ('best', 'JJS'), ('athletes', 'NNS'), ('have', 'VBP'), ('jobs', 'NNS'), ('at', 'IN'), ('which', 'WDT'), ('they', 'PRP'), ('do', 'VBP'), ('little', 'JJ'), ('or', 'CC'), ('no', 'DT'), ('work', 'NN'), (',', ','), ('in', 'IN'), ('order', 'NN'), ('to', 'TO'), ('permit', 'VB'), ('them', 'PRP'), ('to', 'TO'), ('concentrate', 'VB'), ('on', 'IN'), ('training', 'NN'), ('.', '.'), ('even', 'RB'), ('australia', 'NNP'), (',', ','), ('one', 'CD'), ('of', 'IN'), ('the', 'DT'), ('few', 'JJ'), ('nations', 'NNS'), ('that', 'WDT'), ('still', 'RB'), ('hold', 'VB'), ('a', 'DT'), ('firm', 'NN'), ('line', 'NN'), ('between', 'IN'), ('amateurism', 'NN'), ('and', 'CC'), ('professionalism', 'NN'), (',', ','), ('sends', 'VBZ'), ('its', 'PRP$'), ('team', 'NN'), ('to', 'TO'), ('camp', 'VB'), ('for', 'IN'), ('an', 'DT'), ('extended', 'VBN'), ('pre-olympic', 'JJ'), ('period', 'NN'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=55, context=[('one', 'CD'), ('reason', 'NN'), ('optima', 'NNP'), ('could', 'MD'), ('reshape', 'VB'), ('the', 'DT'), ('credit-card', 'NN'), ('business', 'NN'), ('is', 'VBZ'), ('that', 'IN'), ('american', 'NNP'), ('express', 'NNP'), ('is', 'VBZ'), ('the', 'DT'), ('first', 'JJ'), ('high-profile', 'JJ'), ('card', 'NN'), ('issuer', 'NN'), ('with', 'IN'), ('national', 'JJ'), ('marketing', 'NN'), ('muscle', 'NN'), ('to', 'TO'), ('introduce', 'VB'), ('a', 'DT'), ('low-rate', 'JJ'), ('card', 'NN'), ('.', '.'), ('beyond', 'IN'), ('that', 'DT'), (',', ','), ('its', 'PRP$'), ('foray', 'NN'), ('into', 'IN'), ('revolving', 'VBG'), ('credit', 'NN'), ('--', ':'), ('in', 'IN'), ('which', 'WDT'), ('the', 'DT'), ('card', 'NN'), ('holder', 'NN'), ('must', 'MD'), ('make', 'VB'), ('only', 'RB'), ('a', 'DT'), ('minimal', 'JJ'), ('payment', 'NN'), ('each', 'DT'), ('month', 'NN'), ('--', ':'), ('further', 'JJ'), ('blurs', 'NNS'), ('the', 'DT'), ('once-well-defined', 'JJ'), ('line', 'NN'), ('between', 'IN'), ('banks', 'NNS'), ('and', 'CC'), ('other', 'JJ'), ('financial-service', 'NN'), ('companies', 'NNS'), ('.', '.')], senses=('division',))\n",
            "\n",
            "Accuracy: 0.6928\n",
            "\n",
            "SensevalInstance(word='line-n', position=17, context=[('not', 'RB'), ('too', 'RB'), ('young', 'JJ'), (',', ','), ('i', 'PRP'), (\"'d\", 'MD'), ('say', 'VB'), ('.', '.'), ('a', 'DT'), ('new', 'JJ'), ('set', 'VBN'), ('of', 'IN'), ('nappies', 'NNS'), ('suddenly', 'RB'), ('appearing', 'VBG'), ('on', 'IN'), ('the', 'DT'), ('line', 'NN'), ('would', 'MD'), ('certainly', 'RB'), ('set', 'VBN'), ('the', 'DT'), ('neighbours', 'NNS'), ('chattering', 'VBG'), ('.', '.')], senses=('cord',))\n",
            "SensevalInstance(word='line-n', position=71, context=[('consultants', 'NNS'), ('have', 'VBP'), ('said', 'VBD'), ('that', 'IN'), ('it', 'PRP'), ('will', 'MD'), ('take', 'VB'), ('a', 'DT'), ('while', 'NN'), ('for', 'IN'), ('all', 'PDT'), ('the', 'DT'), ('pieces', 'NNS'), ('of', 'IN'), ('the', 'DT'), ('ibm', 'NNP'), ('strategy', 'NN'), ('to', 'TO'), ('fall', 'VB'), ('into', 'IN'), ('place', 'NN'), (',', ','), ('even', 'RB'), ('though', 'IN'), ('the', 'DT'), ('specific', 'JJ'), ('products', 'NNS'), ('ibm', 'NNP'), ('unveiled', 'VBD'), ('will', 'MD'), ('generally', 'RB'), ('be', 'VB'), ('available', 'JJ'), ('by', 'IN'), ('the', 'DT'), ('end', 'NN'), ('of', 'IN'), ('the', 'DT'), ('first', 'JJ'), ('quarter', 'NN'), ('.', '.'), ('sam', 'NNP'), ('albert', 'NNP'), (',', ','), ('a', 'DT'), ('consultant', 'NN'), ('in', 'IN'), ('scarsdale', 'NNP'), (',', ','), ('n', 'NN'), ('.', '.'), ('y', 'NNP'), ('.', '.'), (',', ','), ('said', 'VBD'), ('that', 'IN'), ('in', 'IN'), ('the', 'DT'), ('past', 'JJ'), ('ibm', 'NNP'), ('has', 'VBZ'), ('developed', 'VBN'), ('broad', 'JJ'), ('software', 'NN'), ('strategies', 'NNS'), ('only', 'RB'), ('for', 'IN'), ('problems', 'NNS'), ('that', 'WDT'), ('crossed', 'VBD'), ('industry', 'NN'), ('lines', 'NNS'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=39, context=[('in', 'IN'), ('france', 'NNP'), ('the', 'DT'), ('best', 'JJS'), ('athletes', 'NNS'), ('have', 'VBP'), ('jobs', 'NNS'), ('at', 'IN'), ('which', 'WDT'), ('they', 'PRP'), ('do', 'VBP'), ('little', 'JJ'), ('or', 'CC'), ('no', 'DT'), ('work', 'NN'), (',', ','), ('in', 'IN'), ('order', 'NN'), ('to', 'TO'), ('permit', 'VB'), ('them', 'PRP'), ('to', 'TO'), ('concentrate', 'VB'), ('on', 'IN'), ('training', 'NN'), ('.', '.'), ('even', 'RB'), ('australia', 'NNP'), (',', ','), ('one', 'CD'), ('of', 'IN'), ('the', 'DT'), ('few', 'JJ'), ('nations', 'NNS'), ('that', 'WDT'), ('still', 'RB'), ('hold', 'VB'), ('a', 'DT'), ('firm', 'NN'), ('line', 'NN'), ('between', 'IN'), ('amateurism', 'NN'), ('and', 'CC'), ('professionalism', 'NN'), (',', ','), ('sends', 'VBZ'), ('its', 'PRP$'), ('team', 'NN'), ('to', 'TO'), ('camp', 'VB'), ('for', 'IN'), ('an', 'DT'), ('extended', 'VBN'), ('pre-olympic', 'JJ'), ('period', 'NN'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=55, context=[('one', 'CD'), ('reason', 'NN'), ('optima', 'NNP'), ('could', 'MD'), ('reshape', 'VB'), ('the', 'DT'), ('credit-card', 'NN'), ('business', 'NN'), ('is', 'VBZ'), ('that', 'IN'), ('american', 'NNP'), ('express', 'NNP'), ('is', 'VBZ'), ('the', 'DT'), ('first', 'JJ'), ('high-profile', 'JJ'), ('card', 'NN'), ('issuer', 'NN'), ('with', 'IN'), ('national', 'JJ'), ('marketing', 'NN'), ('muscle', 'NN'), ('to', 'TO'), ('introduce', 'VB'), ('a', 'DT'), ('low-rate', 'JJ'), ('card', 'NN'), ('.', '.'), ('beyond', 'IN'), ('that', 'DT'), (',', ','), ('its', 'PRP$'), ('foray', 'NN'), ('into', 'IN'), ('revolving', 'VBG'), ('credit', 'NN'), ('--', ':'), ('in', 'IN'), ('which', 'WDT'), ('the', 'DT'), ('card', 'NN'), ('holder', 'NN'), ('must', 'MD'), ('make', 'VB'), ('only', 'RB'), ('a', 'DT'), ('minimal', 'JJ'), ('payment', 'NN'), ('each', 'DT'), ('month', 'NN'), ('--', ':'), ('further', 'JJ'), ('blurs', 'NNS'), ('the', 'DT'), ('once-well-defined', 'JJ'), ('line', 'NN'), ('between', 'IN'), ('banks', 'NNS'), ('and', 'CC'), ('other', 'JJ'), ('financial-service', 'NN'), ('companies', 'NNS'), ('.', '.')], senses=('division',))\n",
            "SensevalInstance(word='line-n', position=8, context=[('\"', '\"'), ('you', 'PRP'), ('hear', 'VBP'), ('me', 'PRP'), ('?', '.'), ('get', 'VB'), ('off', 'IN'), ('this', 'DT'), ('line', 'NN'), ('!', '.')], senses=('phone',))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(vocabulary_sizes, accuracies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "bngoqOGgMpas",
        "outputId": "a68a924d-6061-4c7c-f139-07095ccc981a"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25386d3090>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test classifier with best value found for vocabulary size\n",
        "wsd_classifier(train_instances_line, test_instances_line, line_senses, 'frequency', best_vocab_size, \n",
        "               show_parameters_estimation=False, show_confusion_matrix=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6SMr4GAOFZm",
        "outputId": "3309608b-f2ec-425f-cbb1-30977d17c4a8"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.7361\n",
            "[[ 45   5   4   8   2   4]\n",
            " [  4  29   4   1   5   5]\n",
            " [  5   8  26   5   6  11]\n",
            " [  4   5   9  60  10   8]\n",
            " [  5  23  15  17 426  15]\n",
            " [  5   5   8   6   7  25]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.736144578313253"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Features: surrounding context of a target word\n",
        "wsd_classifier(train_instances_line, validation_instances_line, line_senses, 'context', window_size=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Vrh8nzzVr5t",
        "outputId": "71631454-8195-4981-a4b3-06460da8341f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Class prior probabilities:\n",
            " [0.09282842 0.08579088 0.0844504  0.10321716 0.53552279 0.09819035]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.7861\n",
            "[[ 24   5   2   3   2   2]\n",
            " [  0  24   2   0   2   2]\n",
            " [  0   1  18   2   1   0]\n",
            " [  0   0   1  21   4   2]\n",
            " [  5   2   2   7 163  12]\n",
            " [  6   1   1   2   2  11]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.786144578313253"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "window_sizes = [i for i in range(1, 15, 2)]"
      ],
      "metadata": {
        "id": "BgrThjLJmZaV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_acc, accuracies = 0, []\n",
        "best_window_size = None\n",
        "for window_size in window_sizes:\n",
        "  acc = wsd_classifier(train_instances_line, test_instances_line, line_senses, 'context', window_size=window_size, \n",
        "                      show_parameters_estimation=False, show_confusion_matrix=False)\n",
        "  accuracies.append(acc)\n",
        "  if acc > best_acc:\n",
        "    best_acc = acc\n",
        "    best_window_size = window_size                 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Ibt7ugImmx_",
        "outputId": "64e7ad4e-d057-4024-9e51-7ac90542ef06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.6904\n",
            "\n",
            "Accuracy: 0.8000\n",
            "\n",
            "Accuracy: 0.7783\n",
            "\n",
            "Accuracy: 0.7253\n",
            "\n",
            "Accuracy: 0.6892\n",
            "\n",
            "Accuracy: 0.6554\n",
            "\n",
            "Accuracy: 0.6133\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(window_sizes, accuracies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "uKnYrd_hnA18",
        "outputId": "5e7009e0-877e-45c9-f447-da2143069648"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4219323090>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test classifier with best value found for window size\n",
        "wsd_classifier(train_instances_line, test_instances_line, line_senses, 'context', window_size=best_window_size, \n",
        "               show_parameters_estimation=False, show_confusion_matrix=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F0TJsDG9nFTs",
        "outputId": "b4d9ea7e-4a43-40b7-b971-7f4ba7520fe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.8000\n",
            "[[ 46   5  11  14   6  10]\n",
            " [  1  70   0   2   6   2]\n",
            " [  0   2  33   1   1   2]\n",
            " [  3   0   7  46   0   3]\n",
            " [  9   7  18  22 425  21]\n",
            " [  2   1   2   1   7  44]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Features: surrounding context and corresponding pos_tags of a target word\n",
        "wsd_classifier(train_instances_line, validation_instances_line, line_senses, 'context', window_size=3, pos_tag=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7RQ2i0ixvVI",
        "outputId": "c7406d59-0d5f-4950-9430-0ea032683199"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Class prior probabilities:\n",
            " [0.09282842 0.08579088 0.0844504  0.10321716 0.53552279 0.09819035]\n",
            "\n",
            "Accuracy: 0.5241\n",
            "[[  0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0]\n",
            " [ 35  33  26  35 174  29]\n",
            " [  0   0   0   0   0   0]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5240963855421686"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "window_size = [i for i in range(1, 4, 1)]\n",
        "best_acc, accuracies = 0, []\n",
        "best_window_size = None\n",
        "for window_size in window_sizes:\n",
        "  acc = wsd_classifier(train_instances_line, validation_instances_line, line_senses, 'context', window_size=window_size, pos_tag=True,\n",
        "                      show_parameters_estimation=False, show_confusion_matrix=False)\n",
        "  accuracies.append(acc)\n",
        "  if acc > best_acc:\n",
        "    best_acc = acc\n",
        "    best_window_size = window_size                 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IamS_UYc3IsA",
        "outputId": "7b7cb64a-779b-4a86-d32d-4af6ddf8b81e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.5000\n",
            "\n",
            "Accuracy: 0.5000\n",
            "\n",
            "Accuracy: 0.5000\n",
            "\n",
            "Accuracy: 0.5000\n",
            "\n",
            "Accuracy: 0.5000\n",
            "\n",
            "Accuracy: 0.5000\n",
            "\n",
            "Accuracy: 0.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test classifier with best value found for window size\n",
        "wsd_classifier(train_instances_line, test_instances_line, line_senses, 'context', window_size=best_window_size, pos_tag=True,\n",
        "               show_parameters_estimation=False, show_confusion_matrix=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m8dpl2NZ36xs",
        "outputId": "1166c03f-f52a-4900-b47b-30e8a7934942"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.5289\n",
            "[[  0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0]\n",
            " [ 84  69  70  81 439  87]\n",
            " [  0   0   0   0   0   0]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5289156626506024"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the instances into a training, validation and test set\n",
        "train_instances_hard, test_instances_hard = train_test_split(hard_instances, test_size=0.2)\n",
        "train_instances_hard, validation_instances_hard = train_test_split(train_instances_hard, test_size=0.1)\n",
        "print('Train set size:', len(train_instances_hard))\n",
        "print('Validation set size:', len(validation_instances_hard))\n",
        "print('Test set size:', len(test_instances_hard))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHksbFi9Aciw",
        "outputId": "3daa94ec-52ba-49ab-ebf6-038a9fd4a063"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set size: 3119\n",
            "Validation set size: 347\n",
            "Test set size: 867\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wsd_classifier(train_instances_hard, validation_instances_hard, hard_senses, 'frequency', 300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QegrQvIUK9Ve",
        "outputId": "a0e3c8b4-4cf2-40ad-c73d-b69fd3fceffe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Class prior probabilities:\n",
            " [0.79544726 0.119269   0.08528374]\n",
            "\n",
            "Likelihood:\n",
            " [[1.40984971e-01 1.54246191e-01 6.58900619e-02 4.59202247e-02\n",
            "  2.24140621e-02 2.32461386e-02 2.12699568e-02 1.56014353e-04\n",
            "  1.09210047e-02 1.18570909e-02 1.19090956e-02 9.46487077e-03\n",
            "  9.20484685e-03 9.20484685e-03 7.64470331e-03 2.91226793e-03\n",
            "  7.48868896e-03 6.13656456e-03 6.70861719e-03 7.64470331e-03\n",
            "  6.55260284e-03 7.28066982e-03 6.76062198e-03 5.72052629e-03\n",
            "  5.98055021e-03 5.77253107e-03 5.09646888e-03 4.47241146e-03\n",
            "  5.25248323e-03 4.57642103e-03 1.40412918e-03 2.75625358e-03\n",
            "  3.90035883e-03 0.00000000e+00 4.88844974e-03 4.21238754e-03\n",
            "  4.26439232e-03 3.95236362e-03 3.12028707e-03 3.22429664e-03\n",
            "  3.58833013e-03 3.79634926e-03 3.53632534e-03 3.84835405e-03\n",
            "  3.32830620e-03 3.17229185e-03 3.32830620e-03 3.58833013e-03\n",
            "  2.54823444e-03 2.75625358e-03 2.70424879e-03 2.80825836e-03\n",
            "  2.02818659e-03 2.80825836e-03 2.65224401e-03 2.86026314e-03\n",
            "  2.80825836e-03 3.01627750e-03 2.49622965e-03 2.86026314e-03\n",
            "  2.80825836e-03 2.34021530e-03 2.65224401e-03 2.39222008e-03\n",
            "  2.65224401e-03 2.70424879e-03 2.34021530e-03 2.08019138e-03\n",
            "  2.28821052e-03 2.60023922e-03 1.92417702e-03 2.44422487e-03\n",
            "  2.49622965e-03 2.34021530e-03 2.18420095e-03 2.34021530e-03\n",
            "  2.39222008e-03 2.08019138e-03 1.87217224e-03 1.56014353e-03\n",
            "  1.71615789e-03 1.61214832e-03 1.71615789e-03 1.82016746e-03\n",
            "  1.92417702e-03 1.87217224e-03 1.92417702e-03 2.08019138e-03\n",
            "  1.56014353e-03 1.61214832e-03 1.71615789e-03 1.66415310e-03\n",
            "  1.82016746e-03 1.97618181e-03 1.56014353e-03 1.66415310e-03\n",
            "  1.71615789e-03 1.66415310e-03 1.50813875e-03 1.82016746e-03\n",
            "  1.92417702e-03 1.71615789e-03 1.56014353e-03 1.50813875e-03\n",
            "  1.50813875e-03 1.45613396e-03 1.45613396e-03 1.56014353e-03\n",
            "  1.56014353e-03 1.50813875e-03 1.56014353e-03 1.56014353e-03\n",
            "  1.50813875e-03 1.66415310e-03 1.45613396e-03 1.50813875e-03\n",
            "  1.19611004e-03 1.24811483e-03 1.04009569e-03 1.45613396e-03\n",
            "  1.40412918e-03 1.19611004e-03 1.35212440e-03 1.45613396e-03\n",
            "  1.30011961e-03 1.24811483e-03 8.32076551e-04 1.30011961e-03\n",
            "  1.14410526e-03 8.84081335e-04 9.88090904e-04 1.24811483e-03\n",
            "  1.04009569e-04 1.24811483e-03 1.04009569e-03 1.19611004e-03\n",
            "  1.40412918e-03 1.09210047e-03 1.56014353e-03 9.88090904e-04\n",
            "  1.24811483e-03 1.09210047e-03 1.30011961e-03 3.64033491e-04\n",
            "  1.14410526e-03 1.24811483e-03 1.19611004e-03 1.09210047e-03\n",
            "  1.14410526e-03 6.24057413e-04 1.14410526e-03 8.32076551e-04\n",
            "  1.09210047e-03 1.35212440e-03 9.36086120e-04 9.88090904e-04\n",
            "  1.04009569e-03 1.09210047e-03 1.35212440e-03 1.14410526e-03\n",
            "  8.32076551e-04 1.09210047e-03 9.88090904e-04 9.36086120e-04\n",
            "  8.84081335e-04 1.14410526e-03 8.32076551e-04 1.14410526e-03\n",
            "  8.32076551e-04 9.88090904e-04 1.04009569e-03 1.04009569e-03\n",
            "  1.04009569e-03 8.84081335e-04 9.88090904e-04 9.36086120e-04\n",
            "  1.19611004e-03 8.84081335e-04 9.36086120e-04 1.14410526e-03\n",
            "  1.09210047e-03 9.36086120e-04 1.09210047e-03 1.30011961e-03\n",
            "  1.09210047e-03 6.24057413e-04 8.84081335e-04 9.36086120e-04\n",
            "  8.32076551e-04 9.36086120e-04 1.04009569e-03 4.68043060e-04\n",
            "  1.04009569e-04 9.36086120e-04 9.36086120e-04 9.36086120e-04\n",
            "  1.09210047e-03 8.84081335e-04 1.09210047e-03 8.84081335e-04\n",
            "  1.09210047e-03 9.36086120e-04 9.36086120e-04 8.84081335e-04\n",
            "  7.28066982e-04 7.80071767e-04 1.09210047e-03 1.09210047e-03\n",
            "  9.88090904e-04 1.19611004e-03 1.09210047e-03 9.88090904e-04\n",
            "  1.09210047e-03 8.32076551e-04 9.88090904e-04 8.32076551e-04\n",
            "  9.36086120e-04 3.12028707e-04 5.72052629e-04 7.28066982e-04\n",
            "  9.88090904e-04 6.24057413e-04 8.84081335e-04 8.84081335e-04\n",
            "  9.36086120e-04 9.36086120e-04 6.24057413e-04 7.28066982e-04\n",
            "  6.76062198e-04 7.80071767e-04 9.88090904e-04 7.80071767e-04\n",
            "  9.36086120e-04 7.80071767e-04 7.80071767e-04 9.36086120e-04\n",
            "  8.84081335e-04 8.84081335e-04 9.88090904e-04 3.64033491e-04\n",
            "  8.32076551e-04 6.76062198e-04 9.36086120e-04 7.28066982e-04\n",
            "  6.24057413e-04 8.32076551e-04 5.20047844e-04 6.76062198e-04\n",
            "  8.32076551e-04 9.88090904e-04 7.80071767e-04 6.76062198e-04\n",
            "  7.80071767e-04 8.84081335e-04 9.88090904e-04 9.88090904e-04\n",
            "  6.76062198e-04 9.36086120e-04 1.09210047e-03 8.32076551e-04\n",
            "  9.36086120e-04 9.36086120e-04 7.28066982e-04 5.72052629e-04\n",
            "  7.80071767e-04 8.84081335e-04 8.32076551e-04 6.24057413e-04\n",
            "  6.76062198e-04 7.80071767e-04 7.28066982e-04 7.80071767e-04\n",
            "  7.80071767e-04 8.84081335e-04 7.28066982e-04 7.80071767e-04\n",
            "  6.24057413e-04 6.76062198e-04 7.28066982e-04 6.76062198e-04\n",
            "  8.84081335e-04 8.84081335e-04 6.76062198e-04 8.32076551e-04\n",
            "  7.80071767e-04 6.76062198e-04 7.28066982e-04 8.84081335e-04\n",
            "  5.20047844e-04 8.84081335e-04 6.76062198e-04 7.28066982e-04\n",
            "  7.80071767e-04 6.24057413e-04 7.28066982e-04 7.80071767e-04\n",
            "  6.24057413e-04 7.80071767e-04 7.28066982e-04 6.24057413e-04\n",
            "  6.76062198e-04]\n",
            " [1.38647019e-01 1.91225720e-01 2.37776289e-02 4.11922304e-02\n",
            "  2.57870060e-02 1.74146015e-02 1.40656397e-02 0.00000000e+00\n",
            "  9.04219692e-03 5.69323510e-03 1.03817816e-02 4.35365037e-03\n",
            "  9.37709310e-03 4.68854655e-03 6.69792364e-03 3.81781648e-02\n",
            "  6.36302746e-03 6.02813128e-03 8.03750837e-03 1.67448091e-03\n",
            "  8.03750837e-03 2.00937709e-03 6.36302746e-03 3.68385800e-03\n",
            "  3.01406564e-03 1.67448091e-03 4.35365037e-03 4.01875419e-03\n",
            "  2.34427328e-03 3.01406564e-03 2.24380442e-02 1.37307435e-02\n",
            "  6.02813128e-03 0.00000000e+00 2.34427328e-03 2.34427328e-03\n",
            "  2.67916946e-03 2.34427328e-03 5.69323510e-03 6.02813128e-03\n",
            "  2.67916946e-03 1.33958473e-03 2.00937709e-03 0.00000000e+00\n",
            "  3.68385800e-03 2.34427328e-03 2.34427328e-03 3.34896182e-04\n",
            "  2.00937709e-03 2.67916946e-03 1.67448091e-03 1.67448091e-03\n",
            "  6.36302746e-03 6.69792364e-04 2.34427328e-03 1.00468855e-03\n",
            "  3.01406564e-03 1.00468855e-03 2.34427328e-03 1.33958473e-03\n",
            "  6.69792364e-04 2.67916946e-03 1.33958473e-03 2.67916946e-03\n",
            "  2.67916946e-03 1.33958473e-03 2.67916946e-03 4.35365037e-03\n",
            "  3.68385800e-03 3.34896182e-04 3.34896182e-03 2.34427328e-03\n",
            "  1.00468855e-03 1.00468855e-03 2.34427328e-03 1.33958473e-03\n",
            "  1.00468855e-03 2.34427328e-03 2.34427328e-03 3.68385800e-03\n",
            "  1.67448091e-03 0.00000000e+00 1.00468855e-03 1.33958473e-03\n",
            "  6.69792364e-04 2.00937709e-03 1.00468855e-03 1.33958473e-03\n",
            "  3.01406564e-03 3.01406564e-03 2.67916946e-03 2.00937709e-03\n",
            "  2.00937709e-03 0.00000000e+00 3.01406564e-03 1.67448091e-03\n",
            "  2.00937709e-03 1.67448091e-03 3.01406564e-03 1.00468855e-03\n",
            "  0.00000000e+00 2.00937709e-03 3.68385800e-03 2.00937709e-03\n",
            "  2.00937709e-03 1.00468855e-03 6.69792364e-04 3.34896182e-04\n",
            "  2.00937709e-03 1.00468855e-03 1.00468855e-03 2.00937709e-03\n",
            "  0.00000000e+00 1.00468855e-03 6.69792364e-04 1.00468855e-03\n",
            "  6.69792364e-04 1.00468855e-03 1.33958473e-03 3.34896182e-04\n",
            "  1.00468855e-03 1.67448091e-03 1.33958473e-03 0.00000000e+00\n",
            "  1.67448091e-03 1.00468855e-03 1.00468855e-03 1.33958473e-03\n",
            "  2.00937709e-03 1.00468855e-03 3.34896182e-04 1.67448091e-03\n",
            "  8.70730074e-03 1.00468855e-03 1.67448091e-03 1.00468855e-03\n",
            "  3.34896182e-04 0.00000000e+00 6.69792364e-04 1.33958473e-03\n",
            "  1.00468855e-03 2.00937709e-03 6.69792364e-04 3.34896182e-04\n",
            "  1.00468855e-03 0.00000000e+00 3.34896182e-04 1.67448091e-03\n",
            "  1.00468855e-03 4.68854655e-03 3.34896182e-04 3.01406564e-03\n",
            "  1.33958473e-03 0.00000000e+00 3.01406564e-03 6.69792364e-04\n",
            "  1.67448091e-03 3.34896182e-04 0.00000000e+00 6.69792364e-04\n",
            "  2.67916946e-03 1.00468855e-03 1.67448091e-03 2.00937709e-03\n",
            "  2.34427328e-03 1.00468855e-03 2.00937709e-03 6.69792364e-04\n",
            "  2.34427328e-03 1.67448091e-03 1.33958473e-03 3.34896182e-04\n",
            "  1.33958473e-03 3.34896182e-04 2.00937709e-03 2.34427328e-03\n",
            "  3.34896182e-04 2.00937709e-03 6.69792364e-04 0.00000000e+00\n",
            "  6.69792364e-04 1.33958473e-03 1.00468855e-03 3.34896182e-04\n",
            "  1.33958473e-03 3.68385800e-03 1.00468855e-03 2.34427328e-03\n",
            "  1.67448091e-03 1.33958473e-03 3.34896182e-04 0.00000000e+00\n",
            "  1.00468855e-03 1.33958473e-03 1.67448091e-03 1.00468855e-03\n",
            "  1.33958473e-03 1.00468855e-03 0.00000000e+00 1.67448091e-03\n",
            "  3.34896182e-04 6.69792364e-04 3.34896182e-04 1.00468855e-03\n",
            "  2.00937709e-03 6.69792364e-04 3.34896182e-04 0.00000000e+00\n",
            "  6.69792364e-04 0.00000000e+00 3.34896182e-04 1.00468855e-03\n",
            "  3.34896182e-04 6.69792364e-04 6.69792364e-04 6.69792364e-04\n",
            "  1.67448091e-03 1.00468855e-03 2.00937709e-03 2.00937709e-03\n",
            "  6.69792364e-04 2.00937709e-03 1.00468855e-03 6.69792364e-04\n",
            "  3.34896182e-04 3.34896182e-04 6.69792364e-04 2.00937709e-03\n",
            "  2.00937709e-03 6.69792364e-04 6.69792364e-04 1.33958473e-03\n",
            "  0.00000000e+00 1.00468855e-03 1.33958473e-03 6.69792364e-04\n",
            "  1.33958473e-03 1.33958473e-03 3.34896182e-04 4.01875419e-03\n",
            "  1.33958473e-03 1.00468855e-03 6.69792364e-04 1.00468855e-03\n",
            "  1.67448091e-03 6.69792364e-04 3.34896182e-04 2.00937709e-03\n",
            "  6.69792364e-04 6.69792364e-04 6.69792364e-04 1.67448091e-03\n",
            "  1.33958473e-03 1.33958473e-03 0.00000000e+00 0.00000000e+00\n",
            "  3.34896182e-04 3.34896182e-04 3.34896182e-04 3.34896182e-04\n",
            "  3.34896182e-04 3.34896182e-04 1.33958473e-03 1.00468855e-03\n",
            "  6.69792364e-04 6.69792364e-04 3.34896182e-04 3.34896182e-04\n",
            "  1.00468855e-03 6.69792364e-04 6.69792364e-04 1.00468855e-03\n",
            "  6.69792364e-04 3.34896182e-04 1.67448091e-03 1.00468855e-03\n",
            "  1.67448091e-03 1.67448091e-03 6.69792364e-04 2.00937709e-03\n",
            "  0.00000000e+00 0.00000000e+00 6.69792364e-04 3.34896182e-04\n",
            "  1.00468855e-03 6.69792364e-04 6.69792364e-04 0.00000000e+00\n",
            "  6.69792364e-04 0.00000000e+00 6.69792364e-04 1.00468855e-03\n",
            "  1.33958473e-03 1.33958473e-03 6.69792364e-04 6.69792364e-04\n",
            "  2.34427328e-03 6.69792364e-04 1.00468855e-03 1.00468855e-03\n",
            "  3.34896182e-04]\n",
            " [1.64132145e-01 2.48033561e-01 1.67802832e-02 2.83167278e-02\n",
            "  2.20241217e-02 9.43890928e-03 5.24383849e-03 0.00000000e+00\n",
            "  7.34137389e-03 3.14630309e-03 1.25852124e-02 3.14630309e-03\n",
            "  1.52071316e-02 5.24383849e-03 5.76822234e-03 2.09753540e-03\n",
            "  4.19507079e-03 1.20608285e-02 1.62558993e-02 1.57315155e-03\n",
            "  1.62558993e-02 4.19507079e-03 1.57315155e-03 3.14630309e-03\n",
            "  5.24383849e-04 5.24383849e-04 5.24383849e-03 3.67068694e-03\n",
            "  1.04876770e-03 3.67068694e-03 3.14630309e-03 1.57315155e-03\n",
            "  2.09753540e-03 0.00000000e+00 3.67068694e-03 1.04876770e-03\n",
            "  2.09753540e-03 4.19507079e-03 4.71945464e-03 4.71945464e-03\n",
            "  2.09753540e-03 1.04876770e-03 2.62191924e-03 5.24383849e-04\n",
            "  2.09753540e-03 2.09753540e-03 5.24383849e-04 1.04876770e-03\n",
            "  6.29260619e-03 3.14630309e-03 2.62191924e-03 1.57315155e-03\n",
            "  2.09753540e-03 2.62191924e-03 3.14630309e-03 2.09753540e-03\n",
            "  5.24383849e-04 0.00000000e+00 3.67068694e-03 1.57315155e-03\n",
            "  1.57315155e-03 2.09753540e-03 1.57315155e-03 8.39014158e-03\n",
            "  1.04876770e-03 0.00000000e+00 5.24383849e-04 2.09753540e-03\n",
            "  2.62191924e-03 1.04876770e-03 3.67068694e-03 2.09753540e-03\n",
            "  1.04876770e-03 1.57315155e-03 1.57315155e-03 5.24383849e-04\n",
            "  1.57315155e-03 5.24383849e-04 1.57315155e-03 1.57315155e-03\n",
            "  3.14630309e-03 7.34137389e-03 4.71945464e-03 2.09753540e-03\n",
            "  1.57315155e-03 1.04876770e-03 1.57315155e-03 5.24383849e-04\n",
            "  1.04876770e-03 1.04876770e-03 5.24383849e-04 2.62191924e-03\n",
            "  5.24383849e-04 5.24383849e-04 1.04876770e-03 1.57315155e-03\n",
            "  1.57315155e-03 1.25852124e-02 5.24383849e-04 1.04876770e-03\n",
            "  0.00000000e+00 0.00000000e+00 5.24383849e-04 1.57315155e-03\n",
            "  1.57315155e-03 1.57315155e-03 2.62191924e-03 1.57315155e-03\n",
            "  0.00000000e+00 5.24383849e-04 0.00000000e+00 1.04876770e-03\n",
            "  2.09753540e-03 1.04876770e-03 1.04876770e-03 5.24383849e-04\n",
            "  4.71945464e-03 1.57315155e-03 3.14630309e-03 5.24383849e-04\n",
            "  0.00000000e+00 1.04876770e-03 1.57315155e-03 1.04876770e-03\n",
            "  5.24383849e-04 1.57315155e-03 1.10120608e-02 2.09753540e-03\n",
            "  1.04876770e-03 4.71945464e-03 6.81699004e-03 0.00000000e+00\n",
            "  0.00000000e+00 5.24383849e-04 1.57315155e-03 1.04876770e-03\n",
            "  0.00000000e+00 3.67068694e-03 1.04876770e-03 3.67068694e-03\n",
            "  5.24383849e-04 5.24383849e-04 1.04876770e-03 1.10120608e-02\n",
            "  1.04876770e-03 2.09753540e-03 1.57315155e-03 1.04876770e-03\n",
            "  5.24383849e-04 0.00000000e+00 2.09753540e-03 5.24383849e-04\n",
            "  1.04876770e-03 0.00000000e+00 0.00000000e+00 2.09753540e-03\n",
            "  0.00000000e+00 1.57315155e-03 1.04876770e-03 5.24383849e-04\n",
            "  5.24383849e-04 5.24383849e-04 5.24383849e-04 5.24383849e-04\n",
            "  5.24383849e-04 0.00000000e+00 1.57315155e-03 5.24383849e-04\n",
            "  5.24383849e-04 0.00000000e+00 0.00000000e+00 2.09753540e-03\n",
            "  5.24383849e-04 3.14630309e-03 0.00000000e+00 0.00000000e+00\n",
            "  0.00000000e+00 5.24383849e-04 1.57315155e-03 5.24383849e-04\n",
            "  0.00000000e+00 5.24383849e-04 0.00000000e+00 0.00000000e+00\n",
            "  5.24383849e-04 5.24383849e-04 3.14630309e-03 0.00000000e+00\n",
            "  5.24383849e-04 5.24383849e-04 1.04876770e-03 7.34137389e-03\n",
            "  9.43890928e-03 0.00000000e+00 5.24383849e-04 5.24383849e-04\n",
            "  0.00000000e+00 1.57315155e-03 1.04876770e-03 0.00000000e+00\n",
            "  1.57315155e-03 1.57315155e-03 1.57315155e-03 5.24383849e-04\n",
            "  5.24383849e-04 4.71945464e-03 0.00000000e+00 0.00000000e+00\n",
            "  5.24383849e-04 0.00000000e+00 5.24383849e-04 0.00000000e+00\n",
            "  0.00000000e+00 2.09753540e-03 5.24383849e-04 1.57315155e-03\n",
            "  0.00000000e+00 6.29260619e-03 2.62191924e-03 1.04876770e-03\n",
            "  0.00000000e+00 1.04876770e-03 5.24383849e-04 5.24383849e-04\n",
            "  5.24383849e-04 5.24383849e-04 3.14630309e-03 5.24383849e-04\n",
            "  1.04876770e-03 1.57315155e-03 0.00000000e+00 5.24383849e-04\n",
            "  1.04876770e-03 1.57315155e-03 5.24383849e-04 5.24383849e-04\n",
            "  0.00000000e+00 5.24383849e-04 5.24383849e-04 5.24383849e-04\n",
            "  0.00000000e+00 2.62191924e-03 0.00000000e+00 1.04876770e-03\n",
            "  1.04876770e-03 1.04876770e-03 4.19507079e-03 5.24383849e-04\n",
            "  5.24383849e-04 0.00000000e+00 1.04876770e-03 5.24383849e-04\n",
            "  0.00000000e+00 5.24383849e-04 0.00000000e+00 0.00000000e+00\n",
            "  2.62191924e-03 0.00000000e+00 1.04876770e-03 1.04876770e-03\n",
            "  0.00000000e+00 5.24383849e-04 0.00000000e+00 2.09753540e-03\n",
            "  1.04876770e-03 5.24383849e-04 5.24383849e-04 2.62191924e-03\n",
            "  1.04876770e-03 5.24383849e-04 1.57315155e-03 0.00000000e+00\n",
            "  1.04876770e-03 5.24383849e-04 0.00000000e+00 0.00000000e+00\n",
            "  5.24383849e-04 5.24383849e-04 1.04876770e-03 5.24383849e-04\n",
            "  5.24383849e-04 5.24383849e-04 1.57315155e-03 0.00000000e+00\n",
            "  0.00000000e+00 1.57315155e-03 1.04876770e-03 5.24383849e-04\n",
            "  2.62191924e-03 5.24383849e-04 1.04876770e-03 5.24383849e-04\n",
            "  0.00000000e+00 1.04876770e-03 5.24383849e-04 5.24383849e-04\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 5.24383849e-04\n",
            "  1.04876770e-03]]\n",
            "\n",
            "Accuracy: 0.8357\n",
            "[[264  25  22]\n",
            " [  5  19   0]\n",
            " [  3   2   7]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8357348703170029"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_acc, accuracies = 0, []\n",
        "best_vocab_size = None\n",
        "for vocabulary_size in vocabulary_sizes:\n",
        "  acc = wsd_classifier(train_instances_hard, validation_instances_hard, hard_senses, 'frequency', vocabulary_size, \n",
        "                       show_parameters_estimation=False, show_confusion_matrix=False)\n",
        "  accuracies.append(acc)\n",
        "  if acc > best_acc:\n",
        "    best_acc = acc\n",
        "    best_vocab_size = vocabulary_size                 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZB-PUuRPMDW",
        "outputId": "0a02eba8-0454-45bb-8a87-c6f0ef557aed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.8156\n",
            "\n",
            "Accuracy: 0.8156\n",
            "\n",
            "Accuracy: 0.8357\n",
            "\n",
            "Accuracy: 0.8444\n",
            "\n",
            "Accuracy: 0.8329\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(vocabulary_sizes, accuracies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "7uOC1qcWPVbg",
        "outputId": "e1712cf3-1dce-4a80-d9dd-d41ef1563b7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fe12fdc10>"
            ]
          },
          "metadata": {},
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test classifier with best value found for vocabulary size\n",
        "wsd_classifier(train_instances_hard, test_instances_hard, hard_senses, 'frequency', best_vocab_size, show_parameters_estimation=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5VBFy11qoin",
        "outputId": "075ff87c-de66-4422-9f16-a43f0cbe1b6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.8662\n",
            "[[688  42  54]\n",
            " [  8  40   4]\n",
            " [  6   2  23]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8662053056516724"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Features: surrounding context of a target word\n",
        "wsd_classifier(train_instances_hard, validation_instances_hard, hard_senses, 'context', window_size=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPE-KpNz91EO",
        "outputId": "86403996-b311-4c9e-e5e6-94b9eb4b21c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Class prior probabilities:\n",
            " [0.79544726 0.119269   0.08528374]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.8905\n",
            "[[265  17  11]\n",
            " [  5  27   1]\n",
            " [  2   2  17]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8904899135446686"
            ]
          },
          "metadata": {},
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "window_size = [i for i in range(1, 4, 1)]\n",
        "best_acc, accuracies = 0, []\n",
        "best_window_size = None\n",
        "for window_size in window_sizes:\n",
        "  acc = wsd_classifier(train_instances_hard, validation_instances_hard, hard_senses, 'context', window_size=window_size, pos_tag=True,\n",
        "                       show_parameters_estimation=False, show_confusion_matrix=False)\n",
        "  accuracies.append(acc)\n",
        "  if acc > best_acc:\n",
        "    best_acc = acc\n",
        "    best_window_size = window_size                 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NSTqtqpM-JsM",
        "outputId": "ccefc193-0891-49a7-c684-27abad3066a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.7839\n",
            "\n",
            "Accuracy: 0.7839\n",
            "\n",
            "Accuracy: 0.7839\n",
            "\n",
            "Accuracy: 0.7839\n",
            "\n",
            "Accuracy: 0.7839\n",
            "\n",
            "Accuracy: 0.7839\n",
            "\n",
            "Accuracy: 0.7839\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test classifier with best value found for window size\n",
        "wsd_classifier(train_instances_hard, test_instances_hard, hard_senses, 'context', window_size=best_window_size, pos_tag=True,\n",
        "               show_parameters_estimation=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCbgvmM5ElLH",
        "outputId": "2b985608-a68c-469b-fda7-ece0ba29fe6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.8097\n",
            "[[702  84  81]\n",
            " [  0   0   0]\n",
            " [  0   0   0]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8096885813148789"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the instances into a training, validation and test set\n",
        "train_instances_serve, test_instances_serve = train_test_split(serve_instances, test_size=0.2)\n",
        "train_instances_serve, validation_instances_serve = train_test_split(train_instances_serve, test_size=0.15)\n",
        "print('Train set size:', len(train_instances_serve))\n",
        "print('Validation set size:', len(validation_instances_serve))\n",
        "print('Test set size:', len(test_instances_serve))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4WDe5Ytv-S-",
        "outputId": "70c9fd9f-ec48-4db5-bc6c-bcabd227c36c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set size: 2976\n",
            "Validation set size: 526\n",
            "Test set size: 876\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wsd_classifier(train_instances_serve, validation_instances_serve, serve_senses, 'frequency', 300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDXCrDWjymyl",
        "outputId": "a59d32df-f000-4229-ce69-5fff4b5efa71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Class prior probabilities:\n",
            " [0.40625    0.29099462 0.1999328  0.10282258]\n",
            "\n",
            "Likelihood:\n",
            " [[0.20913842 0.15909091 0.00245486 ... 0.00063351 0.00174216 0.00055432]\n",
            " [0.20731887 0.19396488 0.00103291 ... 0.00132802 0.00036889 0.00095913]\n",
            " [0.20653477 0.2198741  0.00029976 ... 0.00089928 0.00089928 0.00089928]\n",
            " [0.19350042 0.20412065 0.00063721 ... 0.00106202 0.         0.00106202]]\n",
            "\n",
            "Accuracy: 0.7281\n",
            "[[195   7  22   3]\n",
            " [ 15 124  23  13]\n",
            " [ 13  16  39   6]\n",
            " [  6  11   8  25]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7281368821292775"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocabulary_sizes = [i for i in range(100, 900, 100)]\n",
        "\n",
        "best_acc, accuracies = 0, []\n",
        "best_vocab_size = None\n",
        "for vocabulary_size in vocabulary_sizes:\n",
        "  acc = wsd_classifier(train_instances_serve, validation_instances_serve, serve_senses, 'frequency', vocabulary_size, \n",
        "                       show_parameters_estimation=False, show_confusion_matrix=False)\n",
        "  accuracies.append(acc)\n",
        "  if acc > best_acc:\n",
        "    best_acc = acc\n",
        "    best_vocab_size = vocabulary_size                 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Cs2NO7WyvST",
        "outputId": "9813ab1a-1939-491f-9ae4-1fc11a1c1443"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.6825\n",
            "\n",
            "Accuracy: 0.7319\n",
            "\n",
            "Accuracy: 0.7281\n",
            "\n",
            "Accuracy: 0.7319\n",
            "\n",
            "Accuracy: 0.7490\n",
            "\n",
            "Accuracy: 0.7624\n",
            "\n",
            "Accuracy: 0.7700\n",
            "\n",
            "Accuracy: 0.7776\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(vocabulary_sizes, accuracies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "tNDS3A-LzTm9",
        "outputId": "0fb0654b-b249-4d41-83b9-585de7be23e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fe71f0410>"
            ]
          },
          "metadata": {},
          "execution_count": 122
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test classifier with best value found for vocabulary size\n",
        "wsd_classifier(train_instances_serve, test_instances_serve, serve_senses, 'frequency', best_vocab_size, show_parameters_estimation=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ZqnsKl9z9iJ",
        "outputId": "1f91e9ee-6677-43df-d0ce-13a741626bb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.7957\n",
            "[[346  13  35  10]\n",
            " [  7 206  31  14]\n",
            " [ 20  19  93  10]\n",
            " [  3  10   7  52]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.795662100456621"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Features: surrounding context of a target word\n",
        "wsd_classifier(train_instances_serve, validation_instances_serve, serve_senses, 'context', window_size=3, pos_tag=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DeV_OuAhFu4R",
        "outputId": "2042938c-63e7-4b84-96a8-20ada9bcb709"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Class prior probabilities:\n",
            " [0.40625    0.29099462 0.1999328  0.10282258]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.8460\n",
            "[[208   5   7   0]\n",
            " [  6 136  12   8]\n",
            " [ 14  11  73  11]\n",
            " [  1   6   0  28]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8460076045627376"
            ]
          },
          "metadata": {},
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "window_size = [i for i in range(1, 6, 1)]\n",
        "best_acc, accuracies = 0, []\n",
        "best_window_size = None\n",
        "for window_size in window_sizes:\n",
        "  acc = wsd_classifier(train_instances_serve, validation_instances_serve, serve_senses, 'context', window_size=window_size, pos_tag=False,\n",
        "                       show_parameters_estimation=False, show_confusion_matrix=False)\n",
        "  accuracies.append(acc)\n",
        "  if acc > best_acc:\n",
        "    best_acc = acc\n",
        "    best_window_size = window_size                 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4vPoeKzCGAFU",
        "outputId": "7a17d8ea-6dc2-4d96-c683-00da257193ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.6654\n",
            "\n",
            "Accuracy: 0.8460\n",
            "\n",
            "Accuracy: 0.8498\n",
            "\n",
            "Accuracy: 0.8308\n",
            "\n",
            "Accuracy: 0.8080\n",
            "\n",
            "Accuracy: 0.7681\n",
            "\n",
            "Accuracy: 0.7433\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test classifier with best value found for window size\n",
        "wsd_classifier(train_instances_serve, test_instances_serve, serve_senses, 'context', window_size=best_window_size, \n",
        "               show_parameters_estimation=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSPAdNYTHTuG",
        "outputId": "2e126878-e355-4e0f-d926-cc43e9cb5b20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.8630\n",
            "[[350  10  16   6]\n",
            " [ 11 213  13   7]\n",
            " [ 13  21 130  10]\n",
            " [  2   4   7  63]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.863013698630137"
            ]
          },
          "metadata": {},
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Features: surrounding context (words and their corresponding pos tags) of a target word\n",
        "wsd_classifier(train_instances_serve, validation_instances_serve, serve_senses, 'context', window_size=3, pos_tag=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AgfZGnWyTNaH",
        "outputId": "0a6d3f12-1fc4-4e41-bde5-410808c8dd9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Class prior probabilities:\n",
            " [0.40625    0.29099462 0.1999328  0.10282258]\n",
            "\n",
            "Accuracy: 0.4354\n",
            "[[229 158  92  47]\n",
            " [  0   0   0   0]\n",
            " [  0   0   0   0]\n",
            " [  0   0   0   0]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.435361216730038"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "window_size = [i for i in range(1, 3, 1)]\n",
        "best_acc, accuracies = 0, []\n",
        "best_window_size = None\n",
        "for window_size in window_sizes:\n",
        "  acc = wsd_classifier(train_instances_serve, validation_instances_serve, serve_senses, 'context', window_size=window_size, pos_tag=True,\n",
        "                       show_parameters_estimation=False, show_confusion_matrix=False)\n",
        "  accuracies.append(acc)\n",
        "  if acc > best_acc:\n",
        "    best_acc = acc\n",
        "    best_window_size = window_size                 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WOHblvgKTG8o",
        "outputId": "e6e7d87b-e181-4e78-c0df-2e2c7c69a7bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.4354\n",
            "\n",
            "Accuracy: 0.4354\n",
            "\n",
            "Accuracy: 0.4354\n",
            "\n",
            "Accuracy: 0.4354\n",
            "\n",
            "Accuracy: 0.4354\n",
            "\n",
            "Accuracy: 0.4354\n",
            "\n",
            "Accuracy: 0.4354\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test classifier with best value found for window size\n",
        "wsd_classifier(train_instances_serve, test_instances_serve, serve_senses, 'context', window_size=best_window_size, pos_tag=True,\n",
        "               show_parameters_estimation=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdTCpu-OTD53",
        "outputId": "28dbedbe-c4d4-41fd-a1bd-11d7d74ebbf2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.4292\n",
            "[[376 248 166  86]\n",
            " [  0   0   0   0]\n",
            " [  0   0   0   0]\n",
            " [  0   0   0   0]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4292237442922374"
            ]
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the instances into a training, validation and test set\n",
        "train_instances_interest, test_instances_interest = train_test_split(interest_instances, test_size=0.2)\n",
        "train_instances_interest, validation_instances_interest = train_test_split(train_instances_interest, test_size=0.15)\n",
        "print('Train set size:', len(train_instances_interest))\n",
        "print('Validation set size:', len(validation_instances_interest))\n",
        "print('Test set size:', len(test_instances_interest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zW0K6IpAz54C",
        "outputId": "d9265c93-b546-4099-b5c5-25ebfd6ef562"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set size: 1609\n",
            "Validation set size: 285\n",
            "Test set size: 474\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wsd_classifier(train_instances_interest, validation_instances_interest, interest_senses, 'frequency', 300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEAqa7ph0Vu2",
        "outputId": "1576fc74-4c99-4647-d1a1-bbcce87934e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Class prior probabilities:\n",
            " [0.15102548 0.00559354 0.02672467 0.0770665  0.20820385 0.53138595]\n",
            "\n",
            "Likelihood:\n",
            " [[0.17592161 0.13999067 0.00093327 ... 0.00139991 0.00093327 0.        ]\n",
            " [0.15492958 0.15492958 0.         ... 0.         0.         0.        ]\n",
            " [0.19951338 0.20924574 0.         ... 0.         0.00243309 0.        ]\n",
            " [0.17854002 0.16007036 0.00087951 ... 0.         0.00087951 0.        ]\n",
            " [0.16991643 0.16333249 0.         ... 0.00025323 0.00202583 0.00025323]\n",
            " [0.1312767  0.10659706 0.04337921 ... 0.00113906 0.00047461 0.00151875]]\n",
            "\n",
            "Accuracy: 0.8070\n",
            "[[ 20   0   0   2   5   4]\n",
            " [  0   0   0   0   0   0]\n",
            " [  0   0   1   0   3   0]\n",
            " [  8   0   3  14   3   0]\n",
            " [  1   0   2   3  51   3]\n",
            " [ 13   1   0   1   3 144]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8070175438596491"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocabulary_sizes = [i for i in range(100, 900, 100)]\n",
        "\n",
        "best_acc, accuracies = 0, []\n",
        "best_vocab_size = None\n",
        "for vocabulary_size in vocabulary_sizes:\n",
        "  acc = wsd_classifier(train_instances_interest, validation_instances_interest, interest_senses, 'frequency', vocabulary_size, \n",
        "                       show_parameters_estimation=False, show_confusion_matrix=False)\n",
        "  accuracies.append(acc)\n",
        "  if acc > best_acc:\n",
        "    best_acc = acc\n",
        "    best_vocab_size = vocabulary_size                 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-96l_5O0wes",
        "outputId": "0c510787-065a-4f40-9e63-5770fb24564d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.7579\n",
            "\n",
            "Accuracy: 0.7965\n",
            "\n",
            "Accuracy: 0.8070\n",
            "\n",
            "Accuracy: 0.7895\n",
            "\n",
            "Accuracy: 0.7895\n",
            "\n",
            "Accuracy: 0.7684\n",
            "\n",
            "Accuracy: 0.7439\n",
            "\n",
            "Accuracy: 0.7368\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(vocabulary_sizes, accuracies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "vDqQf79v041G",
        "outputId": "b40e2f83-86a6-49e7-ee18-ff91fd4ba411"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fe72b6310>"
            ]
          },
          "metadata": {},
          "execution_count": 127
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test classifier with best value found for vocabulary size\n",
        "wsd_classifier(train_instances_interest, test_instances_interest, interest_senses, 'frequency', best_vocab_size, show_parameters_estimation=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zHogwRZ08qE",
        "outputId": "23e91064-e402-458b-84db-96d671e4a3f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.7890\n",
            "[[ 45   0   2   8  10   2]\n",
            " [  0   0   0   0   0   0]\n",
            " [  1   0   5   0   1   0]\n",
            " [  7   0   6  22  10   4]\n",
            " [  6   1   4   4  71   9]\n",
            " [ 17   0   0   0   8 231]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7890295358649789"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Features: surrounding context of a target word\n",
        "wsd_classifier(train_instances_interest, validation_instances_interest, interest_senses, 'context', window_size=3, pos_tag=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygdjVb3mSK0X",
        "outputId": "ff3cafd4-0937-4a6b-9cd8-214764b09574"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Class prior probabilities:\n",
            " [0.15102548 0.00559354 0.02672467 0.0770665  0.20820385 0.53138595]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.8211\n",
            "[[ 27   0   0   2   2   5]\n",
            " [  0   1   0   0   0   0]\n",
            " [  0   0   3   0   1   0]\n",
            " [  2   0   1  13   2   3]\n",
            " [  4   0   1   1  54   7]\n",
            " [  9   0   1   4   6 136]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8210526315789474"
            ]
          },
          "metadata": {},
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "window_size = [i for i in range(1, 6, 1)]\n",
        "best_acc, accuracies = 0, []\n",
        "best_window_size = None\n",
        "for window_size in window_sizes:\n",
        "  acc = wsd_classifier(train_instances_interest, validation_instances_interest, interest_senses, 'context', window_size=window_size, pos_tag=False,\n",
        "                       show_parameters_estimation=False, show_confusion_matrix=False)\n",
        "  accuracies.append(acc)\n",
        "  if acc > best_acc:\n",
        "    best_acc = acc\n",
        "    best_window_size = window_size                 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kE4Cfa4vSTUe",
        "outputId": "1eb944ff-ae4b-41e1-a0ca-d0a9558e130d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.7684\n",
            "\n",
            "Accuracy: 0.8211\n",
            "\n",
            "Accuracy: 0.7825\n",
            "\n",
            "Accuracy: 0.7263\n",
            "\n",
            "Accuracy: 0.6807\n",
            "\n",
            "Accuracy: 0.6281\n",
            "\n",
            "Accuracy: 0.6175\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test classifier with best value found for window size\n",
        "wsd_classifier(train_instances_interest, test_instances_interest, interest_senses, 'context', window_size=best_window_size, \n",
        "               show_parameters_estimation=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zTpBh73hScfK",
        "outputId": "cc8d542a-de7a-4b50-8553-510774a41bdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.8165\n",
            "[[ 45   1   1   6  10   4]\n",
            " [  1   0   0   0   0   0]\n",
            " [  1   0   6   0   0   0]\n",
            " [  6   0   2  20   5   0]\n",
            " [  4   0   3   3  76   2]\n",
            " [ 19   0   5   5   9 240]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8164556962025317"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Features: surrounding context (words and their corresponding pos_tags) of a target word\n",
        "wsd_classifier(train_instances_interest, validation_instances_interest, interest_senses, 'context', window_size=3, pos_tag=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-_cHR5iiTjE_",
        "outputId": "d622aab4-882f-4cf1-8271-64f854ed20b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Class prior probabilities:\n",
            " [0.15102548 0.00559354 0.02672467 0.0770665  0.20820385 0.53138595]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:79: RuntimeWarning: divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.8211\n",
            "[[ 27   0   0   2   2   5]\n",
            " [  0   1   0   0   0   0]\n",
            " [  0   0   3   0   1   0]\n",
            " [  2   0   1  13   2   3]\n",
            " [  4   0   1   1  54   7]\n",
            " [  9   0   1   4   6 136]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8210526315789474"
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "window_size = [i for i in range(1, 4, 1)]\n",
        "best_acc, accuracies = 0, []\n",
        "best_window_size = None\n",
        "for window_size in window_sizes:\n",
        "  acc = wsd_classifier(train_instances_interest, validation_instances_interest, interest_senses, 'context', window_size=window_size, pos_tag=True,\n",
        "                       show_parameters_estimation=False, show_confusion_matrix=False)\n",
        "  accuracies.append(acc)\n",
        "  if acc > best_acc:\n",
        "    best_acc = acc\n",
        "    best_window_size = window_size                 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H04ZQ0LhTjHr",
        "outputId": "3f0f0a5d-0fb9-416c-e14d-78148bef0e17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.5298\n",
            "\n",
            "Accuracy: 0.5298\n",
            "\n",
            "Accuracy: 0.5298\n",
            "\n",
            "Accuracy: 0.5298\n",
            "\n",
            "Accuracy: 0.5298\n",
            "\n",
            "Accuracy: 0.5298\n",
            "\n",
            "Accuracy: 0.5298\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test classifier with best value found for window size\n",
        "wsd_classifier(train_instances_interest, test_instances_interest, interest_senses, 'context', window_size=best_window_size, pos_tag=True,\n",
        "               show_parameters_estimation=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDSarSRJTc8p",
        "outputId": "becb2da0-d586-4ad3-8c20-dc2e9e46997b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.5190\n",
            "[[  0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0]\n",
            " [ 76   1  17  34 100 246]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5189873417721519"
            ]
          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In our case, including the pos_tags of words surrounding the target word did not lead to better results."
      ],
      "metadata": {
        "id": "LEi8pDgoT8dZ"
      }
    }
  ]
}