diff --git a/05_categorizing_and_tagging_words.ipynb b/05_categorizing_and_tagging_words.ipynb
index c1f42ed..42581b3 100644
--- a/05_categorizing_and_tagging_words.ipynb
+++ b/05_categorizing_and_tagging_words.ipynb
@@ -383,16 +383,1233 @@
    "source": [
     "## Tagged Corpora\n",
     "\n",
-    "### Representing Tagged Tokens"
+    "### Representing Tagged Tokens\n",
+    "\n",
+    "*typically represented as a tuple:* `(token, tag)`\n",
+    "\n",
+    "Can be generated using `str2tuple`..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('fly', 'NN')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tagged_token = nltk.tag.str2tuple('fly/NN')\n",
+    "tagged_token"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('The', 'AT'),\n",
+       " ('grand', 'JJ'),\n",
+       " ('jury', 'NN'),\n",
+       " ('commented', 'VBD'),\n",
+       " ('on', 'IN'),\n",
+       " ('a', 'AT'),\n",
+       " ('number', 'NN'),\n",
+       " ('of', 'IN'),\n",
+       " ('other', 'AP'),\n",
+       " ('topics', 'NNS'),\n",
+       " (',', ','),\n",
+       " ('AMONG', 'IN'),\n",
+       " ('them', 'PPO'),\n",
+       " ('the', 'AT'),\n",
+       " ('Atlanta', 'NP'),\n",
+       " ('and', 'CC'),\n",
+       " ('Fulton', 'NP-TL'),\n",
+       " ('County', 'NN-TL'),\n",
+       " ('purchasing', 'VBG'),\n",
+       " ('departments', 'NNS'),\n",
+       " ('which', 'WDT'),\n",
+       " ('it', 'PPS'),\n",
+       " ('said', 'VBD'),\n",
+       " ('``', '``'),\n",
+       " ('ARE', 'BER'),\n",
+       " ('well', 'QL'),\n",
+       " ('operated', 'VBN'),\n",
+       " ('and', 'CC'),\n",
+       " ('follow', 'VB'),\n",
+       " ('generally', 'RB'),\n",
+       " ('accepted', 'VBN'),\n",
+       " ('practices', 'NNS'),\n",
+       " ('which', 'WDT'),\n",
+       " ('inure', 'VB'),\n",
+       " ('to', 'IN'),\n",
+       " ('the', 'AT'),\n",
+       " ('best', 'JJT'),\n",
+       " ('interest', 'NN'),\n",
+       " ('of', 'IN'),\n",
+       " ('both', 'ABX'),\n",
+       " ('governments', 'NNS'),\n",
+       " (\"''\", \"''\"),\n",
+       " ('.', '.')]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sent = '''\n",
+    "The/AT grand/JJ jury/NN commented/VBD on/IN a/AT number/NN of/IN\n",
+    "other/AP topics/NNS ,/, AMONG/IN them/PPO the/AT Atlanta/NP and/CC\n",
+    "Fulton/NP-tl County/NN-tl purchasing/VBG departments/NNS which/WDT it/PPS\n",
+    "said/VBD ``/`` ARE/BER well/QL operated/VBN and/CC follow/VB generally/RB\n",
+    "accepted/VBN practices/NNS which/WDT inure/VB to/IN the/AT best/JJT\n",
+    "interest/NN of/IN both/ABX governments/NNS ''/'' ./.\n",
+    "'''\n",
+    "[nltk.tag.str2tuple(t) for t in sent.split()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reading Tagged Corpora\n",
+    "\n",
+    "    The/at Fulton/np-tl County/nn-tl Grand/jj-tl Jury/nn-tl said/vbd Friday/nr an/at investigation/nn of/in Atlanta's/np$ recent/jj primary/nn election/nn produced/vbd / no/at evidence/nn ''/'' that/cs any/dti irregularities/nns took/vbd place/nn ./.\n",
+    "\n",
+    "if a corpus contains tagged text, the corpus interface will have a **`tagged_words()`** method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('The', 'AT'), ('Fulton', 'NP-TL'), ...]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.corpus.brown.tagged_words()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('The', 'DET'), ('Fulton', 'NOUN'), ...]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.corpus.brown.tagged_words(tagset='universal')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('now', 'RB'), ('im', 'PRP'), ('left', 'VBD'), ...]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(nltk.corpus.nps_chat.tagged_words())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Confidence', 'NN'), ('in', 'IN'), ('the', 'DT'), ...]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.corpus.conll2000.tagged_words()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Pierre', 'NNP'), ('Vinken', 'NNP'), (',', ','), ...]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.corpus.treebank.tagged_words()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*not all corpora have the same tag sets, so can force mapping to **universal** tagset*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('The', 'DET'), ('Fulton', 'NOUN'), ...]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.corpus.brown.tagged_words(tagset='universal')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Pierre', 'NOUN'), ('Vinken', 'NOUN'), (',', '.'), ...]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.corpus.treebank.tagged_words(tagset='universal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tagged corpora in other languages..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('一', 'Neu'), ('友情', 'Nad'), ('嘉珍', 'Nba'), ...]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.corpus.sinica_treebank.tagged_words()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('মহিষের', 'NN'), ('সন্তান', 'NN'), (':', 'SYM'), ...]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.corpus.indian.tagged_words()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Jersei', 'N'), ('atinge', 'V'), ('média', 'N'), ...]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.corpus.mac_morpho.tagged_words()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Sao', 'NC'), ('Paulo', 'VMI'), ('(', 'Fpa'), ...]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.corpus.conll2002.tagged_words()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('El', 'da0ms0'), ('Tribunal_Suprem', 'np0000o'), ...]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.corpus.cess_cat.tagged_words()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Universal Part-of-Speech tagset\n",
+    "\n",
+    "**Common POS tags**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('NOUN', 30640),\n",
+       " ('VERB', 14399),\n",
+       " ('ADP', 12355),\n",
+       " ('.', 11928),\n",
+       " ('DET', 11389),\n",
+       " ('ADJ', 6706),\n",
+       " ('ADV', 3349),\n",
+       " ('CONJ', 2717),\n",
+       " ('PRON', 2535),\n",
+       " ('PRT', 2264),\n",
+       " ('NUM', 2166),\n",
+       " ('X', 106)]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "brn = nltk.corpus.brown\n",
+    "brown_news_tagged = brn.tagged_words(categories='news', tagset='universal')\n",
+    "tag_fd = nltk.FreqDist(tag for (word, tag) in brown_news_tagged)\n",
+    "tag_fd.most_common()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kBjR/fssOjDvugEWLWt7bZBMYO7ZlF8aIEcVjViVJksrNGRqSJDW4FStg0qSW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\nJEmSJKnv/P/heu4yGlJ0FwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a70dc18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot = plt.figure(figsize=(18,10))\n",
+    "tag_fd.plot(cumulative=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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p2GsvmDkTRo0KBfYzzoCbbvJ7jTiXFJ8Ly5WcJ5+EI4+Ejz+Gc8+FK67wJOJc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\nhXu/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a8b7e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tag_fd.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Nouns\n",
+    "\n",
+    "**what parts of speech appear before nouns?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(('The', 'DET'), ('Fulton', 'NOUN')),\n",
+       " (('Fulton', 'NOUN'), ('County', 'NOUN')),\n",
+       " (('County', 'NOUN'), ('Grand', 'ADJ')),\n",
+       " (('Grand', 'ADJ'), ('Jury', 'NOUN')),\n",
+       " (('Jury', 'NOUN'), ('said', 'VERB')),\n",
+       " (('said', 'VERB'), ('Friday', 'NOUN')),\n",
+       " (('Friday', 'NOUN'), ('an', 'DET')),\n",
+       " (('an', 'DET'), ('investigation', 'NOUN')),\n",
+       " (('investigation', 'NOUN'), ('of', 'ADP')),\n",
+       " (('of', 'ADP'), (\"Atlanta's\", 'NOUN')),\n",
+       " ((\"Atlanta's\", 'NOUN'), ('recent', 'ADJ')),\n",
+       " (('recent', 'ADJ'), ('primary', 'NOUN')),\n",
+       " (('primary', 'NOUN'), ('election', 'NOUN')),\n",
+       " (('election', 'NOUN'), ('produced', 'VERB')),\n",
+       " (('produced', 'VERB'), ('``', '.')),\n",
+       " (('``', '.'), ('no', 'DET')),\n",
+       " (('no', 'DET'), ('evidence', 'NOUN')),\n",
+       " (('evidence', 'NOUN'), (\"''\", '.')),\n",
+       " ((\"''\", '.'), ('that', 'ADP')),\n",
+       " (('that', 'ADP'), ('any', 'DET'))]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "word_tag_pairs = nltk.bigrams(brown_news_tagged)\n",
+    "list(word_tag_pairs)[:20]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['DET',\n",
+       " 'NOUN',\n",
+       " 'ADJ',\n",
+       " 'VERB',\n",
+       " 'DET',\n",
+       " 'ADP',\n",
+       " 'ADJ',\n",
+       " 'NOUN',\n",
+       " 'DET',\n",
+       " 'DET',\n",
+       " 'VERB',\n",
+       " 'DET',\n",
+       " 'ADP',\n",
+       " 'NOUN',\n",
+       " 'DET',\n",
+       " 'ADJ',\n",
+       " 'ADJ',\n",
+       " 'DET',\n",
+       " 'DET',\n",
+       " 'CONJ']"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "word_tag_pairs = nltk.bigrams(brown_news_tagged) # generator needs to be redefined\n",
+    "noun_preceders = [a[1] for a, b in word_tag_pairs if b[1] == 'NOUN']\n",
+    "noun_preceders[:20]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['NOUN',\n",
+       " 'DET',\n",
+       " 'ADJ',\n",
+       " 'ADP',\n",
+       " '.',\n",
+       " 'VERB',\n",
+       " 'CONJ',\n",
+       " 'NUM',\n",
+       " 'ADV',\n",
+       " 'PRT',\n",
+       " 'PRON',\n",
+       " 'X']"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fdist = nltk.FreqDist(noun_preceders)\n",
+    "[tag for tag, _ in fdist.most_common()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Z9mkeWV2IamZPA8vS9SlN29HABDOrNrO3gMVAf0lbAJ3MbFb0unHAMSnvGRtt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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11a77d128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fdist.plot(cumulative=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Verbs\n",
+    "\n",
+    "**what are the most common verbs in news text?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['is',\n",
+       " 'said',\n",
+       " 'was',\n",
+       " 'are',\n",
+       " 'be',\n",
+       " 'has',\n",
+       " 'have',\n",
+       " 'will',\n",
+       " 'says',\n",
+       " 'would',\n",
+       " 'were',\n",
+       " 'had',\n",
+       " 'been',\n",
+       " 'could',\n",
+       " \"'s\",\n",
+       " 'can',\n",
+       " 'do',\n",
+       " 'say',\n",
+       " 'make',\n",
+       " 'may',\n",
+       " 'did',\n",
+       " 'rose',\n",
+       " 'made',\n",
+       " 'does',\n",
+       " 'expected']"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wsj = nltk.corpus.treebank.tagged_words(tagset='universal')\n",
+    "word_tag_fd = nltk.FreqDist(wsj)\n",
+    "[wt[0] for wt, _ in word_tag_fd.most_common() if wt[1] == 'VERB'][:25]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*words and tags are paired. treat the **word as a condition** and the **tag as an event** and create a cfd*\n",
+    "\n",
+    "**lists the most common tags, given a word**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('VERB', 28), ('NOUN', 20)]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfd1 = nltk.ConditionalFreqDist(wsj)\n",
+    "cfd1['yield'].most_common()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('VERB', 25), ('NOUN', 3)]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfd1['cut'].most_common()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "reverse the order... conditions: tags, events: words \n",
+    "\n",
+    "**see the likely words, given a tag**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {
-    "collapsed": true
+    "collapsed": false,
+    "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['protective',\n",
+       " 'insidious',\n",
+       " 'long',\n",
+       " 'crash',\n",
+       " 'daily',\n",
+       " '238,000-circulation',\n",
+       " 'interior',\n",
+       " 'only',\n",
+       " 'near-record',\n",
+       " 'matching',\n",
+       " 'three-month',\n",
+       " 'Per-share',\n",
+       " 'questionable',\n",
+       " 'aggressive',\n",
+       " 'combined',\n",
+       " 'acute',\n",
+       " '63-year-old',\n",
+       " 'seven-year',\n",
+       " 'mortgage-based',\n",
+       " 'impudent',\n",
+       " 'unproductive',\n",
+       " 'socialist',\n",
+       " 'tall',\n",
+       " 'sticky',\n",
+       " 'computer-aided']"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wsh = nltk.corpus.treebank.tagged_words()\n",
+    "cfd2 = nltk.ConditionalFreqDist((tag, word) for (word, tag) in wsj)\n",
+    "list(cfd2['ADJ'])[:25]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adjectives and Adverbs\n",
+    "\n",
+    "*skipping*\n",
+    "\n",
+    "### Unsimplified Tags\n",
+    "\n",
+    "*skipping*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring Tagged Corpora\n",
+    "\n",
+    "**Find words that follow '*often*'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[',',\n",
+       " '.',\n",
+       " 'accomplished',\n",
+       " 'analytically',\n",
+       " 'appear',\n",
+       " 'apt',\n",
+       " 'associated',\n",
+       " 'assuming',\n",
+       " 'became',\n",
+       " 'become',\n",
+       " 'been',\n",
+       " 'began',\n",
+       " 'call',\n",
+       " 'called',\n",
+       " 'carefully',\n",
+       " 'chose',\n",
+       " 'classified',\n",
+       " 'colorful',\n",
+       " 'composed',\n",
+       " 'contain',\n",
+       " 'differed',\n",
+       " 'difficult',\n",
+       " 'encountered',\n",
+       " 'enough',\n",
+       " 'equate',\n",
+       " 'extremely',\n",
+       " 'found',\n",
+       " 'happens',\n",
+       " 'have',\n",
+       " 'ignored',\n",
+       " 'in',\n",
+       " 'involved',\n",
+       " 'more',\n",
+       " 'needed',\n",
+       " 'nightly',\n",
+       " 'observed',\n",
+       " 'of',\n",
+       " 'on',\n",
+       " 'out',\n",
+       " 'quite',\n",
+       " 'represent',\n",
+       " 'responsible',\n",
+       " 'revamped',\n",
+       " 'seclude',\n",
+       " 'set',\n",
+       " 'shortened',\n",
+       " 'sing',\n",
+       " 'sounded',\n",
+       " 'stated',\n",
+       " 'still',\n",
+       " 'sung',\n",
+       " 'supported',\n",
+       " 'than',\n",
+       " 'to',\n",
+       " 'when',\n",
+       " 'work']"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "brown_learned_text = brn.words(categories='learned')\n",
+    "sorted(set(b for a, b in nltk.bigrams(brown_learned_text) if a == 'often'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**instead, use `tagged_words()` to look at POS tags**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('1', 'NUM'), ('.', '.'), ('Introduction', 'NOUN'), ...]"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "brown_lrnd_tagged = brn.tagged_words(categories='learned', tagset='universal')\n",
+    "brown_lrnd_tagged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ADJ', 'ADV', 'VERB', 'VERB', 'ADJ']"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tags = [b[1] for a, b in nltk.bigrams(brown_lrnd_tagged) if a[0] == 'often']\n",
+    "tags[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VERB  ADV  ADP  ADJ    .  PRT \n",
+      "  37    8    7    6    4    2 \n"
+     ]
+    }
+   ],
+   "source": [
+    "fd = nltk.FreqDist(tags)\n",
+    "fd.tabulate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*POS that follow **'often'** the most are **verbs***\n",
+    "\n",
+    "---\n",
+    "\n",
+    "Find words that are parts of sequences of tags+words (e.g., **`Verb`, to, `Verb`**)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "combined to achieve\n",
+      "continue to place\n",
+      "serve to protect\n",
+      "wanted to wait\n",
+      "allowed to place\n",
+      "expected to become\n",
+      "expected to approve\n",
+      "expected to make\n",
+      "intends to make\n",
+      "seek to set\n",
+      "like to see\n",
+      "designed to provide\n",
+      "get to hear\n",
+      "expects to tell\n",
+      "expected to give\n",
+      "prefer to pay\n",
+      "required to obtain\n",
+      "permitted to teach\n",
+      "designed to reduce\n"
+     ]
+    }
+   ],
+   "source": [
+    "def process(sentence):\n",
+    "    for (w1, t1), (w2, t2), (w3, t3) in nltk.trigrams(sentence):\n",
+    "        if (t1.startswith('V') and t2 == 'TO' and t3.startswith('V')):\n",
+    "            print(w1, w2, w3)\n",
+    "            \n",
+    "for tagged_sent in brn.tagged_sents()[:200]:\n",
+    "    process(tagged_sent)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "**Identifying words that have ambiguous POS tags**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "best ADJ ADV NOUN VERB\n",
+      "close ADV ADJ VERB NOUN\n",
+      "open ADJ VERB NOUN ADV\n",
+      "present ADJ ADV NOUN VERB\n",
+      "that ADP DET PRON ADV\n"
+     ]
+    }
+   ],
+   "source": [
+    "brown_news_tagged = brn.tagged_words(categories='news', tagset='universal')\n",
+    "data = nltk.ConditionalFreqDist((word.lower(), tag)\n",
+    "                                for word, tag in brown_news_tagged)\n",
+    "for word in sorted(data.conditions()):\n",
+    "    if len(data[word]) > 3:\n",
+    "        tags = [tag for tag, _ in data[word].most_common()]\n",
+    "        print(word, ' '.join(tags))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mapping Words to Properties Using Python Dictionaries\n",
+    "\n",
+    "*skipping this section on python dictionary usage*\n",
+    "\n",
+    "## Automatic Tagging\n",
+    "\n",
+    "**Using tagged *sentences* instead of words**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['The',\n",
+       "  'Fulton',\n",
+       "  'County',\n",
+       "  'Grand',\n",
+       "  'Jury',\n",
+       "  'said',\n",
+       "  'Friday',\n",
+       "  'an',\n",
+       "  'investigation',\n",
+       "  'of',\n",
+       "  \"Atlanta's\",\n",
+       "  'recent',\n",
+       "  'primary',\n",
+       "  'election',\n",
+       "  'produced',\n",
+       "  '``',\n",
+       "  'no',\n",
+       "  'evidence',\n",
+       "  \"''\",\n",
+       "  'that',\n",
+       "  'any',\n",
+       "  'irregularities',\n",
+       "  'took',\n",
+       "  'place',\n",
+       "  '.']]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from nltk.corpus import brown\n",
+    "brown_tagged_sents = brown.tagged_sents(categories='news')\n",
+    "brown_sents = brown.sents(categories='news')\n",
+    "brown_sents[:1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example: create a default tagger that tags everything as **NN**\n",
+    "\n",
+    "*default taggers assign a tag to every single word*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('I', 'NN'),\n",
+       " ('do', 'NN'),\n",
+       " ('not', 'NN'),\n",
+       " ('like', 'NN'),\n",
+       " ('green', 'NN'),\n",
+       " ('eggs', 'NN'),\n",
+       " ('and', 'NN'),\n",
+       " ('ham', 'NN'),\n",
+       " (',', 'NN'),\n",
+       " ('I', 'NN'),\n",
+       " ('do', 'NN'),\n",
+       " ('not', 'NN'),\n",
+       " ('like', 'NN'),\n",
+       " ('them', 'NN'),\n",
+       " ('Sam', 'NN'),\n",
+       " ('I', 'NN'),\n",
+       " ('am', 'NN'),\n",
+       " ('!', 'NN')]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw = 'I do not like green eggs and ham, I do not like them Sam I am!'\n",
+    "tokens = word_tokenize(raw)\n",
+    "default_tagger = nltk.DefaultTagger('NN')\n",
+    "default_tagger.tag(tokens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*this \"tagger\" performs poorly, of course*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.13089484257215028"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "default_tagger.evaluate(brown_tagged_sents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Default taggers are still useful because after processing several thousand words of English text, most new words will be nouns**\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### The Regular Expression Tagger"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": []
   }
  ],