NLP1/POS_tagging,_Syntax (2).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "POS-tagging, Syntax.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# The Stanford POS Tagger\n"
      ],
      "metadata": {
        "id": "TOiBlh5oa6ze"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Web app version: http://nlp.stanford.edu:8080/parser/\n",
        "\n",
        "Newer version of the NLTK interface, requires running a java server locally: https://github.com/nltk/nltk/wiki/Stanford-CoreNLP-API-in-NLTK\n"
      ],
      "metadata": {
        "id": "cfHeXLEUgIKn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Downloading the tagger and models"
      ],
      "metadata": {
        "id": "MOXWLw-SbCpQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Download and uzip the model. You can do the same thing on your own computer to be able to use it locally."
      ],
      "metadata": {
        "id": "rmImjTN1bZMS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "!wget 'https://nlp.stanford.edu/software/stanford-tagger-4.2.0.zip'\n",
        "!unzip './stanford-tagger-4.2.0.zip'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9nYqHkCnbF0o",
        "outputId": "1bb9842b-70e4-40d9-ee2c-709336f3c7aa"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-31 17:24:37--  https://nlp.stanford.edu/software/stanford-tagger-4.2.0.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 FOUND\n",
            "Location: https://downloads.cs.stanford.edu/nlp/software/stanford-tagger-4.2.0.zip [following]\n",
            "--2022-03-31 17:24:38--  https://downloads.cs.stanford.edu/nlp/software/stanford-tagger-4.2.0.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 78034596 (74M) [application/zip]\n",
            "Saving to: ‘stanford-tagger-4.2.0.zip’\n",
            "\n",
            "stanford-tagger-4.2 100%[===================>]  74.42M  4.72MB/s    in 12s     \n",
            "\n",
            "2022-03-31 17:24:50 (6.18 MB/s) - ‘stanford-tagger-4.2.0.zip’ saved [78034596/78034596]\n",
            "\n",
            "Archive:  ./stanford-tagger-4.2.0.zip\n",
            "   creating: stanford-postagger-full-2020-11-17/\n",
            "  inflating: stanford-postagger-full-2020-11-17/stanford-postagger-gui.sh  \n",
            "  inflating: stanford-postagger-full-2020-11-17/sample-input.txt  \n",
            "  inflating: stanford-postagger-full-2020-11-17/stanford-postagger.jar  \n",
            "   creating: stanford-postagger-full-2020-11-17/data/\n",
            "  inflating: stanford-postagger-full-2020-11-17/data/enclitic-inflections.data  \n",
            "  inflating: stanford-postagger-full-2020-11-17/stanford-postagger-4.2.0-javadoc.jar  \n",
            "  inflating: stanford-postagger-full-2020-11-17/README.txt  \n",
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            "  inflating: stanford-postagger-full-2020-11-17/stanford-postagger.sh  \n",
            "   creating: stanford-postagger-full-2020-11-17/models/\n",
            "  inflating: stanford-postagger-full-2020-11-17/models/english-left3words-distsim.tagger.props  \n",
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            "  inflating: stanford-postagger-full-2020-11-17/stanford-postagger-gui.bat  \n",
            "  inflating: stanford-postagger-full-2020-11-17/build.xml  \n",
            "  inflating: stanford-postagger-full-2020-11-17/TaggerDemo2.java  \n",
            "  inflating: stanford-postagger-full-2020-11-17/sample-output.txt  \n",
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            "  inflating: stanford-postagger-full-2020-11-17/TaggerDemo.java  \n",
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            "  inflating: stanford-postagger-full-2020-11-17/LICENSE.txt  \n",
            "CPU times: user 218 ms, sys: 39.8 ms, total: 258 ms\n",
            "Wall time: 16.3 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setting up and using the tagger with NLTK"
      ],
      "metadata": {
        "id": "gT_y9OzBbGwg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_path='./stanford-postagger-full-2020-11-17/models/english-bidirectional-distsim.tagger'\n",
        "jar_tagger_path='./stanford-postagger-full-2020-11-17/stanford-postagger-4.2.0.jar'"
      ],
      "metadata": {
        "id": "xUr6gPwJFKuD"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "6e8EcS6qqVL5"
      },
      "outputs": [],
      "source": [
        "from nltk.tag.stanford import StanfordPOSTagger # -- deprecated?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip freeze | grep nltk"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7kRn3vy0SYlB",
        "outputId": "081fe644-c6d9-4ba9-f286-3e1efffb6bd3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nltk==3.2.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tagger=StanfordPOSTagger(model_path, jar_tagger_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6dDj5bcErgs",
        "outputId": "e7a8fa51-325d-43f1-ac69-98743a1d8de6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/nltk/tag/stanford.py:149: DeprecationWarning: \n",
            "The StanfordTokenizer will be deprecated in version 3.2.5.\n",
            "Please use \u001b[91mnltk.tag.corenlp.CoreNLPPOSTagger\u001b[0m or \u001b[91mnltk.tag.corenlp.CoreNLPNERTagger\u001b[0m instead.\n",
            "  super(StanfordPOSTagger, self).__init__(*args, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNGYvc7GWfrX",
        "outputId": "59ff409b-2fdb-43ac-c0fe-670766e81643"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tagger.tag(nltk.word_tokenize(\"I am eating a lot of candy.\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ovqmsl_yE1ac",
        "outputId": "cb9e8ddc-152f-4855-f7b9-0d2bce9f9155"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('I', 'PRP'),\n",
              " ('am', 'VBP'),\n",
              " ('eating', 'VBG'),\n",
              " ('a', 'DT'),\n",
              " ('lot', 'NN'),\n",
              " ('of', 'IN'),\n",
              " ('candy', 'NN'),\n",
              " ('.', '.')]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tagger.tag(nltk.word_tokenize(\"Time flies like an arrow.\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aoauGuMcmT0C",
        "outputId": "b172f981-b0ec-4e20-8b0f-7735405e0be2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Time', 'NNP'),\n",
              " ('flies', 'VBZ'),\n",
              " ('like', 'IN'),\n",
              " ('an', 'DT'),\n",
              " ('arrow', 'NN'),\n",
              " ('.', '.')]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tagger.tag(nltk.word_tokenize(\"Fruit flies like a banana.\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L9VN5EdHWQzQ",
        "outputId": "fc7df062-9995-42e2-e2bb-c46f12492f8e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Fruit', 'NNP'),\n",
              " ('flies', 'VBZ'),\n",
              " ('like', 'IN'),\n",
              " ('a', 'DT'),\n",
              " ('banana', 'NN'),\n",
              " ('.', '.')]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tagger.tag(nltk.word_tokenize(\"I don't like fruit flies like a banana.\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TD98Iz1EmUE2",
        "outputId": "6c52dc36-31b4-4b72-bd97-f2c295c8bd8a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('I', 'PRP'),\n",
              " ('do', 'VBP'),\n",
              " (\"n't\", 'RB'),\n",
              " ('like', 'VB'),\n",
              " ('fruit', 'NN'),\n",
              " ('flies', 'NNS'),\n",
              " ('like', 'IN'),\n",
              " ('a', 'DT'),\n",
              " ('banana', 'NN'),\n",
              " ('.', '.')]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tagger.tag(nltk.word_tokenize(\"I am eating a lot of candy.\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M4sjjsmG4Jc7",
        "outputId": "7ca4fe51-28dd-4398-ec96-9b1cbbdbfc82"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('I', 'PRP'),\n",
              " ('am', 'VBP'),\n",
              " ('eating', 'VBG'),\n",
              " ('a', 'DT'),\n",
              " ('lot', 'NN'),\n",
              " ('of', 'IN'),\n",
              " ('candy', 'NN'),\n",
              " ('.', '.')]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem import PorterStemmer\n",
        "st = PorterStemmer()\n",
        "tagger.tag([st.stem(t) \n",
        "      for t in nltk.word_tokenize(\"I am eating a lot of candy.\")])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vjcJgsNAFDl6",
        "outputId": "9d417664-ed75-403c-91a7-6e9f1194ecbd"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('I', 'PRP'),\n",
              " ('am', 'VBP'),\n",
              " ('eat', 'VB'),\n",
              " ('a', 'DT'),\n",
              " ('lot', 'NN'),\n",
              " ('of', 'IN'),\n",
              " ('candi', 'NN'),\n",
              " ('.', '.')]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using averaged_perceptron_tagger in NLTK"
      ],
      "metadata": {
        "id": "eOhBNvOHjbgA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('averaged_perceptron_tagger')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4oiS8XGnajjP",
        "outputId": "804e7744-b258-4e18-e176-9b4be7df9799"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.pos_tag(nltk.word_tokenize(\"I am eating a lot of candy.\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "trUEM4Y3jj1t",
        "outputId": "575e34c0-bc70-4243-cb6e-2cc9e12ecc3d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('I', 'PRP'),\n",
              " ('am', 'VBP'),\n",
              " ('eating', 'VBG'),\n",
              " ('a', 'DT'),\n",
              " ('lot', 'NN'),\n",
              " ('of', 'IN'),\n",
              " ('candy', 'NN'),\n",
              " ('.', '.')]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.pos_tag(nltk.word_tokenize(\"Time flies like an arrow.\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmrSdotFjrpk",
        "outputId": "b0259292-94a2-4ccc-f5cf-26652e6797bb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Time', 'NNP'),\n",
              " ('flies', 'NNS'),\n",
              " ('like', 'IN'),\n",
              " ('an', 'DT'),\n",
              " ('arrow', 'NN'),\n",
              " ('.', '.')]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.pos_tag(nltk.word_tokenize(\"Fruit flies like a banana.\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "213h9xIbmMcF",
        "outputId": "b927313f-50d7-482c-e218-7fc35633a99f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Fruit', 'NNP'),\n",
              " ('flies', 'VBZ'),\n",
              " ('like', 'IN'),\n",
              " ('a', 'DT'),\n",
              " ('banana', 'NN'),\n",
              " ('.', '.')]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Understanding the tags"
      ],
      "metadata": {
        "id": "UPjryCPmcUhA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('tagsets')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cvzHwm3cWOg",
        "outputId": "f36a1899-c92a-4727-86dc-24ea92dedab9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package tagsets to /root/nltk_data...\n",
            "[nltk_data]   Unzipping help/tagsets.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.help.upenn_tagset('NNP')\n",
        "print()\n",
        "nltk.help.upenn_tagset()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmE9xp0ocdT4",
        "outputId": "46662fbf-c8f4-4de2-ae8d-9b1899ee3f06"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NNP: noun, proper, singular\n",
            "    Motown Venneboerger Czestochwa Ranzer Conchita Trumplane Christos\n",
            "    Oceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCA\n",
            "    Shannon A.K.C. Meltex Liverpool ...\n",
            "\n",
            "$: dollar\n",
            "    $ -$ --$ A$ C$ HK$ M$ NZ$ S$ U.S.$ US$\n",
            "'': closing quotation mark\n",
            "    ' ''\n",
            "(: opening parenthesis\n",
            "    ( [ {\n",
            "): closing parenthesis\n",
            "    ) ] }\n",
            ",: comma\n",
            "    ,\n",
            "--: dash\n",
            "    --\n",
            ".: sentence terminator\n",
            "    . ! ?\n",
            ":: colon or ellipsis\n",
            "    : ; ...\n",
            "CC: conjunction, coordinating\n",
            "    & 'n and both but either et for less minus neither nor or plus so\n",
            "    therefore times v. versus vs. whether yet\n",
            "CD: numeral, cardinal\n",
            "    mid-1890 nine-thirty forty-two one-tenth ten million 0.5 one forty-\n",
            "    seven 1987 twenty '79 zero two 78-degrees eighty-four IX '60s .025\n",
            "    fifteen 271,124 dozen quintillion DM2,000 ...\n",
            "DT: determiner\n",
            "    all an another any both del each either every half la many much nary\n",
            "    neither no some such that the them these this those\n",
            "EX: existential there\n",
            "    there\n",
            "FW: foreign word\n",
            "    gemeinschaft hund ich jeux habeas Haementeria Herr K'ang-si vous\n",
            "    lutihaw alai je jour objets salutaris fille quibusdam pas trop Monte\n",
            "    terram fiche oui corporis ...\n",
            "IN: preposition or conjunction, subordinating\n",
            "    astride among uppon whether out inside pro despite on by throughout\n",
            "    below within for towards near behind atop around if like until below\n",
            "    next into if beside ...\n",
            "JJ: adjective or numeral, ordinal\n",
            "    third ill-mannered pre-war regrettable oiled calamitous first separable\n",
            "    ectoplasmic battery-powered participatory fourth still-to-be-named\n",
            "    multilingual multi-disciplinary ...\n",
            "JJR: adjective, comparative\n",
            "    bleaker braver breezier briefer brighter brisker broader bumper busier\n",
            "    calmer cheaper choosier cleaner clearer closer colder commoner costlier\n",
            "    cozier creamier crunchier cuter ...\n",
            "JJS: adjective, superlative\n",
            "    calmest cheapest choicest classiest cleanest clearest closest commonest\n",
            "    corniest costliest crassest creepiest crudest cutest darkest deadliest\n",
            "    dearest deepest densest dinkiest ...\n",
            "LS: list item marker\n",
            "    A A. B B. C C. D E F First G H I J K One SP-44001 SP-44002 SP-44005\n",
            "    SP-44007 Second Third Three Two * a b c d first five four one six three\n",
            "    two\n",
            "MD: modal auxiliary\n",
            "    can cannot could couldn't dare may might must need ought shall should\n",
            "    shouldn't will would\n",
            "NN: noun, common, singular or mass\n",
            "    common-carrier cabbage knuckle-duster Casino afghan shed thermostat\n",
            "    investment slide humour falloff slick wind hyena override subhumanity\n",
            "    machinist ...\n",
            "NNP: noun, proper, singular\n",
            "    Motown Venneboerger Czestochwa Ranzer Conchita Trumplane Christos\n",
            "    Oceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCA\n",
            "    Shannon A.K.C. Meltex Liverpool ...\n",
            "NNPS: noun, proper, plural\n",
            "    Americans Americas Amharas Amityvilles Amusements Anarcho-Syndicalists\n",
            "    Andalusians Andes Andruses Angels Animals Anthony Antilles Antiques\n",
            "    Apache Apaches Apocrypha ...\n",
            "NNS: noun, common, plural\n",
            "    undergraduates scotches bric-a-brac products bodyguards facets coasts\n",
            "    divestitures storehouses designs clubs fragrances averages\n",
            "    subjectivists apprehensions muses factory-jobs ...\n",
            "PDT: pre-determiner\n",
            "    all both half many quite such sure this\n",
            "POS: genitive marker\n",
            "    ' 's\n",
            "PRP: pronoun, personal\n",
            "    hers herself him himself hisself it itself me myself one oneself ours\n",
            "    ourselves ownself self she thee theirs them themselves they thou thy us\n",
            "PRP$: pronoun, possessive\n",
            "    her his mine my our ours their thy your\n",
            "RB: adverb\n",
            "    occasionally unabatingly maddeningly adventurously professedly\n",
            "    stirringly prominently technologically magisterially predominately\n",
            "    swiftly fiscally pitilessly ...\n",
            "RBR: adverb, comparative\n",
            "    further gloomier grander graver greater grimmer harder harsher\n",
            "    healthier heavier higher however larger later leaner lengthier less-\n",
            "    perfectly lesser lonelier longer louder lower more ...\n",
            "RBS: adverb, superlative\n",
            "    best biggest bluntest earliest farthest first furthest hardest\n",
            "    heartiest highest largest least less most nearest second tightest worst\n",
            "RP: particle\n",
            "    aboard about across along apart around aside at away back before behind\n",
            "    by crop down ever fast for forth from go high i.e. in into just later\n",
            "    low more off on open out over per pie raising start teeth that through\n",
            "    under unto up up-pp upon whole with you\n",
            "SYM: symbol\n",
            "    % & ' '' ''. ) ). * + ,. < = > @ A[fj] U.S U.S.S.R * ** ***\n",
            "TO: \"to\" as preposition or infinitive marker\n",
            "    to\n",
            "UH: interjection\n",
            "    Goodbye Goody Gosh Wow Jeepers Jee-sus Hubba Hey Kee-reist Oops amen\n",
            "    huh howdy uh dammit whammo shucks heck anyways whodunnit honey golly\n",
            "    man baby diddle hush sonuvabitch ...\n",
            "VB: verb, base form\n",
            "    ask assemble assess assign assume atone attention avoid bake balkanize\n",
            "    bank begin behold believe bend benefit bevel beware bless boil bomb\n",
            "    boost brace break bring broil brush build ...\n",
            "VBD: verb, past tense\n",
            "    dipped pleaded swiped regummed soaked tidied convened halted registered\n",
            "    cushioned exacted snubbed strode aimed adopted belied figgered\n",
            "    speculated wore appreciated contemplated ...\n",
            "VBG: verb, present participle or gerund\n",
            "    telegraphing stirring focusing angering judging stalling lactating\n",
            "    hankerin' alleging veering capping approaching traveling besieging\n",
            "    encrypting interrupting erasing wincing ...\n",
            "VBN: verb, past participle\n",
            "    multihulled dilapidated aerosolized chaired languished panelized used\n",
            "    experimented flourished imitated reunifed factored condensed sheared\n",
            "    unsettled primed dubbed desired ...\n",
            "VBP: verb, present tense, not 3rd person singular\n",
            "    predominate wrap resort sue twist spill cure lengthen brush terminate\n",
            "    appear tend stray glisten obtain comprise detest tease attract\n",
            "    emphasize mold postpone sever return wag ...\n",
            "VBZ: verb, present tense, 3rd person singular\n",
            "    bases reconstructs marks mixes displeases seals carps weaves snatches\n",
            "    slumps stretches authorizes smolders pictures emerges stockpiles\n",
            "    seduces fizzes uses bolsters slaps speaks pleads ...\n",
            "WDT: WH-determiner\n",
            "    that what whatever which whichever\n",
            "WP: WH-pronoun\n",
            "    that what whatever whatsoever which who whom whosoever\n",
            "WP$: WH-pronoun, possessive\n",
            "    whose\n",
            "WRB: Wh-adverb\n",
            "    how however whence whenever where whereby whereever wherein whereof why\n",
            "``: opening quotation mark\n",
            "    ` ``\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Syntactic Parsing"
      ],
      "metadata": {
        "id": "BVvUf_bfjv7V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Stanford Parser"
      ],
      "metadata": {
        "id": "USY05F9NHDfs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can try the usage below locally: (needs java)"
      ],
      "metadata": {
        "id": "1wW5TEazbjwV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget 'https://nlp.stanford.edu/software/stanford-parser-4.2.0.zip'\n",
        "!unzip 'stanford-parser-4.2.0.zip'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWCNPBqTnEej",
        "outputId": "c0dd8638-ed82-405f-9a25-b76a6158ef98"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-31 17:25:14--  https://nlp.stanford.edu/software/stanford-parser-4.2.0.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 FOUND\n",
            "Location: https://downloads.cs.stanford.edu/nlp/software/stanford-parser-4.2.0.zip [following]\n",
            "--2022-03-31 17:25:14--  https://downloads.cs.stanford.edu/nlp/software/stanford-parser-4.2.0.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 182285548 (174M) [application/zip]\n",
            "Saving to: ‘stanford-parser-4.2.0.zip’\n",
            "\n",
            "stanford-parser-4.2 100%[===================>] 173.84M  4.95MB/s    in 32s     \n",
            "\n",
            "2022-03-31 17:25:47 (5.36 MB/s) - ‘stanford-parser-4.2.0.zip’ saved [182285548/182285548]\n",
            "\n",
            "Archive:  stanford-parser-4.2.0.zip\n",
            "   creating: stanford-parser-full-2020-11-17/\n",
            "  inflating: stanford-parser-full-2020-11-17/ejml-simple-0.38.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/StanfordDependenciesManual.pdf  \n",
            "  inflating: stanford-parser-full-2020-11-17/Makefile  \n",
            "  inflating: stanford-parser-full-2020-11-17/ShiftReduceDemo.java  \n",
            "  inflating: stanford-parser-full-2020-11-17/slf4j-api-1.7.12-sources.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/ejml-core-0.38.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/build.xml  \n",
            "  inflating: stanford-parser-full-2020-11-17/lexparser-gui.sh  \n",
            "   creating: stanford-parser-full-2020-11-17/data/\n",
            " extracting: stanford-parser-full-2020-11-17/data/chinese-onesent-unseg-gb18030.txt  \n",
            " extracting: stanford-parser-full-2020-11-17/data/arabic-onesent-utf8.txt  \n",
            "  inflating: stanford-parser-full-2020-11-17/data/chinese-onesent-unseg-utf8.txt  \n",
            "  inflating: stanford-parser-full-2020-11-17/data/testsent.txt  \n",
            "  inflating: stanford-parser-full-2020-11-17/data/pos-sentences.txt  \n",
            " extracting: stanford-parser-full-2020-11-17/data/chinese-onesent-utf8.txt  \n",
            " extracting: stanford-parser-full-2020-11-17/data/chinese-onesent-gb18030.txt  \n",
            " extracting: stanford-parser-full-2020-11-17/data/english-onesent.txt  \n",
            " extracting: stanford-parser-full-2020-11-17/data/german-onesent.txt  \n",
            "  inflating: stanford-parser-full-2020-11-17/data/french-onesent.txt  \n",
            "  inflating: stanford-parser-full-2020-11-17/LICENSE.txt  \n",
            "  inflating: stanford-parser-full-2020-11-17/stanford-parser-4.2.0-sources.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/lexparser-gui.command  \n",
            "  inflating: stanford-parser-full-2020-11-17/README_dependencies.txt  \n",
            "  inflating: stanford-parser-full-2020-11-17/ejml-ddense-0.38.jar  \n",
            "   creating: stanford-parser-full-2020-11-17/conf/\n",
            "  inflating: stanford-parser-full-2020-11-17/conf/ftb-latest.conf  \n",
            "  inflating: stanford-parser-full-2020-11-17/conf/atb-latest.conf  \n",
            "  inflating: stanford-parser-full-2020-11-17/ejml-ddense-0.39-sources.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/ejml-simple-0.39-sources.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/README.txt  \n",
            "  inflating: stanford-parser-full-2020-11-17/slf4j-simple.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/lexparser-lang-train-test.sh  \n",
            "  inflating: stanford-parser-full-2020-11-17/stanford-parser-4.2.0-models.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/stanford-parser-4.2.0-javadoc.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/lexparser.sh  \n",
            "  inflating: stanford-parser-full-2020-11-17/lexparser-lang.sh  \n",
            "  inflating: stanford-parser-full-2020-11-17/lexparser.bat  \n",
            "  inflating: stanford-parser-full-2020-11-17/ParserDemo2.java  \n",
            "  inflating: stanford-parser-full-2020-11-17/pom.xml  \n",
            "  inflating: stanford-parser-full-2020-11-17/ejml-core-0.39-sources.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/stanford-parser.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/DependencyParserDemo.java  \n",
            "   creating: stanford-parser-full-2020-11-17/bin/\n",
            "  inflating: stanford-parser-full-2020-11-17/bin/makeSerialized.csh  \n",
            "  inflating: stanford-parser-full-2020-11-17/bin/run-tb-preproc  \n",
            "  inflating: stanford-parser-full-2020-11-17/slf4j-api.jar  \n",
            "  inflating: stanford-parser-full-2020-11-17/ParserDemo.java  \n",
            "  inflating: stanford-parser-full-2020-11-17/lexparser_lang.def  \n",
            "  inflating: stanford-parser-full-2020-11-17/lexparser-gui.bat  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !wget 'https://nlp.stanford.edu/software/stanford-corenlp-4.2.0-models-english.jar'"
      ],
      "metadata": {
        "id": "_YSxVuhnoFCQ"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from nltk.parse.stanford import StanfordParser"
      ],
      "metadata": {
        "id": "mRHcRHDgj0Sq"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ['JAVAHOME'] = \"/usr/bin/java\"\n",
        "os.environ['STANFORD_PARSER'] = '/content/stanford-parser-full-2020-11-17/stanford-parser.jar'\n",
        "os.environ['STANFORD_MODELS'] = '/content/stanford-parser-full-2020-11-17/stanford-parser-4.2.0-models.jar'"
      ],
      "metadata": {
        "id": "vi-uBl6yk3HV"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parser = StanfordParser(model_path=\"/content/stanford-corenlp-4.2.0-models-english.jar\")\n",
        "propozitii = parser.raw_parse_sents((\"I like to go to school.\", \"The cat is running through the room.\",\"Where are you?\"))\n",
        "for prop in propozitii:\n",
        "    print(list(prop))"
      ],
      "metadata": {
        "id": "vHnRCOuZAVvW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "outputId": "a9fefafe-37e4-4f80-df6c-2dc9a228e38d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[main] ERROR edu.stanford.nlp.parser.lexparser.LexicalizedParser - java.io.IOException: Unable to open \"/content/stanford-corenlp-4.2.0-models-english.jar\" as class path, filename or URL\n",
            "  edu.stanford.nlp.io.IOUtils.getInputStreamFromURLOrClasspathOrFileSystem(IOUtils.java:501)\n",
            "  edu.stanford.nlp.io.IOUtils.readStreamFromString(IOUtils.java:402)\n",
            "  edu.stanford.nlp.parser.lexparser.LexicalizedParser.getParserFromSerializedFile(LexicalizedParser.java:567)\n",
            "  edu.stanford.nlp.parser.lexparser.LexicalizedParser.getParserFromFile(LexicalizedParser.java:373)\n",
            "  edu.stanford.nlp.parser.lexparser.LexicalizedParser.loadModel(LexicalizedParser.java:183)\n",
            "  edu.stanford.nlp.parser.lexparser.LexicalizedParser.main(LexicalizedParser.java:1373)\n",
            "java.io.IOException: Unable to open \"/content/stanford-corenlp-4.2.0-models-english.jar\" as class path, filename or URL\n",
            "\tat edu.stanford.nlp.io.IOUtils.getInputStreamFromURLOrClasspathOrFileSystem(IOUtils.java:501)\n",
            "\tat edu.stanford.nlp.io.IOUtils.readerFromString(IOUtils.java:634)\n",
            "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.getParserFromTextFile(LexicalizedParser.java:510)\n",
            "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.getParserFromFile(LexicalizedParser.java:375)\n",
            "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.loadModel(LexicalizedParser.java:183)\n",
            "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.main(LexicalizedParser.java:1373)\n",
            "Exception in thread \"main\" java.lang.NullPointerException\n",
            "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.loadModel(LexicalizedParser.java:185)\n",
            "\tat edu.stanford.nlp.parser.lexparser.LexicalizedParser.main(LexicalizedParser.java:1373)\n",
            "\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-dd9aad16e19a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStanfordParser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"/content/stanford-corenlp-4.2.0-models-english.jar\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpropozitii\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_parse_sents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"I like to go to school.\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"The cat is running through the room.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Where are you?\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mprop\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpropozitii\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nltk/parse/stanford.py\u001b[0m in \u001b[0;36mraw_parse_sents\u001b[0;34m(self, sentences, verbose)\u001b[0m\n\u001b[1;32m    150\u001b[0m             \u001b[0;34m'-outputFormat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_OUTPUT_FORMAT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m         ]\n\u001b[0;32m--> 152\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_trees_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentences\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtagged_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msentence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nltk/parse/stanford.py\u001b[0m in \u001b[0;36m_execute\u001b[0;34m(self, cmd, input_, verbose)\u001b[0m\n\u001b[1;32m    216\u001b[0m                 \u001b[0mcmd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m                 stdout, stderr = java(cmd, classpath=self._classpath,\n\u001b[0;32m--> 218\u001b[0;31m                                       stdout=PIPE, stderr=PIPE)\n\u001b[0m\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m             \u001b[0mstdout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mb'\\xc2\\xa0'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34mb' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nltk/__init__.py\u001b[0m in \u001b[0;36mjava\u001b[0;34m(cmd, classpath, stdin, stdout, stderr, blocking)\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_decode_stdoutdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Java command failed : '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: Java command failed : ['/usr/bin/java', '-mx1000m', '-cp', '/content/stanford-parser-full-2020-11-17/stanford-parser-4.2.0-models.jar:/content/stanford-parser-full-2020-11-17/slf4j-api-1.7.12-sources.jar:/content/stanford-parser-full-2020-11-17/stanford-parser-4.2.0-models.jar:/content/stanford-parser-full-2020-11-17/stanford-parser-4.2.0-javadoc.jar:/content/stanford-parser-full-2020-11-17/stanford-parser.jar:/content/stanford-parser-full-2020-11-17/ejml-simple-0.38.jar:/content/stanford-parser-full-2020-11-17/ejml-ddense-0.38.jar:/content/stanford-parser-full-2020-11-17/stanford-parser-4.2.0-sources.jar:/content/stanford-parser-full-2020-11-17/slf4j-api.jar:/content/stanford-parser-full-2020-11-17/ejml-simple-0.39-sources.jar:/content/stanford-parser-full-2020-11-17/ejml-ddense-0.39-sources.jar:/content/stanford-parser-full-2020-11-17/ejml-core-0.38.jar:/content/stanford-parser-full-2020-11-17/slf4j-simple.jar:/content/stanford-parser-full-2020-11-17/ejml-core-0.39-sources.jar', 'edu.stanford.nlp.parser.lexparser.LexicalizedParser', '-model', '/content/stanford-corenlp-4.2.0-models-english.jar', '-sentences', 'newline', '-outputFormat', 'penn', '-encoding', 'utf8', '/tmp/tmphljxa_um']"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Alternatively\n"
      ],
      "metadata": {
        "id": "j5nUR1Y3HHpk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget 'https://nlp.stanford.edu/software/stanford-corenlp-latest.zip'\n",
        "!unzip 'stanford-corenlp-latest.zip'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-dAKHb7QIpI",
        "outputId": "c98c4628-7c24-4f1c-b92e-141154b296e0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-31 17:26:06--  https://nlp.stanford.edu/software/stanford-corenlp-latest.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 FOUND\n",
            "Location: https://downloads.cs.stanford.edu/nlp/software/stanford-corenlp-latest.zip [following]\n",
            "--2022-03-31 17:26:07--  https://downloads.cs.stanford.edu/nlp/software/stanford-corenlp-latest.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 505207915 (482M) [application/zip]\n",
            "Saving to: ‘stanford-corenlp-latest.zip’\n",
            "\n",
            "stanford-corenlp-la 100%[===================>] 481.80M  5.11MB/s    in 92s     \n",
            "\n",
            "2022-03-31 17:27:39 (5.22 MB/s) - ‘stanford-corenlp-latest.zip’ saved [505207915/505207915]\n",
            "\n",
            "Archive:  stanford-corenlp-latest.zip\n",
            "   creating: stanford-corenlp-4.4.0/\n",
            "  inflating: stanford-corenlp-4.4.0/jaxb-api-2.4.0-b180830.0359-sources.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/LIBRARY-LICENSES  \n",
            "  inflating: stanford-corenlp-4.4.0/slf4j-api.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/pom.xml  \n",
            "  inflating: stanford-corenlp-4.4.0/Makefile  \n",
            "  inflating: stanford-corenlp-4.4.0/stanford-corenlp-4.4.0-sources.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/input.txt.xml  \n",
            "  inflating: stanford-corenlp-4.4.0/javax.activation-api-1.2.0.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/LICENSE.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/CoreNLP-to-HTML.xsl  \n",
            "  inflating: stanford-corenlp-4.4.0/joda-time.jar  \n",
            "   creating: stanford-corenlp-4.4.0/sutime/\n",
            "  inflating: stanford-corenlp-4.4.0/sutime/english.sutime.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/sutime/spanish.sutime.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/sutime/defs.sutime.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/sutime/british.sutime.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/sutime/english.holidays.sutime.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/StanfordDependenciesManual.pdf  \n",
            "  inflating: stanford-corenlp-4.4.0/jaxb-impl-2.4.0-b180830.0438.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/RESOURCE-LICENSES  \n",
            "  inflating: stanford-corenlp-4.4.0/jollyday.jar  \n",
            "   creating: stanford-corenlp-4.4.0/tokensregex/\n",
            "  inflating: stanford-corenlp-4.4.0/tokensregex/color.rules.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/tokensregex/color.properties  \n",
            "  inflating: stanford-corenlp-4.4.0/tokensregex/retokenize.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/tokensregex/color.input.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/protobuf-java-3.19.2.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/stanford-corenlp-4.4.0.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/input.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/slf4j-simple.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/ejml-simple-0.39.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/javax.json.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/corenlp.sh  \n",
            "  inflating: stanford-corenlp-4.4.0/istack-commons-runtime-3.0.7.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/ejml-core-0.39.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/javax.json-api-1.0-sources.jar  \n",
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            "  inflating: stanford-corenlp-4.4.0/pom-java-11.xml  \n",
            "  inflating: stanford-corenlp-4.4.0/SemgrexDemo.java  \n",
            "  inflating: stanford-corenlp-4.4.0/ShiftReduceDemo.java  \n",
            "  inflating: stanford-corenlp-4.4.0/jaxb-impl-2.4.0-b180830.0438-sources.jar  \n",
            "   creating: stanford-corenlp-4.4.0/patterns/\n",
            "  inflating: stanford-corenlp-4.4.0/patterns/example.properties  \n",
            "  inflating: stanford-corenlp-4.4.0/patterns/stopwords.txt  \n",
            " extracting: stanford-corenlp-4.4.0/patterns/goldplaces.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/patterns/goldnames.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/patterns/names.txt  \n",
            " extracting: stanford-corenlp-4.4.0/patterns/places.txt  \n",
            " extracting: stanford-corenlp-4.4.0/patterns/otherpeople.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/patterns/presidents.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/xom-1.3.7-sources.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/jollyday-0.4.9-sources.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/build.xml  \n",
            "  inflating: stanford-corenlp-4.4.0/javax.activation-api-1.2.0-sources.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/StanfordCoreNlpDemo.java  \n",
            "  inflating: stanford-corenlp-4.4.0/README.txt  \n",
            "  inflating: stanford-corenlp-4.4.0/pom-java-17.xml  \n",
            "  inflating: stanford-corenlp-4.4.0/ejml-ddense-0.39-sources.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/ejml-simple-0.39-sources.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/ejml-ddense-0.39.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/joda-time-2.10.5-sources.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/xom.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/stanford-corenlp-4.4.0-models.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/input.txt.out  \n",
            "  inflating: stanford-corenlp-4.4.0/ejml-core-0.39-sources.jar  \n",
            "  inflating: stanford-corenlp-4.4.0/stanford-corenlp-4.4.0-javadoc.jar  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install stanfordcorenlp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fw_7gV3pHJpQ",
        "outputId": "48fb1726-ee1b-4d50-bd9b-9e67c783a16a"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting stanfordcorenlp\n",
            "  Downloading stanfordcorenlp-3.9.1.1-py2.py3-none-any.whl (5.7 kB)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from stanfordcorenlp) (5.4.8)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from stanfordcorenlp) (2.23.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->stanfordcorenlp) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->stanfordcorenlp) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->stanfordcorenlp) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->stanfordcorenlp) (3.0.4)\n",
            "Installing collected packages: stanfordcorenlp\n",
            "Successfully installed stanfordcorenlp-3.9.1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import stanfordcorenlp\n",
        "sc = stanfordcorenlp.StanfordCoreNLP('/content/stanford-corenlp-4.4.0')"
      ],
      "metadata": {
        "id": "BVPPE8TePAxK"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dependency parsing"
      ],
      "metadata": {
        "id": "njw19ohd-RLD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"I eat a lot of candy.\"\n",
        "dependencies = sc.dependency_parse(text)\n",
        "dependencies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7CAbPIuRQ2Hy",
        "outputId": "9c1c7f1e-a4ef-445f-854d-b2c35ff9b00a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('ROOT', 0, 2),\n",
              " ('nsubj', 2, 1),\n",
              " ('det', 4, 3),\n",
              " ('obj', 2, 4),\n",
              " ('case', 6, 5),\n",
              " ('nmod', 4, 6),\n",
              " ('punct', 2, 7)]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = nltk.word_tokenize(text)\n",
        "for (t, w1, w2) in dependencies:\n",
        "  if w1 < len(tokens) and w2 < len(tokens):\n",
        "    print(\"%s --> %s (%s)\" % (\n",
        "        tokens[w2-1] if w2>0 else \"\", \n",
        "        tokens[w1-1] if w1>0 else \"\",\n",
        "         t))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpkrxm6dZCeh",
        "outputId": "f3e6a83c-2aa5-4d10-9da0-a1ad5154d5cd"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "eat -->  (ROOT)\n",
            "I --> eat (nsubj)\n",
            "a --> lot (det)\n",
            "lot --> eat (obj)\n",
            "of --> candy (case)\n",
            "candy --> lot (nmod)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Descriptions of dependency relations: https://universaldependencies.org/u/dep/\n",
        "\n",
        "Demo: https://nlp.stanford.edu/software/stanford-dependencies.html\n"
      ],
      "metadata": {
        "id": "a5u_0xRdfaP8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Constituent parsing"
      ],
      "metadata": {
        "id": "vwjQVOKv-NpC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "parsed = sc.parse(text)\n",
        "print(parsed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GwAAqvOFQ8Ff",
        "outputId": "8d2a7958-bf8a-4ae7-de5d-cac8adaa7344"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(ROOT\n",
            "  (S\n",
            "    (NP (PRP I))\n",
            "    (VP (VBP eat)\n",
            "      (NP\n",
            "        (NP (DT a) (NN lot))\n",
            "        (PP (IN of)\n",
            "          (NP (NN candy)))))\n",
            "    (. .)))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tagged corpora"
      ],
      "metadata": {
        "id": "BdLxIkm7-nER"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('brown')\n",
        "nltk.corpus.brown.tagged_words()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QyMcw22io47Y",
        "outputId": "79efab00-932a-4dea-d86c-c41b956bb7a5"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package brown to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/brown.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('The', 'AT'), ('Fulton', 'NP-TL'), ...]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(nltk.corpus.brown.tagged_sents(categories='news'))[:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDispBNWHx9A",
        "outputId": "9fc62cd5-c3da-486a-ef6f-c7d36361827d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[('The', 'AT'),\n",
              "  ('Fulton', 'NP-TL'),\n",
              "  ('County', 'NN-TL'),\n",
              "  ('Grand', 'JJ-TL'),\n",
              "  ('Jury', 'NN-TL'),\n",
              "  ('said', 'VBD'),\n",
              "  ('Friday', 'NR'),\n",
              "  ('an', 'AT'),\n",
              "  ('investigation', 'NN'),\n",
              "  ('of', 'IN'),\n",
              "  (\"Atlanta's\", 'NP$'),\n",
              "  ('recent', 'JJ'),\n",
              "  ('primary', 'NN'),\n",
              "  ('election', 'NN'),\n",
              "  ('produced', 'VBD'),\n",
              "  ('``', '``'),\n",
              "  ('no', 'AT'),\n",
              "  ('evidence', 'NN'),\n",
              "  (\"''\", \"''\"),\n",
              "  ('that', 'CS'),\n",
              "  ('any', 'DTI'),\n",
              "  ('irregularities', 'NNS'),\n",
              "  ('took', 'VBD'),\n",
              "  ('place', 'NN'),\n",
              "  ('.', '.')],\n",
              " [('The', 'AT'),\n",
              "  ('jury', 'NN'),\n",
              "  ('further', 'RBR'),\n",
              "  ('said', 'VBD'),\n",
              "  ('in', 'IN'),\n",
              "  ('term-end', 'NN'),\n",
              "  ('presentments', 'NNS'),\n",
              "  ('that', 'CS'),\n",
              "  ('the', 'AT'),\n",
              "  ('City', 'NN-TL'),\n",
              "  ('Executive', 'JJ-TL'),\n",
              "  ('Committee', 'NN-TL'),\n",
              "  (',', ','),\n",
              "  ('which', 'WDT'),\n",
              "  ('had', 'HVD'),\n",
              "  ('over-all', 'JJ'),\n",
              "  ('charge', 'NN'),\n",
              "  ('of', 'IN'),\n",
              "  ('the', 'AT'),\n",
              "  ('election', 'NN'),\n",
              "  (',', ','),\n",
              "  ('``', '``'),\n",
              "  ('deserves', 'VBZ'),\n",
              "  ('the', 'AT'),\n",
              "  ('praise', 'NN'),\n",
              "  ('and', 'CC'),\n",
              "  ('thanks', 'NNS'),\n",
              "  ('of', 'IN'),\n",
              "  ('the', 'AT'),\n",
              "  ('City', 'NN-TL'),\n",
              "  ('of', 'IN-TL'),\n",
              "  ('Atlanta', 'NP-TL'),\n",
              "  (\"''\", \"''\"),\n",
              "  ('for', 'IN'),\n",
              "  ('the', 'AT'),\n",
              "  ('manner', 'NN'),\n",
              "  ('in', 'IN'),\n",
              "  ('which', 'WDT'),\n",
              "  ('the', 'AT'),\n",
              "  ('election', 'NN'),\n",
              "  ('was', 'BEDZ'),\n",
              "  ('conducted', 'VBN'),\n",
              "  ('.', '.')]]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Penn Treebank Corpus"
      ],
      "metadata": {
        "id": "f87x_HaCAc5o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "References:\n",
        "\n",
        "Paper with Penn Treebank description: https://www.researchgate.net/publication/2873803_The_Penn_Treebank_An_overview\n",
        "\n",
        "List of explanations for tag acronyms: https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html\n"
      ],
      "metadata": {
        "id": "NUHBOwuFI1-k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('treebank')\n",
        "len(list(nltk.corpus.treebank.tagged_words()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XzKpPn3b-t59",
        "outputId": "db03457b-86a5-4830-ab9e-26fd67b9672c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package treebank to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/treebank.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100676"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.corpus.treebank.tagged_words()[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8jj6m7U-Ag5v",
        "outputId": "b21ad889-3182-4a7b-ac8f-32e41cd2ae64"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Pierre', 'NNP'),\n",
              " ('Vinken', 'NNP'),\n",
              " (',', ','),\n",
              " ('61', 'CD'),\n",
              " ('years', 'NNS'),\n",
              " ('old', 'JJ'),\n",
              " (',', ','),\n",
              " ('will', 'MD'),\n",
              " ('join', 'VB'),\n",
              " ('the', 'DT')]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Other tools, POS Tagging performance & comparison: https://aclweb.org/aclwiki/index.php?title=POS_Tagging_(State_of_the_art)"
      ],
      "metadata": {
        "id": "FFZ0yM74MT8e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Parsing with given grammar"
      ],
      "metadata": {
        "id": "0NMLbkBIqrU8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://www.nltk.org/book/ch08.html"
      ],
      "metadata": {
        "id": "UHEZ0ie6t9Nq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "gram = nltk.CFG.fromstring(\"\"\"  \n",
        "S -> NP VP | TO VB\n",
        "VP -> V NP | V NP PP | V S | V PP\n",
        "PP -> P NP  \n",
        "V -> \"caught\" | \"ate\" | \"likes\" | \"like\" | \"chase\" | \"go\" | \"fly\" | \"flies\" | \"eat\" | \"saw\"\n",
        "NP -> Det N | Det N PP | PRP\n",
        "Det -> \"the\" | \"a\" | \"an\" | \"my\" | \"some\" | \"The\"\n",
        "N -> \"mice\" | \"cat\" | \"dog\" |  \"school\" | \"Time\" | \"arrow\" | \"fly\" | \"flies\" | \"candy\" | \"man\" | \"park\"\n",
        "P -> \"in\" | \"to\" | \"on\"\n",
        "TO -> \"to\"\n",
        "PRP -> \"I\"  \"\"\")\n"
      ],
      "metadata": {
        "id": "RISdpyTPqv-3"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "gram = nltk.CFG.fromstring(\"\"\"  \n",
        "S -> NP VP | TO VB\n",
        "VP -> V NP | V NP PP| V S | V PP\n",
        "PP -> P NP  \n",
        "V -> \"caught\" | \"ate\" | \"likes\" | \"like\" | \"chase\" | \"go\" | \"fly\" | \"flies\" | \"eat\" | \"saw\"\n",
        "NP -> Det N | Det N PP | PRP\n",
        "Det -> \"the\" | \"a\" | \"an\" | \"my\" | \"some\" | \"The\"\n",
        "N -> \"mice\" | \"cat\" | \"dog\" |  \"school\" | \"Time\" | \"arrow\" | \"fly\" | \"flies\" | \"candy\" | \"man\" | \"park\"\n",
        "P -> \"in\" | \"to\" | \"on\"\n",
        "TO -> \"to\"\n",
        "PRP -> \"I\"  \"\"\")\n"
      ],
      "metadata": {
        "id": "DJPT-XHtlLJl"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rdp = nltk.RecursiveDescentParser(gram)\n",
        "rdp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S--OvWijq10q",
        "outputId": "95ce2865-53d3-43b7-e5e6-283a00580b76"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<nltk.parse.recursivedescent.RecursiveDescentParser at 0x7f4c5dc24c10>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"I eat some candy\"\n",
        "for tree in rdp.parse(nltk.word_tokenize(text)):\n",
        "    print(tree)"
      ],
      "metadata": {
        "id": "Iu5HKnFhsyFG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c44ece02-c7d9-46bf-f6b5-17f1afa48910"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(S (NP (PRP I)) (VP (V eat) (NP (Det some) (N candy))))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Syntactic ambiguity:"
      ],
      "metadata": {
        "id": "ZKwCg4BgtCS9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"The dog saw a man in the park\"\n",
        "for tree in rdp.parse(nltk.word_tokenize(text)):\n",
        "    print(tree)"
      ],
      "metadata": {
        "id": "-I8fOSo-szj_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac153e16-7a1d-43f4-cfb7-cdfcda44f94b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(S\n",
            "  (NP (Det The) (N dog))\n",
            "  (VP\n",
            "    (V saw)\n",
            "    (NP (Det a) (N man) (PP (P in) (NP (Det the) (N park))))))\n",
            "(S\n",
            "  (NP (Det The) (N dog))\n",
            "  (VP\n",
            "    (V saw)\n",
            "    (NP (Det a) (N man))\n",
            "    (PP (P in) (NP (Det the) (N park)))))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.draw.tree import draw_trees"
      ],
      "metadata": {
        "id": "kq1zcNBwKhib"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for tree in rdp.parse(nltk.word_tokenize(text)):\n",
        "    draw_trees(tree)  # run locally"
      ],
      "metadata": {
        "id": "tu-5jlrE1dLz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "srp = nltk.ShiftReduceParser(gram, trace=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8Djg8VwvSkI",
        "outputId": "72316f91-eac0-4a88-c106-8203d7d5a5c2"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: V -> 'fly' will never be used\n",
            "Warning: V -> 'flies' will never be used\n",
            "Warning: P -> 'to' will never be used\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"I like my candy\"\n",
        "for tree in srp.parse(nltk.word_tokenize(text)):\n",
        "    print(tree)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8YE9mlUvfxw",
        "outputId": "ded24f5b-389c-42d4-9eee-19e4d1947796"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parsing 'I like my candy'\n",
            "    [ * I like my candy]\n",
            "  S [ 'I' * like my candy]\n",
            "  R [ PRP * like my candy]\n",
            "  R [ NP * like my candy]\n",
            "  S [ NP 'like' * my candy]\n",
            "  R [ NP V * my candy]\n",
            "  S [ NP V 'my' * candy]\n",
            "  R [ NP V Det * candy]\n",
            "  S [ NP V Det 'candy' * ]\n",
            "  R [ NP V Det N * ]\n",
            "  R [ NP V NP * ]\n",
            "  R [ NP VP * ]\n",
            "  R [ S * ]\n",
            "(S (NP (PRP I)) (VP (V like) (NP (Det my) (N candy))))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "groucho_grammar = nltk.CFG.fromstring(\"\"\"\n",
        " S -> NP VP\n",
        " PP -> P NP\n",
        " NP -> Det N | Det N PP | 'I'\n",
        " VP -> V NP | VP PP\n",
        " Det -> 'an' | 'my'\n",
        " N -> 'elephant' | 'pajamas'\n",
        " V -> 'shot'\n",
        " P -> 'in'\n",
        " \"\"\")\n"
      ],
      "metadata": {
        "id": "BFfd-eRQy5Pl"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sent = ['I', 'shot', 'an', 'elephant', 'in', 'my', 'pajamas']\n",
        "parser = nltk.ChartParser(groucho_grammar)\n",
        "for tree in parser.parse(sent):\n",
        "  print(tree)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ggZ4VB4y-JL",
        "outputId": "48cd4e06-a42b-45c7-aba5-133827ab790b"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(S\n",
            "  (NP I)\n",
            "  (VP\n",
            "    (VP (V shot) (NP (Det an) (N elephant)))\n",
            "    (PP (P in) (NP (Det my) (N pajamas)))))\n",
            "(S\n",
            "  (NP I)\n",
            "  (VP\n",
            "    (V shot)\n",
            "    (NP (Det an) (N elephant) (PP (P in) (NP (Det my) (N pajamas))))))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sent = ['I', 'shot', 'an', 'elephant', 'in', 'my', 'pajamas']\n",
        "parser = nltk.ShiftReduceParser(groucho_grammar)\n",
        "for tree in parser.parse(sent):\n",
        "  print(tree)"
      ],
      "metadata": {
        "id": "zeVp4k5volVC"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercitii (1p total)"
      ],
      "metadata": {
        "id": "H_1O3gu1A-Ax"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Completati functia scrisa in laboratorul anterior cu POS-tagging: in final veti avea o functie care sa primeasca un text si sa faca toata preprocesarea (tokenizare, lematizare, normalizare) si pos-tagging, si sa intoarca textul cu tags pe cuvinte.\n",
        "\n",
        "/\n",
        "\n",
        "Add to the function implemented for the past lab instructions for POS-tagging: in the end you should have a function which receives an input text and performs preprocessing from beginning to end (tokenization, lemmatization, normalization) as well as POS-tagging, then returns the tagged text. "
      ],
      "metadata": {
        "id": "wB4FM4LABFAA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "import unicodedata\n",
        "from collections import Counter\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.stem import PorterStemmer\n",
        "\n",
        "import seaborn as sns\n",
        "from matplotlib import colors\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('universal_tagset')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RmqXMzLLC2-L",
        "outputId": "fd960198-3aea-48db-c5bc-d1a80b6f9737"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package universal_tagset to /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/universal_tagset.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stemmer = PorterStemmer()\n",
        "lemmatizaer = WordNetLemmatizer()"
      ],
      "metadata": {
        "id": "ng7KLwL7FeuC"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentence = \"This sentence is used for testing different preprocessing steps in NLP!!!\""
      ],
      "metadata": {
        "id": "iu7GLl0UBidf"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess(text, remove_punctuation=False, remove_stopwords=False, stemming=False, lemmatization=True):\n",
        "  preprocessed_text = text\n",
        "  # lowercasing\n",
        "  preprocessed_text = preprocessed_text.lower()\n",
        "  # normalize text\n",
        "  preprocessed_text = unicodedata.normalize('NFKD', preprocessed_text).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n",
        "  # tokenize words\n",
        "  tokens = nltk.word_tokenize(preprocessed_text)\n",
        "  # punctuation mark removal\n",
        "  if remove_punctuation:\n",
        "    tokens = [token for token in tokens if token.isalnum()]\n",
        "  # stopwords removal\n",
        "  if remove_stopwords:\n",
        "    tokens = [token for token in tokens if token not in stopwords.words(\"english\")]\n",
        "  # stemming (normalizing technique)\n",
        "  if stemming:\n",
        "    tokens = [stemmer.stem(token) for token in tokens]\n",
        "  # lemmatization - reducing words to their root\n",
        "  if lemmatization:\n",
        "    tokens = [lemmatizaer.lemmatize(token) for token in tokens]\n",
        "\n",
        "  pos_tags = nltk.pos_tag(tokens, tagset='universal')\n",
        "\n",
        "  return pos_tags"
      ],
      "metadata": {
        "id": "G27nCM7ZBVlS"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Preprocessed text: \\n\", preprocess(sentence))\n",
        "print(\"Original text: \\n\", sentence)"
      ],
      "metadata": {
        "id": "mkCPvJ4XCCU6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "696570d5-ed56-4ae1-f60a-89278b006151"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Preprocessed text: \n",
            " [('this', 'DET'), ('sentence', 'NOUN'), ('is', 'VERB'), ('used', 'VERB'), ('for', 'ADP'), ('testing', 'VERB'), ('different', 'ADJ'), ('preprocessing', 'VERB'), ('step', 'NOUN'), ('in', 'ADP'), ('nlp', 'NOUN'), ('!', '.'), ('!', '.'), ('!', '.')]\n",
            "Original text: \n",
            " This sentence is used for testing different preprocessing steps in NLP!!!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Aplicati functia pe un fragment de cateva sute de cuvinte din stiri din ultimele cateva zile (aveti grija sa fie intr-o limba pe care functia o suporta). Afisati distributia partilor de vorbire intr-un grafic.\n",
        "\n",
        "/\n",
        "\n",
        "Execute your function on a piece of news from the past days of at least a few hundred words (make sure it's in a language that the function supports). Illustrate the distribution of POSs in a graph. "
      ],
      "metadata": {
        "id": "jb70NTXABV2a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "metadata": {
        "id": "G5skYL9zCXaC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27ad81ba-b52f-4d64-c0ce-be2fcd775ceb"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/MyDrive/test.txt') as f:\n",
        "    text = f.read()\n",
        "    print(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAd8Pz8LKEO4",
        "outputId": "83acf270-f4f0-4ac6-fe73-e7c951c572cc"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The COVID-19 pandemic, also known as the coronavirus pandemic, is an ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The novel virus was first identified from an outbreak in the Chinese city of Wuhan in December 2019, and attempts to contain it there failed, allowing it to spread across the globe. The World Health Organization (WHO) declared a Public Health Emergency of International Concern on 30 January 2020 and a pandemic on 11 March 2020. As of 23 March 2022, the pandemic had caused more than 474 million cases and 6.1 million deaths, making it one of the deadliest in history.\n",
            "\n",
            "COVID-19 symptoms range from undetectable to deadly, but most commonly include fever, dry cough, and fatigue. Severe illness is more likely in elderly patients and those with certain underlying medical conditions. COVID‑19 transmits when people breathe in air contaminated by droplets and small airborne particles containing the virus. The risk of breathing these in is highest when people are in close proximity, but they can be inhaled over longer distances, particularly indoors. Transmission can also occur if contaminated fluids reach the eyes, nose or mouth, and, rarely, via contaminated surfaces. Infected persons are typically contagious for 10 days, and can spread the virus even if they do not develop symptoms. Mutations have produced many strains (variants) with varying degrees of infectivity and virulence.[5][6]\n",
            "\n",
            "COVID-19 vaccines have been approved and widely distributed in various countries since December 2020. Other recommended preventive measures include social distancing, masking, improving ventilation and air filtration, and quarantining those who have been exposed or are symptomatic. Treatments include monoclonal antibodies,[7] novel antiviral drugs, and symptom control. Governmental interventions include travel restrictions, lockdowns, business restrictions and closures, workplace hazard controls, quarantines, testing systems, and tracing contacts of the infected.\n",
            "\n",
            "The pandemic triggered severe social and economic disruption around the world, including the largest global recession since the Great Depression.[8] Widespread supply shortages, including food shortages, were caused by supply chain disruption and panic buying. The resultant near-global lockdowns saw an unprecedented pollution decrease. Educational institutions and public areas were partially or fully closed in many jurisdictions, and many events were cancelled or postponed. Misinformation circulated through social media and mass media and political tensions intensified. The pandemic raised issues of racial and geographic discrimination, health equity, and the balance between public health imperatives and individual rights.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_preprocessed = preprocess(text)\n",
        "print('Preprocessing step + POS-tagging: \\n', text_preprocessed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VhAZovd-Kmho",
        "outputId": "51d039fa-9ac0-4229-d70b-3edcf72d7d78"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Preprocessing step + POS-tagging: \n",
            " [('the', 'DET'), ('covid-19', 'ADJ'), ('pandemic', 'NOUN'), (',', '.'), ('also', 'ADV'), ('known', 'VERB'), ('a', 'DET'), ('the', 'DET'), ('coronavirus', 'NOUN'), ('pandemic', 'NOUN'), (',', '.'), ('is', 'VERB'), ('an', 'DET'), ('ongoing', 'ADJ'), ('global', 'ADJ'), ('pandemic', 'NOUN'), ('of', 'ADP'), ('coronavirus', 'NOUN'), ('disease', 'NOUN'), ('2019', 'NUM'), ('(', '.'), ('covid-19', 'NOUN'), (')', '.'), ('caused', 'VERB'), ('by', 'ADP'), ('severe', 'ADJ'), ('acute', 'ADJ'), ('respiratory', 'NOUN'), ('syndrome', 'NOUN'), ('coronavirus', 'NOUN'), ('2', 'NUM'), ('(', '.'), ('sars-cov-2', 'NOUN'), (')', '.'), ('.', '.'), ('the', 'DET'), ('novel', 'ADJ'), ('virus', 'NOUN'), ('wa', 'NOUN'), ('first', 'ADV'), ('identified', 'VERB'), ('from', 'ADP'), ('an', 'DET'), ('outbreak', 'NOUN'), ('in', 'ADP'), ('the', 'DET'), ('chinese', 'ADJ'), ('city', 'NOUN'), ('of', 'ADP'), ('wuhan', 'NOUN'), ('in', 'ADP'), ('december', 'NOUN'), ('2019', 'NUM'), (',', '.'), ('and', 'CONJ'), ('attempt', 'NOUN'), ('to', 'PRT'), ('contain', 'VERB'), ('it', 'PRON'), ('there', 'ADV'), ('failed', 'VERB'), (',', '.'), ('allowing', 'VERB'), ('it', 'PRON'), ('to', 'PRT'), ('spread', 'VERB'), ('across', 'ADP'), ('the', 'DET'), ('globe', 'NOUN'), ('.', '.'), ('the', 'DET'), ('world', 'NOUN'), ('health', 'NOUN'), ('organization', 'NOUN'), ('(', '.'), ('who', 'PRON'), (')', '.'), ('declared', 'VERB'), ('a', 'DET'), ('public', 'ADJ'), ('health', 'NOUN'), ('emergency', 'NOUN'), ('of', 'ADP'), ('international', 'ADJ'), ('concern', 'NOUN'), ('on', 'ADP'), ('30', 'NUM'), ('january', 'ADJ'), ('2020', 'NUM'), ('and', 'CONJ'), ('a', 'DET'), ('pandemic', 'NOUN'), ('on', 'ADP'), ('11', 'NUM'), ('march', 'NOUN'), ('2020.', 'NUM'), ('a', 'DET'), ('of', 'ADP'), ('23', 'NUM'), ('march', 'NOUN'), ('2022', 'NUM'), (',', '.'), ('the', 'DET'), ('pandemic', 'NOUN'), ('had', 'VERB'), ('caused', 'VERB'), ('more', 'ADJ'), ('than', 'ADP'), ('474', 'NUM'), ('million', 'NUM'), ('case', 'NOUN'), ('and', 'CONJ'), ('6.1', 'NUM'), ('million', 'NUM'), ('death', 'NOUN'), (',', '.'), ('making', 'VERB'), ('it', 'PRON'), ('one', 'NUM'), ('of', 'ADP'), ('the', 'DET'), ('deadliest', 'ADJ'), ('in', 'ADP'), ('history', 'NOUN'), ('.', '.'), ('covid-19', 'ADJ'), ('symptom', 'ADJ'), ('range', 'NOUN'), ('from', 'ADP'), ('undetectable', 'ADJ'), ('to', 'PRT'), ('deadly', 'ADV'), (',', '.'), ('but', 'CONJ'), ('most', 'ADJ'), ('commonly', 'ADV'), ('include', 'VERB'), ('fever', 'ADV'), (',', '.'), ('dry', 'ADJ'), ('cough', 'NOUN'), (',', '.'), ('and', 'CONJ'), ('fatigue', 'NOUN'), ('.', '.'), ('severe', 'ADJ'), ('illness', 'NOUN'), ('is', 'VERB'), ('more', 'ADV'), ('likely', 'ADJ'), ('in', 'ADP'), ('elderly', 'ADJ'), ('patient', 'NOUN'), ('and', 'CONJ'), ('those', 'DET'), ('with', 'ADP'), ('certain', 'ADJ'), ('underlying', 'ADJ'), ('medical', 'ADJ'), ('condition', 'NOUN'), ('.', '.'), ('covid19', 'NOUN'), ('transmits', 'NOUN'), ('when', 'ADV'), ('people', 'NOUN'), ('breathe', 'VERB'), ('in', 'ADP'), ('air', 'NOUN'), ('contaminated', 'VERB'), ('by', 'ADP'), ('droplet', 'NOUN'), ('and', 'CONJ'), ('small', 'ADJ'), ('airborne', 'ADJ'), ('particle', 'NOUN'), ('containing', 'VERB'), ('the', 'DET'), ('virus', 'NOUN'), ('.', '.'), ('the', 'DET'), ('risk', 'NOUN'), ('of', 'ADP'), ('breathing', 'VERB'), ('these', 'DET'), ('in', 'ADP'), ('is', 'VERB'), ('highest', 'ADJ'), ('when', 'ADV'), ('people', 'NOUN'), ('are', 'VERB'), ('in', 'ADP'), ('close', 'ADJ'), ('proximity', 'NOUN'), (',', '.'), ('but', 'CONJ'), ('they', 'PRON'), ('can', 'VERB'), ('be', 'VERB'), ('inhaled', 'VERB'), ('over', 'ADP'), ('longer', 'ADJ'), ('distance', 'NOUN'), (',', '.'), ('particularly', 'ADV'), ('indoors', 'NOUN'), ('.', '.'), ('transmission', 'NOUN'), ('can', 'VERB'), ('also', 'ADV'), ('occur', 'VERB'), ('if', 'ADP'), ('contaminated', 'VERB'), ('fluid', 'NOUN'), ('reach', 'VERB'), ('the', 'DET'), ('eye', 'NOUN'), (',', '.'), ('nose', 'ADJ'), ('or', 'CONJ'), ('mouth', 'NOUN'), (',', '.'), ('and', 'CONJ'), (',', '.'), ('rarely', 'ADV'), (',', '.'), ('via', 'ADP'), ('contaminated', 'VERB'), ('surface', 'NOUN'), ('.', '.'), ('infected', 'VERB'), ('person', 'NOUN'), ('are', 'VERB'), ('typically', 'ADV'), ('contagious', 'ADJ'), ('for', 'ADP'), ('10', 'NUM'), ('day', 'NOUN'), (',', '.'), ('and', 'CONJ'), ('can', 'VERB'), ('spread', 'VERB'), ('the', 'DET'), ('virus', 'NOUN'), ('even', 'ADV'), ('if', 'ADP'), ('they', 'PRON'), ('do', 'VERB'), ('not', 'ADV'), ('develop', 'VERB'), ('symptom', 'NOUN'), ('.', '.'), ('mutation', 'NOUN'), ('have', 'VERB'), ('produced', 'VERB'), ('many', 'ADJ'), ('strain', 'NOUN'), ('(', '.'), ('variant', 'ADJ'), (')', '.'), ('with', 'ADP'), ('varying', 'VERB'), ('degree', 'NOUN'), ('of', 'ADP'), ('infectivity', 'NOUN'), ('and', 'CONJ'), ('virulence', 'NOUN'), ('.', '.'), ('[', 'CONJ'), ('5', 'NUM'), (']', 'ADJ'), ('[', '.'), ('6', 'NUM'), (']', 'ADJ'), ('covid-19', 'ADJ'), ('vaccine', 'NOUN'), ('have', 'VERB'), ('been', 'VERB'), ('approved', 'VERB'), ('and', 'CONJ'), ('widely', 'ADV'), ('distributed', 'VERB'), ('in', 'ADP'), ('various', 'ADJ'), ('country', 'NOUN'), ('since', 'ADP'), ('december', '.'), ('2020.', 'NUM'), ('other', 'ADJ'), ('recommended', 'VERB'), ('preventive', 'ADJ'), ('measure', 'NOUN'), ('include', 'VERB'), ('social', 'ADJ'), ('distancing', 'NOUN'), (',', '.'), ('masking', 'NOUN'), (',', '.'), ('improving', 'VERB'), ('ventilation', 'NOUN'), ('and', 'CONJ'), ('air', 'NOUN'), ('filtration', 'NOUN'), (',', '.'), ('and', 'CONJ'), ('quarantining', 'VERB'), ('those', 'DET'), ('who', 'PRON'), ('have', 'VERB'), ('been', 'VERB'), ('exposed', 'VERB'), ('or', 'CONJ'), ('are', 'VERB'), ('symptomatic', 'ADJ'), ('.', '.'), ('treatment', 'NOUN'), ('include', 'VERB'), ('monoclonal', 'ADJ'), ('antibody', 'NOUN'), (',', '.'), ('[', 'VERB'), ('7', 'NUM'), (']', 'NOUN'), ('novel', 'ADJ'), ('antiviral', 'ADJ'), ('drug', 'NOUN'), (',', '.'), ('and', 'CONJ'), ('symptom', 'VERB'), ('control', 'NOUN'), ('.', '.'), ('governmental', 'ADJ'), ('intervention', 'NOUN'), ('include', 'VERB'), ('travel', 'ADJ'), ('restriction', 'NOUN'), (',', '.'), ('lockdown', 'ADJ'), (',', '.'), ('business', 'NOUN'), ('restriction', 'NOUN'), ('and', 'CONJ'), ('closure', 'NOUN'), (',', '.'), ('workplace', 'ADJ'), ('hazard', 'NOUN'), ('control', 'NOUN'), (',', '.'), ('quarantine', 'NOUN'), (',', '.'), ('testing', 'VERB'), ('system', 'NOUN'), (',', '.'), ('and', 'CONJ'), ('tracing', 'VERB'), ('contact', 'NOUN'), ('of', 'ADP'), ('the', 'DET'), ('infected', 'VERB'), ('.', '.'), ('the', 'DET'), ('pandemic', 'ADJ'), ('triggered', 'VERB'), ('severe', 'ADJ'), ('social', 'ADJ'), ('and', 'CONJ'), ('economic', 'ADJ'), ('disruption', 'NOUN'), ('around', 'ADP'), ('the', 'DET'), ('world', 'NOUN'), (',', '.'), ('including', 'VERB'), ('the', 'DET'), ('largest', 'ADJ'), ('global', 'ADJ'), ('recession', 'NOUN'), ('since', 'ADP'), ('the', 'DET'), ('great', 'ADJ'), ('depression', 'NOUN'), ('.', '.'), ('[', 'CONJ'), ('8', 'NUM'), (']', 'ADJ'), ('widespread', 'ADJ'), ('supply', 'NOUN'), ('shortage', 'NOUN'), (',', '.'), ('including', 'VERB'), ('food', 'NOUN'), ('shortage', 'NOUN'), (',', '.'), ('were', 'VERB'), ('caused', 'VERB'), ('by', 'ADP'), ('supply', 'NOUN'), ('chain', 'NOUN'), ('disruption', 'NOUN'), ('and', 'CONJ'), ('panic', 'ADJ'), ('buying', 'NOUN'), ('.', '.'), ('the', 'DET'), ('resultant', 'ADJ'), ('near-global', 'ADJ'), ('lockdown', 'NOUN'), ('saw', 'VERB'), ('an', 'DET'), ('unprecedented', 'ADJ'), ('pollution', 'NOUN'), ('decrease', 'NOUN'), ('.', '.'), ('educational', 'ADJ'), ('institution', 'NOUN'), ('and', 'CONJ'), ('public', 'ADJ'), ('area', 'NOUN'), ('were', 'VERB'), ('partially', 'ADV'), ('or', 'CONJ'), ('fully', 'ADV'), ('closed', 'VERB'), ('in', 'ADP'), ('many', 'ADJ'), ('jurisdiction', 'NOUN'), (',', '.'), ('and', 'CONJ'), ('many', 'ADJ'), ('event', 'NOUN'), ('were', 'VERB'), ('cancelled', 'VERB'), ('or', 'CONJ'), ('postponed', 'VERB'), ('.', '.'), ('misinformation', 'NOUN'), ('circulated', 'VERB'), ('through', 'ADP'), ('social', 'ADJ'), ('medium', 'NOUN'), ('and', 'CONJ'), ('mass', 'NOUN'), ('medium', 'NOUN'), ('and', 'CONJ'), ('political', 'ADJ'), ('tension', 'NOUN'), ('intensified', 'VERB'), ('.', '.'), ('the', 'DET'), ('pandemic', 'ADJ'), ('raised', 'ADJ'), ('issue', 'NOUN'), ('of', 'ADP'), ('racial', 'ADJ'), ('and', 'CONJ'), ('geographic', 'ADJ'), ('discrimination', 'NOUN'), (',', '.'), ('health', 'NOUN'), ('equity', 'NOUN'), (',', '.'), ('and', 'CONJ'), ('the', 'DET'), ('balance', 'NOUN'), ('between', 'ADP'), ('public', 'ADJ'), ('health', 'NOUN'), ('imperative', 'NOUN'), ('and', 'CONJ'), ('individual', 'ADJ'), ('right', 'NOUN'), ('.', '.')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_pos_tags(tagged_text):\n",
        "  pos_tags_count = Counter([j for _, j in tagged_text])\n",
        "  return pos_tags_count\n",
        "\n",
        "count_pos_tags = get_pos_tags(text_preprocessed)\n",
        "print(count_pos_tags)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJCeUdenLiOH",
        "outputId": "d88b0c3c-0416-404c-96fe-e981e289ca9f"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({'NOUN': 119, 'ADJ': 72, 'VERB': 69, '.': 62, 'ADP': 39, 'CONJ': 32, 'DET': 30, 'NUM': 20, 'ADV': 18, 'PRON': 7, 'PRT': 3})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pos_tags_labels = {\n",
        "    'ADJ': 'Adjective',\n",
        "    'ADP': 'Adposition',\n",
        "    'ADV': 'Adverb',\n",
        "    'CONJ': 'Conjuction',\n",
        "    'DET': 'Determiner/Article',\n",
        "    'NOUN': 'Noun',\n",
        "    'NUM': 'Numeral',\n",
        "    'PRT': 'Particle',\n",
        "    'PRON': 'Pronoun',\n",
        "    'VERB': 'Verb',\n",
        "    '.': 'Punctuation mark',\n",
        "    'X': 'Other'\n",
        "}"
      ],
      "metadata": {
        "id": "VDlO9MY1MKGP"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=pd.DataFrame(pd.Series(count_pos_tags)).T)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "7YoDorL-M3x3",
        "outputId": "7bb34041-1916-4881-b482-c83564737234"
      },
      "execution_count": 56,
      "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": [
        "3. Modificati functia de mai sus astfel incat sa efectueze POS-tagging inainte sau dupa lematizare. Comparati diferentele. Afisati cuvintele (top 20) pentru care POS tag-ul identificat difera cel mai des intre cele doua versiuni. \n",
        "\n",
        "/\n",
        "\n",
        "Modify your function such that POS-tagging is performed before or after lemmatization. Print the words in the vocabulary (top 20) for which the identified POS tag differs most often between the two versions."
      ],
      "metadata": {
        "id": "sxDdel9eCYan"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Firstly, we create a POS-TAG mapper for the StanfordPOSTagger."
      ],
      "metadata": {
        "id": "-OFGNb7IShk8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tagger=StanfordPOSTagger(model_path, jar_tagger_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pc29Mo_TSg_z",
        "outputId": "937957c8-cea8-40e9-c0ee-025ba432a274"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/nltk/tag/stanford.py:149: DeprecationWarning: \n",
            "The StanfordTokenizer will be deprecated in version 3.2.5.\n",
            "Please use \u001b[91mnltk.tag.corenlp.CoreNLPPOSTagger\u001b[0m or \u001b[91mnltk.tag.corenlp.CoreNLPNERTagger\u001b[0m instead.\n",
            "  super(StanfordPOSTagger, self).__init__(*args, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.corpus.reader.wordnet import VERB, NOUN, ADJ, ADV\n",
        "\n",
        "pos_map = {\n",
        "    'NN': NOUN,\n",
        "    'VB': VERB,\n",
        "    'JJ': ADJ,\n",
        "    'RB': ADV,\n",
        "    'WR': ADV\n",
        "}"
      ],
      "metadata": {
        "id": "ZERClK4bS-JZ"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess2(text, remove_punctuation=False, remove_stopwords=False, stemming=False, lemmatization=True, pos_tag_first=True):\n",
        "  preprocessed_text = text\n",
        "  # lowercasing\n",
        "  preprocessed_text = preprocessed_text.lower()\n",
        "  # normalize text\n",
        "  preprocessed_text = unicodedata.normalize('NFKD', preprocessed_text).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n",
        "  # tokenize words\n",
        "  tokens = nltk.word_tokenize(preprocessed_text)\n",
        "  # punctuation mark removal\n",
        "  if remove_punctuation:\n",
        "    tokens = [token for token in tokens if token not in string.punctuation]\n",
        "  # stopwords removal\n",
        "  if remove_stopwords:\n",
        "    tokens = [token for token in tokens if token not in stopwords.words(\"english\")]\n",
        "  # stemming (normalizing technique)\n",
        "  if stemming:\n",
        "    tokens = [stemmer.stem(token) for token in tokens]\n",
        "  # lemmatization - reducing words to their root\n",
        "  if pos_tag_first:\n",
        "    pos_tags = tagger.tag(tokens)\n",
        "\n",
        "  if lemmatization:\n",
        "    if pos_tag_first:\n",
        "      lemmatized_tokens = []\n",
        "      for (token, tag) in pos_tags:\n",
        "        if tag[:2] in pos_map.keys():\n",
        "          lemmatized_tokens.append(lemmatizaer.lemmatize(token, pos=pos_map.get(tag[:2])))\n",
        "        else:\n",
        "          lemmatized_tokens.append(lemmatizaer.lemmatize(token))\n",
        "\n",
        "      return tagger.tag(lemmatized_tokens)\n",
        "\n",
        "    tokens = [lemmatizaer.lemmatize(token) for token in tokens]\n",
        "\n",
        "  pos_tags = tagger.tag(tokens)\n",
        "\n",
        "  return pos_tags"
      ],
      "metadata": {
        "id": "naOAjOjFb2Y4"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Lemmatization first:')\n",
        "text_preprocessed = preprocess2(text, remove_punctuation=True, pos_tag_first=False)\n",
        "print(text_preprocessed, '\\n')\n",
        "\n",
        "print('POS-TAGGING first:')\n",
        "text_preprocessed2 = preprocess2(text, remove_punctuation=True)\n",
        "print(text_preprocessed2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVcnPdveUMbh",
        "outputId": "75f6a2d7-d3db-4beb-b671-fcd5475f3851"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lemmatization first:\n",
            "[('the', 'DT'), ('covid-19', 'NN'), ('pandemic', 'NN'), ('also', 'RB'), ('known', 'VBN'), ('a', 'DT'), ('the', 'DT'), ('coronavirus', 'NN'), ('pandemic', 'NN'), ('is', 'VBZ'), ('an', 'DT'), ('ongoing', 'JJ'), ('global', 'JJ'), ('pandemic', 'NN'), ('of', 'IN'), ('coronavirus', 'NN'), ('disease', 'NN'), ('2019', 'CD'), ('covid-19', 'NN'), ('caused', 'VBN'), ('by', 'IN'), ('severe', 'JJ'), ('acute', 'JJ'), ('respiratory', 'JJ'), ('syndrome', 'NN'), ('coronavirus', 'NN'), ('2', 'CD'), ('sars-cov-2', 'NN'), ('the', 'DT'), ('novel', 'JJ'), ('virus', 'NN'), ('wa', 'NN'), ('first', 'RB'), ('identified', 'VBN'), ('from', 'IN'), ('an', 'DT'), ('outbreak', 'NN'), ('in', 'IN'), ('the', 'DT'), ('chinese', 'JJ'), ('city', 'NN'), ('of', 'IN'), ('wuhan', 'NNP'), ('in', 'IN'), ('december', 'NNP'), ('2019', 'CD'), ('and', 'CC'), ('attempt', 'NN'), ('to', 'TO'), ('contain', 'VB'), ('it', 'PRP'), ('there', 'RB'), ('failed', 'VBD'), ('allowing', 'VBG'), ('it', 'PRP'), ('to', 'TO'), ('spread', 'VB'), ('across', 'IN'), ('the', 'DT'), ('globe', 'NN'), ('the', 'DT'), ('world', 'NN'), ('health', 'NN'), ('organization', 'NN'), ('who', 'WP'), ('declared', 'VBD'), ('a', 'DT'), ('public', 'JJ'), ('health', 'NN'), ('emergency', 'NN'), ('of', 'IN'), ('international', 'JJ'), ('concern', 'NN'), ('on', 'IN'), ('30', 'CD'), ('january', 'NNP'), ('2020', 'CD'), ('and', 'CC'), ('a', 'DT'), ('pandemic', 'NN'), ('on', 'IN'), ('11', 'CD'), ('march', 'NN'), ('2020.', 'NN'), ('a', 'DT'), ('of', 'IN'), ('23', 'CD'), ('march', 'NN'), ('2022', 'CD'), ('the', 'DT'), ('pandemic', 'NN'), ('had', 'VBD'), ('caused', 'VBN'), ('more', 'JJR'), ('than', 'IN'), ('474', 'CD'), ('million', 'CD'), ('case', 'NN'), ('and', 'CC'), ('6.1', 'CD'), ('million', 'CD'), ('death', 'NN'), ('making', 'VBG'), ('it', 'PRP'), ('one', 'CD'), ('of', 'IN'), ('the', 'DT'), ('deadliest', 'JJS'), ('in', 'IN'), ('history', 'NN'), ('covid-19', 'CD'), ('symptom', 'NN'), ('range', 'NN'), ('from', 'IN'), ('undetectable', 'JJ'), ('to', 'IN'), ('deadly', 'JJ'), ('but', 'CC'), ('most', 'RBS'), ('commonly', 'RB'), ('include', 'VBP'), ('fever', 'NN'), ('dry', 'JJ'), ('cough', 'NN'), ('and', 'CC'), ('fatigue', 'NN'), ('severe', 'JJ'), ('illness', 'NN'), ('is', 'VBZ'), ('more', 'RBR'), ('likely', 'JJ'), ('in', 'IN'), ('elderly', 'JJ'), ('patient', 'NN'), ('and', 'CC'), ('those', 'DT'), ('with', 'IN'), ('certain', 'JJ'), ('underlying', 'VBG'), ('medical', 'JJ'), ('condition', 'NN'), ('covid19', 'NN'), ('transmits', 'VBZ'), ('when', 'WRB'), ('people', 'NNS'), ('breathe', 'VBP'), ('in', 'IN'), ('air', 'NN'), ('contaminated', 'VBN'), ('by', 'IN'), ('droplet', 'NN'), ('and', 'CC'), ('small', 'JJ'), ('airborne', 'JJ'), ('particle', 'NN'), ('containing', 'VBG'), ('the', 'DT'), ('virus', 'NN'), ('the', 'DT'), ('risk', 'NN'), ('of', 'IN'), ('breathing', 'VBG'), ('these', 'DT'), ('in', 'IN'), ('is', 'VBZ'), ('highest', 'JJS'), ('when', 'WRB'), ('people', 'NNS'), ('are', 'VBP'), ('in', 'IN'), ('close', 'JJ'), ('proximity', 'NN'), ('but', 'CC'), ('they', 'PRP'), ('can', 'MD'), ('be', 'VB'), ('inhaled', 'VBN'), ('over', 'IN'), ('longer', 'JJR'), ('distance', 'NN'), ('particularly', 'RB'), ('indoors', 'RB'), ('transmission', 'NN'), ('can', 'MD'), ('also', 'RB'), ('occur', 'VB'), ('if', 'IN'), ('contaminated', 'JJ'), ('fluid', 'NN'), ('reach', 'VB'), ('the', 'DT'), ('eye', 'NN'), ('nose', 'NN'), ('or', 'CC'), ('mouth', 'NN'), ('and', 'CC'), ('rarely', 'RB'), ('via', 'IN'), ('contaminated', 'JJ'), ('surface', 'NN'), ('infected', 'VBN'), ('person', 'NN'), ('are', 'VBP'), ('typically', 'RB'), ('contagious', 'JJ'), ('for', 'IN'), ('10', 'CD'), ('day', 'NN'), ('and', 'CC'), ('can', 'MD'), ('spread', 'VB'), ('the', 'DT'), ('virus', 'NN'), ('even', 'RB'), ('if', 'IN'), ('they', 'PRP'), ('do', 'VBP'), ('not', 'RB'), ('develop', 'VB'), ('symptom', 'NN'), ('mutation', 'NN'), ('have', 'VBP'), ('produced', 'VBN'), ('many', 'JJ'), ('strain', 'NN'), ('variant', 'NN'), ('with', 'IN'), ('varying', 'VBG'), ('degree', 'NN'), ('of', 'IN'), ('infectivity', 'NN'), ('and', 'CC'), ('virulence', 'NN'), ('5', 'CD'), ('6', 'CD'), ('covid-19', 'NN'), ('vaccine', 'NN'), ('have', 'VBP'), ('been', 'VBN'), ('approved', 'VBN'), ('and', 'CC'), ('widely', 'RB'), ('distributed', 'VBN'), ('in', 'IN'), ('various', 'JJ'), ('country', 'NN'), ('since', 'IN'), ('december', 'NNP'), ('2020.', 'CD'), ('other', 'JJ'), ('recommended', 'JJ'), ('preventive', 'JJ'), ('measure', 'NN'), ('include', 'VBP'), ('social', 'JJ'), ('distancing', 'NN'), ('masking', 'NN'), ('improving', 'VBG'), ('ventilation', 'NN'), ('and', 'CC'), ('air', 'NN'), ('filtration', 'NN'), ('and', 'CC'), ('quarantining', 'VBG'), ('those', 'DT'), ('who', 'WP'), ('have', 'VBP'), ('been', 'VBN'), ('exposed', 'VBN'), ('or', 'CC'), ('are', 'VBP'), ('symptomatic', 'JJ'), ('treatment', 'NN'), ('include', 'VBP'), ('monoclonal', 'JJ'), ('antibody', 'NN'), ('7', 'CD'), ('novel', 'JJ'), ('antiviral', 'JJ'), ('drug', 'NN'), ('and', 'CC'), ('symptom', 'NN'), ('control', 'NN'), ('governmental', 'JJ'), ('intervention', 'NN'), ('include', 'VBP'), ('travel', 'NN'), ('restriction', 'NN'), ('lockdown', 'NN'), ('business', 'NN'), ('restriction', 'NN'), ('and', 'CC'), ('closure', 'NN'), ('workplace', 'NN'), ('hazard', 'NN'), ('control', 'NN'), ('quarantine', 'NN'), ('testing', 'NN'), ('system', 'NN'), ('and', 'CC'), ('tracing', 'NN'), ('contact', 'NN'), ('of', 'IN'), ('the', 'DT'), ('infected', 'VBN'), ('the', 'DT'), ('pandemic', 'NN'), ('triggered', 'VBD'), ('severe', 'JJ'), ('social', 'JJ'), ('and', 'CC'), ('economic', 'JJ'), ('disruption', 'NN'), ('around', 'IN'), ('the', 'DT'), ('world', 'NN'), ('including', 'VBG'), ('the', 'DT'), ('largest', 'JJS'), ('global', 'JJ'), ('recession', 'NN'), ('since', 'IN'), ('the', 'DT'), ('great', 'JJ'), ('depression', 'NN'), ('8', 'CD'), ('widespread', 'JJ'), ('supply', 'NN'), ('shortage', 'NN'), ('including', 'VBG'), ('food', 'NN'), ('shortage', 'NN'), ('were', 'VBD'), ('caused', 'VBN'), ('by', 'IN'), ('supply', 'NN'), ('chain', 'NN'), ('disruption', 'NN'), ('and', 'CC'), ('panic', 'NN'), ('buying', 'VBG'), ('the', 'DT'), ('resultant', 'JJ'), ('near-global', 'JJ'), ('lockdown', 'NN'), ('saw', 'VBD'), ('an', 'DT'), ('unprecedented', 'JJ'), ('pollution', 'NN'), ('decrease', 'NN'), ('educational', 'JJ'), ('institution', 'NN'), ('and', 'CC'), ('public', 'JJ'), ('area', 'NN'), ('were', 'VBD'), ('partially', 'RB'), ('or', 'CC'), ('fully', 'RB'), ('closed', 'VBN'), ('in', 'IN'), ('many', 'JJ'), ('jurisdiction', 'NN'), ('and', 'CC'), ('many', 'JJ'), ('event', 'NN'), ('were', 'VBD'), ('cancelled', 'VBN'), ('or', 'CC'), ('postponed', 'VBN'), ('misinformation', 'NN'), ('circulated', 'VBN'), ('through', 'IN'), ('social', 'JJ'), ('medium', 'NN'), ('and', 'CC'), ('mass', 'NN'), ('medium', 'NN'), ('and', 'CC'), ('political', 'JJ'), ('tension', 'NN'), ('intensified', 'VBD'), ('the', 'DT'), ('pandemic', 'NN'), ('raised', 'VBD'), ('issue', 'NN'), ('of', 'IN'), ('racial', 'JJ'), ('and', 'CC'), ('geographic', 'JJ'), ('discrimination', 'NN'), ('health', 'NN'), ('equity', 'NN'), ('and', 'CC'), ('the', 'DT'), ('balance', 'NN'), ('between', 'IN'), ('public', 'JJ'), ('health', 'NN'), ('imperative', 'NN'), ('and', 'CC'), ('individual', 'JJ'), ('right', 'NN')] \n",
            "\n",
            "POS-TAGGING first:\n",
            "[('the', 'DT'), ('covid-19', 'NN'), ('pandemic', 'NN'), ('also', 'RB'), ('know', 'VBP'), ('a', 'DT'), ('the', 'DT'), ('coronavirus', 'NN'), ('pandemic', 'NN'), ('be', 'VB'), ('an', 'DT'), ('ongoing', 'JJ'), ('global', 'JJ'), ('pandemic', 'NN'), ('of', 'IN'), ('coronavirus', 'NN'), ('disease', 'NN'), ('2019', 'CD'), ('covid-19', 'NN'), ('cause', 'NN'), ('by', 'IN'), ('severe', 'JJ'), ('acute', 'JJ'), ('respiratory', 'JJ'), ('syndrome', 'NN'), ('coronavirus', 'NN'), ('2', 'CD'), ('sars-cov-2', 'NN'), ('the', 'DT'), ('novel', 'JJ'), ('virus', 'NN'), ('be', 'VB'), ('first', 'RB'), ('identify', 'VB'), ('from', 'IN'), ('an', 'DT'), ('outbreak', 'NN'), ('in', 'IN'), ('the', 'DT'), ('chinese', 'JJ'), ('city', 'NN'), ('of', 'IN'), ('wuhan', 'NNP'), ('in', 'IN'), ('december', 'NNP'), ('2019', 'CD'), ('and', 'CC'), ('attempt', 'NN'), ('to', 'TO'), ('contain', 'VB'), ('it', 'PRP'), ('there', 'RB'), ('fail', 'VBP'), ('allow', 'VB'), ('it', 'PRP'), ('to', 'TO'), ('spread', 'VB'), ('across', 'IN'), ('the', 'DT'), ('globe', 'NN'), ('the', 'DT'), ('world', 'NN'), ('health', 'NN'), ('organization', 'NN'), ('who', 'WP'), ('declare', 'VBP'), ('a', 'DT'), ('public', 'JJ'), ('health', 'NN'), ('emergency', 'NN'), ('of', 'IN'), ('international', 'JJ'), ('concern', 'NN'), ('on', 'IN'), ('30', 'CD'), ('january', 'NNP'), ('2020', 'CD'), ('and', 'CC'), ('a', 'DT'), ('pandemic', 'NN'), ('on', 'IN'), ('11', 'CD'), ('march', 'NN'), ('2020.', 'NN'), ('a', 'DT'), ('of', 'IN'), ('23', 'CD'), ('march', 'NN'), ('2022', 'CD'), ('the', 'DT'), ('pandemic', 'NN'), ('have', 'VBP'), ('cause', 'NN'), ('more', 'JJR'), ('than', 'IN'), ('474', 'CD'), ('million', 'CD'), ('case', 'NN'), ('and', 'CC'), ('6.1', 'CD'), ('million', 'CD'), ('death', 'NN'), ('make', 'VBP'), ('it', 'PRP'), ('one', 'CD'), ('of', 'IN'), ('the', 'DT'), ('deadly', 'JJ'), ('in', 'IN'), ('history', 'NN'), ('covid-19', 'CD'), ('symptom', 'NN'), ('range', 'NN'), ('from', 'IN'), ('undetectable', 'JJ'), ('to', 'IN'), ('deadly', 'JJ'), ('but', 'CC'), ('most', 'RBS'), ('commonly', 'RB'), ('include', 'VBP'), ('fever', 'NN'), ('dry', 'JJ'), ('cough', 'NN'), ('and', 'CC'), ('fatigue', 'NN'), ('severe', 'JJ'), ('illness', 'NN'), ('be', 'VB'), ('more', 'RBR'), ('likely', 'JJ'), ('in', 'IN'), ('elderly', 'JJ'), ('patient', 'NN'), ('and', 'CC'), ('those', 'DT'), ('with', 'IN'), ('certain', 'JJ'), ('underlie', 'VBP'), ('medical', 'JJ'), ('condition', 'NN'), ('covid19', 'NN'), ('transmit', 'VB'), ('when', 'WRB'), ('people', 'NNS'), ('breathe', 'VBP'), ('in', 'IN'), ('air', 'NN'), ('contaminate', 'NN'), ('by', 'IN'), ('droplet', 'NN'), ('and', 'CC'), ('small', 'JJ'), ('airborne', 'JJ'), ('particle', 'NN'), ('contain', 'VBP'), ('the', 'DT'), ('virus', 'NN'), ('the', 'DT'), ('risk', 'NN'), ('of', 'IN'), ('breathe', 'VB'), ('these', 'DT'), ('in', 'IN'), ('be', 'VB'), ('high', 'JJ'), ('when', 'WRB'), ('people', 'NNS'), ('be', 'VBP'), ('in', 'IN'), ('close', 'JJ'), ('proximity', 'NN'), ('but', 'CC'), ('they', 'PRP'), ('can', 'MD'), ('be', 'VB'), ('inhale', 'JJ'), ('over', 'IN'), ('long', 'JJ'), ('distance', 'NN'), ('particularly', 'RB'), ('indoors', 'RB'), ('transmission', 'NN'), ('can', 'MD'), ('also', 'RB'), ('occur', 'VB'), ('if', 'IN'), ('contaminate', 'JJ'), ('fluid', 'NN'), ('reach', 'VB'), ('the', 'DT'), ('eye', 'NN'), ('nose', 'NN'), ('or', 'CC'), ('mouth', 'NN'), ('and', 'CC'), ('rarely', 'RB'), ('via', 'IN'), ('contaminated', 'JJ'), ('surface', 'NN'), ('infect', 'VBP'), ('person', 'NN'), ('be', 'VB'), ('typically', 'RB'), ('contagious', 'JJ'), ('for', 'IN'), ('10', 'CD'), ('day', 'NN'), ('and', 'CC'), ('can', 'MD'), ('spread', 'VB'), ('the', 'DT'), ('virus', 'NN'), ('even', 'RB'), ('if', 'IN'), ('they', 'PRP'), ('do', 'VBP'), ('not', 'RB'), ('develop', 'VB'), ('symptom', 'NN'), ('mutation', 'NN'), ('have', 'VBP'), ('produce', 'NN'), ('many', 'JJ'), ('strain', 'NN'), ('variant', 'NN'), ('with', 'IN'), ('vary', 'VBP'), ('degree', 'NN'), ('of', 'IN'), ('infectivity', 'NN'), ('and', 'CC'), ('virulence', 'NN'), ('5', 'CD'), ('6', 'CD'), ('covid-19', 'NN'), ('vaccine', 'NN'), ('have', 'VBP'), ('be', 'NN'), ('approve', 'VB'), ('and', 'CC'), ('widely', 'RB'), ('distribute', 'VB'), ('in', 'IN'), ('various', 'JJ'), ('country', 'NN'), ('since', 'IN'), ('december', 'NNP'), ('2020.', 'CD'), ('other', 'JJ'), ('recommended', 'JJ'), ('preventive', 'JJ'), ('measure', 'NN'), ('include', 'VBP'), ('social', 'JJ'), ('distancing', 'NN'), ('masking', 'NN'), ('improve', 'VB'), ('ventilation', 'NN'), ('and', 'CC'), ('air', 'NN'), ('filtration', 'NN'), ('and', 'CC'), ('quarantine', 'NN'), ('those', 'DT'), ('who', 'WP'), ('have', 'VBP'), ('be', 'NN'), ('expose', 'VB'), ('or', 'CC'), ('be', 'VB'), ('symptomatic', 'JJ'), ('treatment', 'NN'), ('include', 'VBP'), ('monoclonal', 'JJ'), ('antibody', 'NN'), ('7', 'CD'), ('novel', 'JJ'), ('antiviral', 'JJ'), ('drug', 'NN'), ('and', 'CC'), ('symptom', 'NN'), ('control', 'NN'), ('governmental', 'JJ'), ('intervention', 'NN'), ('include', 'VBP'), ('travel', 'NN'), ('restriction', 'NN'), ('lockdown', 'NN'), ('business', 'NN'), ('restriction', 'NN'), ('and', 'CC'), ('closure', 'NN'), ('workplace', 'NN'), ('hazard', 'NN'), ('control', 'NN'), ('quarantine', 'NN'), ('test', 'NN'), ('system', 'NN'), ('and', 'CC'), ('trace', 'NN'), ('contact', 'NN'), ('of', 'IN'), ('the', 'DT'), ('infect', 'VB'), ('the', 'DT'), ('pandemic', 'NN'), ('trigger', 'VBP'), ('severe', 'JJ'), ('social', 'JJ'), ('and', 'CC'), ('economic', 'JJ'), ('disruption', 'NN'), ('around', 'IN'), ('the', 'DT'), ('world', 'NN'), ('include', 'VBP'), ('the', 'DT'), ('large', 'JJ'), ('global', 'JJ'), ('recession', 'NN'), ('since', 'IN'), ('the', 'DT'), ('great', 'JJ'), ('depression', 'NN'), ('8', 'CD'), ('widespread', 'JJ'), ('supply', 'NN'), ('shortage', 'NN'), ('include', 'VBP'), ('food', 'NN'), ('shortage', 'NN'), ('be', 'VB'), ('cause', 'NN'), ('by', 'IN'), ('supply', 'NN'), ('chain', 'NN'), ('disruption', 'NN'), ('and', 'CC'), ('panic', 'NN'), ('buy', 'VBP'), ('the', 'DT'), ('resultant', 'JJ'), ('near-global', 'JJ'), ('lockdown', 'NN'), ('saw', 'VBD'), ('an', 'DT'), ('unprecedented', 'JJ'), ('pollution', 'NN'), ('decrease', 'NN'), ('educational', 'JJ'), ('institution', 'NN'), ('and', 'CC'), ('public', 'JJ'), ('area', 'NN'), ('be', 'VB'), ('partially', 'RB'), ('or', 'CC'), ('fully', 'RB'), ('close', 'JJ'), ('in', 'IN'), ('many', 'JJ'), ('jurisdiction', 'NN'), ('and', 'CC'), ('many', 'JJ'), ('event', 'NN'), ('be', 'VB'), ('cancel', 'VB'), ('or', 'CC'), ('postpone', 'VB'), ('misinformation', 'NN'), ('circulate', 'VBP'), ('through', 'IN'), ('social', 'JJ'), ('medium', 'NN'), ('and', 'CC'), ('mass', 'NN'), ('medium', 'NN'), ('and', 'CC'), ('political', 'JJ'), ('tension', 'NN'), ('intensify', 'VB'), ('the', 'DT'), ('pandemic', 'NN'), ('raise', 'VB'), ('issue', 'NN'), ('of', 'IN'), ('racial', 'JJ'), ('and', 'CC'), ('geographic', 'JJ'), ('discrimination', 'NN'), ('health', 'NN'), ('equity', 'NN'), ('and', 'CC'), ('the', 'DT'), ('balance', 'NN'), ('between', 'IN'), ('public', 'JJ'), ('health', 'NN'), ('imperative', 'NN'), ('and', 'CC'), ('individual', 'JJ'), ('right', 'NN')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "freq_dic = dict()\n",
        "\n",
        "for idx, _ in enumerate(text_preprocessed):\n",
        "  if text_preprocessed[idx][1] != text_preprocessed2[idx][1]:\n",
        "    if text_preprocessed[idx][0] not in freq_dic:\n",
        "      freq_dic[text_preprocessed[idx][0]] = 1\n",
        "    else:\n",
        "      freq_dic[text_preprocessed[idx][0]] += 1\n",
        "\n",
        "counter = Counter(freq_dic)\n",
        "print(counter.most_common()[:20])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RiVygITeKJmY",
        "outputId": "33aa96ff-1710-4d40-eef7-d4e42b00b6db"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('is', 3), ('caused', 3), ('were', 3), ('infected', 2), ('are', 2), ('been', 2), ('including', 2), ('known', 1), ('wa', 1), ('identified', 1), ('failed', 1), ('allowing', 1), ('declared', 1), ('had', 1), ('making', 1), ('deadliest', 1), ('underlying', 1), ('transmits', 1), ('contaminated', 1), ('containing', 1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Gasiti bigramele de POS tags cele mai frecvente in textul analizat, si apoi in propozitiile tagged din Brown corpus.\n",
        "\n",
        "/\n",
        "\n",
        "Find the most frequent POS tag bigrams occurring in the analyzed text, then in the tagged sentences in the Brown corpus."
      ],
      "metadata": {
        "id": "w7H6Dwdcb2qq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bigrams = dict()\n",
        "\n",
        "for i in range(len(text_preprocessed2) - 1):\n",
        "  bigram = text_preprocessed2[i][1] + ' ' + text_preprocessed2[i + 1][1] \n",
        "  if bigram not in bigrams:\n",
        "    bigrams[bigram] = 1\n",
        "  else:\n",
        "    bigrams[bigram] +=1\n",
        "\n",
        "bigrams = Counter(bigrams)\n",
        "print(bigrams.most_common()[:-1])"
      ],
      "metadata": {
        "id": "3TvlshlgcF_w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecf5bbb1-13b0-45f5-c487-c5ef9f1137c3"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('NN NN', 39), ('JJ NN', 38), ('NN CC', 21), ('NN IN', 19), ('DT NN', 15), ('IN JJ', 15), ('NN VB', 14), ('NN VBP', 14), ('CC NN', 11), ('JJ JJ', 10), ('DT JJ', 9), ('CD NN', 9), ('VBP NN', 9), ('NN CD', 7), ('IN DT', 7), ('CC JJ', 7), ('VB DT', 6), ('IN NN', 6), ('JJ IN', 6), ('VBP DT', 5), ('NN DT', 5), ('IN CD', 5), ('NN JJ', 5), ('VB NN', 5), ('RB VB', 4), ('VB IN', 4), ('VBP JJ', 4), ('NN RB', 3), ('RB VBP', 3), ('VB RB', 3), ('IN NNP', 3), ('NNP CD', 3), ('CC DT', 3), ('DT IN', 3), ('CD CD', 3), ('JJ CC', 3), ('VBP IN', 3), ('VB JJ', 3), ('CC RB', 3), ('VB CC', 3), ('CD JJ', 3), ('CD CC', 2), ('TO VB', 2), ('VB PRP', 2), ('WP VBP', 2), ('WRB NNS', 2), ('NNS VBP', 2), ('IN VB', 2), ('MD VB', 2), ('RB IN', 2), ('RB JJ', 2), ('CC VB', 2), ('DT DT', 1), ('NNP IN', 1), ('NN TO', 1), ('PRP RB', 1), ('VBP VB', 1), ('PRP TO', 1), ('NN WP', 1), ('CD NNP', 1), ('CD DT', 1), ('NN JJR', 1), ('JJR IN', 1), ('CC CD', 1), ('VBP PRP', 1), ('PRP CD', 1), ('CD IN', 1), ('CC RBS', 1), ('RBS RB', 1), ('VB RBR', 1), ('RBR JJ', 1), ('JJ VBP', 1), ('VB WRB', 1), ('JJ WRB', 1), ('CC PRP', 1), ('PRP MD', 1), ('RB RB', 1), ('RB NN', 1), ('NN MD', 1), ('MD RB', 1), ('CC MD', 1), ('IN PRP', 1), ('PRP VBP', 1), ('VBP RB', 1), ('IN VBP', 1), ('DT WP', 1), ('DT VB', 1), ('NN VBD', 1), ('VBD DT', 1), ('RB CC', 1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bigrams_brown = dict()\n",
        "tagged_sent_news = nltk.corpus.brown.tagged_sents(categories='news')\n",
        "\n",
        "for idx_text in range(len(tagged_sent_news)):\n",
        "  for i in range(len(tagged_sent_news[idx_text]) - 1):\n",
        "    bigram = tagged_sent_news[idx_text][i][1] + ' ' + tagged_sent_news[idx_text][i + 1][1] \n",
        "    if bigram not in bigrams_brown:\n",
        "      bigrams_brown[bigram] = 1\n",
        "    else:\n",
        "      bigrams_brown[bigram] +=1\n",
        "\n",
        "bigrams_brown = Counter(bigrams_brown)\n",
        "print(bigrams_brown.most_common()[:-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lSWwgIiaB51O",
        "outputId": "d79be458-df9e-4e6f-8ce0-0c22287ddf84"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('AT NN', 3981), ('IN AT', 3966), ('NN IN', 3735), ('NP NP', 2390), ('JJ NN', 2105), ('AT JJ', 1522), ('NN .', 1509), ('NN NN', 1461), ('NNS IN', 1415), ('NN ,', 1404), ('NP ,', 1271), ('IN NN', 1202), ('IN NP', 1091), ('TO VB', 1066), ('VBN IN', 867), ('JJ NNS', 839), (', NP', 741), ('NN NNS', 688), ('IN CD', 668), ('NNS .', 641), ('NNS ,', 615), ('NN CC', 607), ('VB AT', 567), ('CD NNS', 558), ('AT NNS', 547), ('PP$ NN', 530), (', AT', 513), ('IN JJ', 512), (', CC', 505), ('IN NNS', 503), ('VBD AT', 499), ('MD VB', 485), ('NP VBD', 481), ('NN-TL NN-TL', 480), ('CS AT', 469), (', IN', 458), ('IN PP$', 454), ('AT NP', 429), ('PPS VBD', 403), ('NP-TL NN-TL', 388), ('NP .', 387), ('AT NN-TL', 381), ('NN VBD', 364), ('NNS CC', 356), ('VB IN', 350), ('RB IN', 350), ('CC NP', 347), ('VBD IN', 345), ('NP IN', 345), ('MD BE', 342), ('NN-TL NP', 327), (\"'' .\", 325), ('DT NN', 311), ('CC NN', 310), ('NP CC', 310), ('AP NN', 307), ('JJ-TL NN-TL', 298), ('NP NN', 297), ('IN VBG', 280), ('BE VBN', 280), ('BEDZ VBN', 274), ('CD IN', 273), ('CC AT', 272), ('NN CS', 263), (\"NN ''\", 260), (', NN', 258), ('NN-TL ,', 255), (', VBD', 255), ('NN MD', 252), ('NN TO', 250), ('VBG IN', 241), ('VBG AT', 239), ('NN BEZ', 236), ('CD ,', 229), ('VBG NN', 226), ('. .', 220), ('NN BEDZ', 217), ('AT AP', 217), ('JJ JJ', 216), ('QL JJ', 209), ('NN-TL .', 207), ('AT JJ-TL', 204), ('IN NN-TL', 203), ('CD NN', 201), (\"'' ,\", 198), ('JJ IN', 198), ('AP NNS', 195), ('NN RB', 195), ('PP$ JJ', 193), ('AT OD', 191), ('CC JJ', 186), (', PPS', 186), ('IN DT', 182), ('RB ,', 179), ('RP IN', 177), ('AT NP-TL', 174), ('AT CD', 174), ('NNS VBD', 173), ('CD .', 172), ('NN-TL NN', 172), ('PP$ NNS', 169), ('JJ-TL NP-TL', 168), ('RB VBD', 165), ('CS PPS', 165), ('BEZ AT', 164), (', CD', 160), ('VBN NN', 160), ('RB VBN', 159), (', JJ', 157), ('NN-TL IN', 157), (', RB', 156), (', CS', 155), ('CC VBD', 154), ('CC NNS', 153), ('VBN TO', 146), ('OD NN', 145), ('PPS BEDZ', 145), ('NN-TL IN-TL', 144), 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('CS VBZ', 1), ('VBZ --', 1), (\"' PPS+BEZ\", 1), ('CD OD', 1), ('BEN CS', 1), ('DTS :', 1), ('NN-TL DOZ*', 1), ('WDT+BEZ VBG', 1), ('HVG NN', 1), ('BER RP', 1), ('AP DTS', 1), ('DTS WP$', 1), ('EX MD*', 1), (', PPSS+BEM', 1), ('NN DO', 1), ('JJ-TL HVZ', 1), ('CC DO', 1), ('DO WDT', 1), ('HV ,', 1), ('HVZ WDT', 1), ('ABX DTS', 1), ('PPS JJ', 1), ('* ,', 1), ('JJ BER', 1), ('WRB ABN', 1), ('VBN JJ-TL', 1), ('PPS TO', 1), ('PPL MD*', 1), ('AT-TL NR-TL', 1), (\"NR-TL ''\", 1), ('AT-TL NN$-TL', 1), ('VBN NNS$', 1), ('PPL BEZ', 1), ('VBN NPS', 1), ('BEZ ABX', 1), ('PPLS VBN', 1), ('DO VB', 1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. Write a grammar and code to produce two trees, one for each reading of the phrase \"old men and women\""
      ],
      "metadata": {
        "id": "cQ9ez4eHqcx_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gram = nltk.CFG.fromstring(\"\"\"\n",
        "  S -> NP | PP | NP PP\n",
        "  PP -> P N | P NP\n",
        "  NP -> N PP | ADJ NP | ADJ N\n",
        "  N -> \"men\" | \"women\"\n",
        "  ADJ -> \"old\"\n",
        "  P -> \"and\"\n",
        "\"\"\")"
      ],
      "metadata": {
        "id": "hupNTlUSBYqg"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rdp = nltk.RecursiveDescentParser(gram)\n",
        "rdp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOylP_viu0VU",
        "outputId": "4576f33d-1696-4043-db70-b5b560b5f0ef"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<nltk.parse.recursivedescent.RecursiveDescentParser at 0x7f4c52beef90>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"old men and women\"\n",
        "\n",
        "for tree in rdp.parse(nltk.word_tokenize(text)):\n",
        "  print(tree)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aK0Ksnrru6Z4",
        "outputId": "f92e3a2e-3878-4660-f61e-78794cfb2bc8"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(S (NP (ADJ old) (NP (N men) (PP (P and) (N women)))))\n",
            "(S (NP (ADJ old) (N men)) (PP (P and) (N women)))\n"
          ]
        }
      ]
    }
  ]
}