Part of Speech Tagging with Hidden Markov Models_git.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part of Speech Tagging with Hidden Markov Models \n",
    "- ![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from IPython.core.display import HTML\n",
    "from itertools import chain\n",
    "from collections import Counter, defaultdict\n",
    "from helpers import show_model, Dataset\n",
    "from pomegranate import State, HiddenMarkovModel, DiscreteDistribution"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example from the Brown corpus dataset\n",
    "- ![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 57340 sentences in the corpus.\n",
      "There are 45872 sentences in the training set.\n",
      "There are 11468 sentences in the testing set.\n"
     ]
    }
   ],
   "source": [
    "data = Dataset(\"tags-universal.txt\", \"brown-universal.txt\", train_test_split=0.8)\n",
    "\n",
    "print(\"There are {} sentences in the corpus.\".format(len(data)))\n",
    "print(\"There are {} sentences in the training set.\".format(len(data.training_set)))\n",
    "print(\"There are {} sentences in the testing set.\".format(len(data.testing_set)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentences\n",
    "- `Dataset.sentences` is a dictionary of all sentences in the training corpus\n",
    "- the unique sentence identifier is the key, a tuple of (words,tag) is the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentence(words=('Perhaps', 'it', 'was', 'right', ';', ';'), tags=('ADV', 'PRON', 'VERB', 'ADJ', '.', '.'))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key = 'b100-38532'\n",
    "data.sentences[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence: b100-38532\n",
      "words:\n",
      "\t('Perhaps', 'it', 'was', 'right', ';', ';')\n",
      "tags:\n",
      "\t('ADV', 'PRON', 'VERB', 'ADJ', '.', '.')\n"
     ]
    }
   ],
   "source": [
    "print(f\"Sentence: {key}\")\n",
    "print(f\"words:\\n\\t{data.sentences[key].words}\")\n",
    "print(f\"tags:\\n\\t{data.sentences[key].tags}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counting Unique Elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are a total of 1161192 samples of 56057 unique words in the corpus.\n",
      "There are 928458 samples of 50536 unique words in the training set.\n",
      "There are 232734 samples of 25112 unique words in the testing set.\n",
      "There are 5521 words in the test set that are missing in the training set.\n"
     ]
    }
   ],
   "source": [
    "print(\"There are a total of {} samples of {} unique words in the corpus.\"\n",
    "      .format(data.N, len(data.vocab)))\n",
    "\n",
    "print(\"There are {} samples of {} unique words in the training set.\"\n",
    "      .format(data.training_set.N, len(data.training_set.vocab)))\n",
    "\n",
    "print(\"There are {} samples of {} unique words in the testing set.\"\n",
    "      .format(data.testing_set.N, len(data.testing_set.vocab)))\n",
    "\n",
    "print(\"There are {} words in the test set that are missing in the training set.\"\n",
    "      .format(len(data.testing_set.vocab - data.training_set.vocab)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pair Counts\n",
    "- computes the joint frequency counts for two input sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"alert alert-block alert-success\">Your emission counts look good!</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pair_counts(sequences_A, sequences_B):\n",
    "\n",
    "    dcount = defaultdict(dict) \n",
    "    \n",
    "    d = dict(Counter(list(zip(sequences_A, sequences_B))))\n",
    "    \n",
    "    for key,value in d.items():\n",
    "        dcount[key[0]][key[1]] = value\n",
    "    return dcount\n",
    "\n",
    "tags = [tag for word, tag in data.stream()]\n",
    "words = [word for word, tag in data.stream()]\n",
    "emission_counts = pair_counts(tags, words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Frequent Class Tagger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "\n",
    "FakeState = namedtuple(\"FakeState\", \"name\")\n",
    "\n",
    "class MFCTagger:\n",
    "    missing = FakeState(name=\"<MISSING>\")\n",
    "    \n",
    "    def __init__(self, table):\n",
    "        self.table = defaultdict(lambda: MFCTagger.missing)\n",
    "        self.table.update({word: FakeState(name=tag) for word, tag in table.items()})\n",
    "        \n",
    "    def viterbi(self, seq):\n",
    "        \"\"\"This method simplifies predictions by matching the Pomegranate viterbi() interface\"\"\"\n",
    "        return 0., list(enumerate([\"<start>\"] + [self.table[w] for w in seq] + [\"<end>\"]))\n",
    "\n",
    "word_counts = pair_counts(words, tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"alert alert-block alert-success\">Your MFC tagger has all the correct words!</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfc_table = {}\n",
    "\n",
    "for word in data.training_set.vocab:\n",
    "    \n",
    "    mfc_table[word] = max(word_counts[word], key=word_counts[word].get)\n",
    "\n",
    "mfc_model = MFCTagger(mfc_table) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Veblen': 'NOUN',\n",
       " 'fifties': 'NOUN',\n",
       " 'unfairly': 'ADV',\n",
       " 'passiveness': 'NOUN',\n",
       " 'hepatitis': 'NOUN',\n",
       " 'ventures': 'NOUN',\n",
       " 'Latinovich': 'NOUN',\n",
       " 'capillary': 'NOUN',\n",
       " 'startling': 'ADJ',\n",
       " 'Sixties': 'NOUN',\n",
       " 'Oral': 'ADJ',\n",
       " 'proportions': 'NOUN',\n",
       " 'misdemeanor': 'NOUN',\n",
       " 'Countrymen': 'NOUN',\n",
       " 'convert': 'VERB',\n",
       " 'Alberto': 'NOUN',\n",
       " 'causal': 'ADJ',\n",
       " 'depravity': 'NOUN',\n",
       " 'coriander': 'NOUN',\n",
       " 'Connor': 'NOUN',\n",
       " \"Sangallo's\": 'NOUN',\n",
       " 'deaf': 'ADJ',\n",
       " 'Vermeersch': 'NOUN',\n",
       " 'chestnut': 'NOUN',\n",
       " 'whining': 'VERB',\n",
       " 'crook': 'NOUN',\n",
       " 'intact': 'ADJ',\n",
       " 'deceiving': 'VERB',\n",
       " 'barbecued': 'VERB',\n",
       " 'end-product': 'NOUN',\n",
       " 'germinate': 'VERB',\n",
       " 'waving': 'VERB',\n",
       " 'Phil': 'NOUN',\n",
       " 'undiluted': 'ADJ',\n",
       " 'they': 'PRON',\n",
       " 'statutory': 'ADJ',\n",
       " 'millennia': 'NOUN',\n",
       " 'vice': 'NOUN',\n",
       " 'Tanin': 'X',\n",
       " 'Lecture': 'NOUN',\n",
       " \"Dexter's\": 'NOUN',\n",
       " 'occurrence': 'NOUN',\n",
       " 'moment': 'NOUN',\n",
       " 'understood': 'VERB',\n",
       " 'remaining': 'VERB',\n",
       " 'doweling': 'VERB',\n",
       " 'shooter': 'NOUN',\n",
       " 'Sinton': 'NOUN',\n",
       " 'aniline': 'NOUN',\n",
       " 'nominating': 'VERB',\n",
       " 'Torrio-Capone': 'NOUN',\n",
       " 'injection': 'NOUN',\n",
       " 'Pierce': 'NOUN',\n",
       " 'chills': 'NOUN',\n",
       " 'nothingness': 'NOUN',\n",
       " 'frangipani': 'NOUN',\n",
       " 'chinked': 'VERB',\n",
       " 'Hydrogen': 'NOUN',\n",
       " 'possessed': 'VERB',\n",
       " 'Aside': 'ADV',\n",
       " 'Donaldson': 'NOUN',\n",
       " 'line-driven': 'VERB',\n",
       " 'asw': 'NOUN',\n",
       " \"baby's\": 'NOUN',\n",
       " 'interested': 'VERB',\n",
       " 'Sung-Shan': 'NOUN',\n",
       " 'chansons': 'NOUN',\n",
       " 'Irene': 'NOUN',\n",
       " 'longshot': 'NOUN',\n",
       " 'hypothesis': 'NOUN',\n",
       " 'bloom': 'NOUN',\n",
       " 'Fleischmanns': 'NOUN',\n",
       " 'aided': 'VERB',\n",
       " 'gambles': 'NOUN',\n",
       " 'Bookies': 'NOUN',\n",
       " 'Dragons': 'NOUN',\n",
       " 'incomparably': 'ADV',\n",
       " 'crowds': 'NOUN',\n",
       " 'slingshot': 'NOUN',\n",
       " 'Artisans': 'X',\n",
       " '7,484,268': 'NUM',\n",
       " 'itch': 'VERB',\n",
       " 'Becket': 'NOUN',\n",
       " 'globally': 'ADV',\n",
       " 'obtained': 'VERB',\n",
       " 'politico': 'NOUN',\n",
       " 'lemon': 'NOUN',\n",
       " 'Gates': 'NOUN',\n",
       " 'seamless': 'ADJ',\n",
       " 'blinked': 'VERB',\n",
       " 'transitions': 'NOUN',\n",
       " 'septa': 'NOUN',\n",
       " 'Assiniboia': 'NOUN',\n",
       " 'carryovers': 'NOUN',\n",
       " 'sycophantically': 'ADV',\n",
       " 'mould': 'VERB',\n",
       " 'Cam': 'NOUN',\n",
       " 'more-than-ordinary': 'ADJ',\n",
       " 'floodlit': 'VERB',\n",
       " 'Nagasaki': 'NOUN',\n",
       " 'deep': 'ADJ',\n",
       " 'present': 'ADJ',\n",
       " 'oxidised': 'VERB',\n",
       " 'transversally': 'ADV',\n",
       " 'Abra': 'NOUN',\n",
       " 'Proposed': 'VERB',\n",
       " 'editorially': 'ADV',\n",
       " 'necessary': 'ADJ',\n",
       " 'Conceivably': 'ADV',\n",
       " 'Australia': 'NOUN',\n",
       " 'easement': 'NOUN',\n",
       " 'presenting': 'VERB',\n",
       " 'guiltless': 'ADJ',\n",
       " 'subliterary': 'ADJ',\n",
       " 'Karns': 'NOUN',\n",
       " 'uncooperative': 'ADJ',\n",
       " 'strenuous': 'ADJ',\n",
       " 'Converts': 'NOUN',\n",
       " 'SP-44007': 'NOUN',\n",
       " 'L': 'NOUN',\n",
       " 'Ivory': 'NOUN',\n",
       " 'Saw': 'VERB',\n",
       " \"l'Universite\": 'X',\n",
       " 'small-arms': 'NOUN',\n",
       " 'solemnity': 'NOUN',\n",
       " 'Bartlett': 'NOUN',\n",
       " 'Straightened': 'VERB',\n",
       " '61.2%': 'NOUN',\n",
       " 'knight-errantry': 'NOUN',\n",
       " 'filigree': 'NOUN',\n",
       " 'Malabar': 'NOUN',\n",
       " 'sunning': 'VERB',\n",
       " 'Benesi': 'NOUN',\n",
       " 'winders': 'NOUN',\n",
       " 'alleviating': 'VERB',\n",
       " \"Hard's\": 'NOUN',\n",
       " 'Living': 'VERB',\n",
       " 'Premium': 'NOUN',\n",
       " 'Greetings': 'NOUN',\n",
       " 'twelve-hour': 'ADJ',\n",
       " \"Verplanck's\": 'NOUN',\n",
       " 'dinnertime': 'NOUN',\n",
       " 'Tsarevich': 'NOUN',\n",
       " \"Angeles'\": 'NOUN',\n",
       " 'Rarely': 'ADV',\n",
       " 'kindness': 'NOUN',\n",
       " 'Promoters': 'NOUN',\n",
       " \"gran'dad\": 'NOUN',\n",
       " 'infliction': 'NOUN',\n",
       " 'belongs': 'VERB',\n",
       " 'inverted': 'VERB',\n",
       " 'sportiest': 'ADJ',\n",
       " 'Dwyer': 'NOUN',\n",
       " 'consorting': 'VERB',\n",
       " 'Fledermaus': 'X',\n",
       " 'girders': 'NOUN',\n",
       " 'appropriate': 'ADJ',\n",
       " 'tiredly': 'ADV',\n",
       " 'Contribution': 'NOUN',\n",
       " 'exorcise': 'VERB',\n",
       " 'magnetic': 'ADJ',\n",
       " 'loathed': 'VERB',\n",
       " 'sheepe': 'NOUN',\n",
       " 'Terral': 'NOUN',\n",
       " 'Astaires': 'NOUN',\n",
       " '30-inch': 'ADJ',\n",
       " 'grim': 'ADJ',\n",
       " 'combined': 'VERB',\n",
       " 'subtleties': 'NOUN',\n",
       " 'savory': 'ADJ',\n",
       " 'protective': 'ADJ',\n",
       " 'Border': 'NOUN',\n",
       " 'sarcastic': 'ADJ',\n",
       " 'pencil': 'NOUN',\n",
       " 'glaring': 'VERB',\n",
       " 'Grande': 'NOUN',\n",
       " \"givin'\": 'VERB',\n",
       " 'Me-210': 'NOUN',\n",
       " 'Scarborough': 'NOUN',\n",
       " 'exclusive': 'ADJ',\n",
       " 'ascendancy': 'NOUN',\n",
       " 'Maurine': 'NOUN',\n",
       " 'wistful': 'ADJ',\n",
       " 'Ice': 'NOUN',\n",
       " '1450': 'NUM',\n",
       " 'things': 'NOUN',\n",
       " 'neutron': 'NOUN',\n",
       " \"somebody's\": 'NOUN',\n",
       " 'arraigning': 'VERB',\n",
       " 'Fortress': 'NOUN',\n",
       " 'footfall': 'NOUN',\n",
       " 'idler': 'NOUN',\n",
       " 'sander': 'NOUN',\n",
       " 'thoroughfares': 'NOUN',\n",
       " 'purged': 'VERB',\n",
       " 'preferred': 'VERB',\n",
       " 'greenhouses': 'NOUN',\n",
       " 'throat': 'NOUN',\n",
       " 'diversions': 'NOUN',\n",
       " 'rejected': 'VERB',\n",
       " 'ducking': 'VERB',\n",
       " 'ventricle': 'NOUN',\n",
       " 'speculative': 'ADJ',\n",
       " 'extenuate': 'VERB',\n",
       " 'Retrace': 'NOUN',\n",
       " 'arrive': 'VERB',\n",
       " 'irreparable': 'ADJ',\n",
       " 'won-lost': 'ADJ',\n",
       " 'synonymy': 'NOUN',\n",
       " 'Improvement': 'NOUN',\n",
       " 'perspective': 'NOUN',\n",
       " 'aplomb': 'NOUN',\n",
       " 'Quarterly': 'NOUN',\n",
       " 'contemptible': 'ADJ',\n",
       " 'prisoner': 'NOUN',\n",
       " 'pseudo-glamorous': 'ADJ',\n",
       " 'totalitarianism': 'NOUN',\n",
       " 'kernels': 'NOUN',\n",
       " 'payoff': 'NOUN',\n",
       " 'freeze-out': 'NOUN',\n",
       " \"He'll\": 'PRT',\n",
       " 'nuances': 'NOUN',\n",
       " 'sops': 'NOUN',\n",
       " 'primaries': 'NOUN',\n",
       " 'piling': 'VERB',\n",
       " 'monumental': 'ADJ',\n",
       " 'Earth-weeks': 'NOUN',\n",
       " 'burn': 'VERB',\n",
       " 'Carl': 'NOUN',\n",
       " 'lamechians': 'NOUN',\n",
       " 'Crusade': 'NOUN',\n",
       " 'ultimate': 'ADJ',\n",
       " 'Apparatus': 'NOUN',\n",
       " 'viewless': 'ADJ',\n",
       " 'deteriorated': 'VERB',\n",
       " \"1860's\": 'NOUN',\n",
       " 'Dealing': 'VERB',\n",
       " 'light-transmitting': 'ADJ',\n",
       " 'Tenda': 'NOUN',\n",
       " 'Baffin': 'NOUN',\n",
       " \"atom's\": 'NOUN',\n",
       " 'seventy': 'NUM',\n",
       " \"drug's\": 'NOUN',\n",
       " 'shuddering': 'VERB',\n",
       " 'parenthood': 'NOUN',\n",
       " 'Lana': 'X',\n",
       " 'Champlain': 'NOUN',\n",
       " 'malignancy': 'NOUN',\n",
       " 'rises': 'VERB',\n",
       " 'Telephone': 'NOUN',\n",
       " 'rests': 'VERB',\n",
       " 'dungeon': 'NOUN',\n",
       " 'lifted': 'VERB',\n",
       " 'strapping': 'VERB',\n",
       " 'complicating': 'VERB',\n",
       " 'sun-tan': 'NOUN',\n",
       " 'Often': 'ADV',\n",
       " 'whistled': 'VERB',\n",
       " 'archaeology': 'NOUN',\n",
       " 'concentrated': 'VERB',\n",
       " 'planters': 'NOUN',\n",
       " 'emphasizing': 'VERB',\n",
       " 'extraneous': 'ADJ',\n",
       " 'York-born': 'ADJ',\n",
       " 'inside': 'ADP',\n",
       " 'Achievement': 'NOUN',\n",
       " 'contradictorily': 'ADV',\n",
       " 'sandwiches': 'NOUN',\n",
       " 'unquiet': 'ADJ',\n",
       " 'Specifications': 'NOUN',\n",
       " 'pinkly': 'ADV',\n",
       " 'Dappertutto': 'NOUN',\n",
       " 'participate': 'VERB',\n",
       " 'harbor': 'NOUN',\n",
       " 'infringements': 'NOUN',\n",
       " 'hollyhock': 'NOUN',\n",
       " 'Albert': 'NOUN',\n",
       " 'Lorenz': 'NOUN',\n",
       " 'doors': 'NOUN',\n",
       " 'nakedly': 'ADV',\n",
       " 'billboard': 'NOUN',\n",
       " 'Frist': 'X',\n",
       " 'knight': 'NOUN',\n",
       " 'regrets': 'NOUN',\n",
       " 'dervishes': 'NOUN',\n",
       " 'Magog': 'NOUN',\n",
       " 'deloused': 'VERB',\n",
       " 'nigs': 'NOUN',\n",
       " 'mere': 'ADJ',\n",
       " 'Sean': 'NOUN',\n",
       " 'Conference': 'NOUN',\n",
       " 'tugging': 'VERB',\n",
       " 'efficacy': 'NOUN',\n",
       " 'joyous': 'ADJ',\n",
       " 'clumsy': 'ADJ',\n",
       " 'saxophonist': 'NOUN',\n",
       " 'while': 'ADP',\n",
       " 'stickpin': 'NOUN',\n",
       " '2-score-year': 'ADJ',\n",
       " 'Century': 'NOUN',\n",
       " 'astray': 'ADV',\n",
       " \"Dana's\": 'NOUN',\n",
       " 'whizzing': 'VERB',\n",
       " 'Computation': 'NOUN',\n",
       " 'sending': 'VERB',\n",
       " 'vacuuming': 'VERB',\n",
       " \"Won't\": 'VERB',\n",
       " 'Ich': 'X',\n",
       " 'misty-eyed': 'ADJ',\n",
       " 'Librarians': 'NOUN',\n",
       " 'associatively': 'ADV',\n",
       " 'Toys': 'NOUN',\n",
       " '45%': 'NOUN',\n",
       " 'Tempos': 'NOUN',\n",
       " 'disagreements': 'NOUN',\n",
       " 'Hanukkah': 'NOUN',\n",
       " 'gel': 'NOUN',\n",
       " 'apples': 'NOUN',\n",
       " 'manumission': 'NOUN',\n",
       " 'chemise': 'NOUN',\n",
       " 'Ecumenical': 'ADJ',\n",
       " 'New-York': 'NOUN',\n",
       " 'Karshilama': 'X',\n",
       " 'ensue': 'VERB',\n",
       " 'Eddies': 'NOUN',\n",
       " '20,000,000': 'NUM',\n",
       " 'cramps': 'NOUN',\n",
       " 'surreptitiously': 'ADV',\n",
       " 'participant': 'NOUN',\n",
       " 'Purvis': 'NOUN',\n",
       " 'cost-billing': 'NOUN',\n",
       " 'Nuit': 'X',\n",
       " 'rhinotracheitis': 'NOUN',\n",
       " 'proteases': 'NOUN',\n",
       " 'experimental': 'ADJ',\n",
       " 'hoops': 'NOUN',\n",
       " 'garish': 'ADJ',\n",
       " 'nearest': 'ADJ',\n",
       " 'salamander': 'NOUN',\n",
       " 'consist': 'VERB',\n",
       " 'diesel': 'NOUN',\n",
       " 'notifying': 'VERB',\n",
       " 'perennially': 'ADV',\n",
       " 'unflagging': 'ADJ',\n",
       " 'lard': 'NOUN',\n",
       " 'Nutrition': 'NOUN',\n",
       " 'foothills': 'NOUN',\n",
       " 'sweetly': 'ADV',\n",
       " 'conjugation': 'NOUN',\n",
       " 'Yorkers': 'NOUN',\n",
       " 'Methods': 'NOUN',\n",
       " 'Lung': 'NOUN',\n",
       " 'recruiting': 'VERB',\n",
       " 'introducing': 'VERB',\n",
       " 'graced': 'VERB',\n",
       " 'Adios-Rena': 'NOUN',\n",
       " 'motorist': 'NOUN',\n",
       " 'Merz': 'NOUN',\n",
       " 'Faust': 'NOUN',\n",
       " \"Somebody'll\": 'PRT',\n",
       " 'May': 'NOUN',\n",
       " 'injunction': 'NOUN',\n",
       " 'Dallas': 'NOUN',\n",
       " 'mop': 'VERB',\n",
       " 'daffodils': 'NOUN',\n",
       " 'Wansley': 'NOUN',\n",
       " 'low-power': 'NOUN',\n",
       " 'headline': 'NOUN',\n",
       " 'oversoftness': 'NOUN',\n",
       " \"Frankfurter's\": 'NOUN',\n",
       " 'domain': 'NOUN',\n",
       " 'spring': 'NOUN',\n",
       " 'thither': 'ADV',\n",
       " 'Kingston': 'NOUN',\n",
       " 'swart': 'ADJ',\n",
       " 'diary': 'NOUN',\n",
       " 'fortune-tellers': 'NOUN',\n",
       " 'unspoken': 'ADJ',\n",
       " 'whips': 'NOUN',\n",
       " 'Clemence': 'NOUN',\n",
       " \"female's\": 'NOUN',\n",
       " 'Jouvet': 'NOUN',\n",
       " 'pace': 'NOUN',\n",
       " 'Loveless': 'NOUN',\n",
       " 'fund-raising': 'NOUN',\n",
       " 'lions': 'NOUN',\n",
       " 'risk': 'NOUN',\n",
       " 'prejudged': 'VERB',\n",
       " 'Restrict': 'VERB',\n",
       " 'booster': 'NOUN',\n",
       " 'parolees': 'NOUN',\n",
       " 'Virginia': 'NOUN',\n",
       " 'excellence': 'NOUN',\n",
       " \"Li'l\": 'ADJ',\n",
       " 'Trinity': 'NOUN',\n",
       " 'dangerously': 'ADV',\n",
       " 'Wild': 'ADJ',\n",
       " 'volcanos': 'NOUN',\n",
       " 'no-trading': 'ADJ',\n",
       " \"tailin'\": 'NOUN',\n",
       " \"Edythe's\": 'NOUN',\n",
       " '26-28': 'NUM',\n",
       " 'outcast': 'NOUN',\n",
       " 'facet': 'NOUN',\n",
       " 'dame': 'NOUN',\n",
       " 'Balkan': 'NOUN',\n",
       " 'legged': 'ADJ',\n",
       " 'hideously': 'ADV',\n",
       " 'surveyed': 'VERB',\n",
       " 'Caution': 'NOUN',\n",
       " 'Serving': 'VERB',\n",
       " '643': 'NUM',\n",
       " 'Comique': 'X',\n",
       " 'naively': 'ADV',\n",
       " 'promenades': 'NOUN',\n",
       " 'carbohydrate': 'NOUN',\n",
       " 'abstract': 'ADJ',\n",
       " 'collectively': 'ADV',\n",
       " 'pink-petticoated': 'ADJ',\n",
       " 'vibrated': 'VERB',\n",
       " 'archaic': 'ADJ',\n",
       " '1702-14': 'NUM',\n",
       " 'compliments': 'NOUN',\n",
       " 'wearing': 'VERB',\n",
       " 'unrepentant': 'ADJ',\n",
       " 'supply': 'NOUN',\n",
       " '328': 'NUM',\n",
       " 'carven': 'ADJ',\n",
       " 'crabapple': 'NOUN',\n",
       " 'Hail': 'VERB',\n",
       " 'kids': 'NOUN',\n",
       " 'aristocratic': 'ADJ',\n",
       " 'burners': 'NOUN',\n",
       " 'Zwei': 'X',\n",
       " 'mealtime': 'NOUN',\n",
       " 'politicians': 'NOUN',\n",
       " 'cortical': 'ADJ',\n",
       " 'tiptoeing': 'VERB',\n",
       " 'escapist': 'NOUN',\n",
       " '78th': 'ADJ',\n",
       " 'S.K.': 'NOUN',\n",
       " 'requests': 'NOUN',\n",
       " 'bud': 'NOUN',\n",
       " 'loose-leaf': 'NOUN',\n",
       " 'revolutionized': 'VERB',\n",
       " 'experimentation': 'NOUN',\n",
       " 'nudging': 'VERB',\n",
       " 'Galilee': 'NOUN',\n",
       " 'caraway': 'NOUN',\n",
       " 'unattached': 'ADJ',\n",
       " 'osmotic': 'ADJ',\n",
       " 'Nazarova': 'NOUN',\n",
       " 'Lobar': 'ADJ',\n",
       " 'false': 'ADJ',\n",
       " 'Hartsfield': 'NOUN',\n",
       " 'plentiful': 'ADJ',\n",
       " 'Thankful': 'ADJ',\n",
       " 'Adler': 'NOUN',\n",
       " 'daisies': 'NOUN',\n",
       " 'Hessians': 'NOUN',\n",
       " 'quivering': 'VERB',\n",
       " 'dreamlike': 'ADJ',\n",
       " 'Midwood': 'NOUN',\n",
       " 'Participation': 'NOUN',\n",
       " 'Alpers': 'NOUN',\n",
       " 'inventories': 'NOUN',\n",
       " 'sad': 'ADJ',\n",
       " 'durable': 'ADJ',\n",
       " 'prancing': 'VERB',\n",
       " 'simplistic': 'ADJ',\n",
       " 'regards': 'VERB',\n",
       " 'drain': 'NOUN',\n",
       " 'liberation': 'NOUN',\n",
       " 'associated': 'VERB',\n",
       " 'turbinates': 'NOUN',\n",
       " 'slippers': 'NOUN',\n",
       " 'Welborn': 'NOUN',\n",
       " 'dispensary': 'NOUN',\n",
       " 'Forget': 'VERB',\n",
       " 'high-velocity': 'NOUN',\n",
       " 'creamy': 'ADJ',\n",
       " 'Coast': 'NOUN',\n",
       " 'loosely-taped': 'ADJ',\n",
       " 'Ozon': 'NOUN',\n",
       " 'million': 'NUM',\n",
       " 'judgments': 'NOUN',\n",
       " 'pacifies': 'VERB',\n",
       " 'boarder': 'NOUN',\n",
       " \"Governor's\": 'NOUN',\n",
       " 'rabbit': 'NOUN',\n",
       " 'straight-backed': 'ADJ',\n",
       " 'dictates': 'NOUN',\n",
       " 'prefabricated': 'VERB',\n",
       " 'wrenches': 'NOUN',\n",
       " 'Annapolis': 'NOUN',\n",
       " 'Yen': 'NOUN',\n",
       " 'Nazis': 'NOUN',\n",
       " '4-d': 'NUM',\n",
       " 'flashy': 'ADJ',\n",
       " 'Customs': 'NOUN',\n",
       " 'mastery': 'NOUN',\n",
       " 'lemonade': 'NOUN',\n",
       " 'maiden': 'NOUN',\n",
       " \"Ma'am\": 'NOUN',\n",
       " 'Kililngsworth': 'NOUN',\n",
       " \"comet's-tail\": 'NOUN',\n",
       " 'Undismayed': 'ADJ',\n",
       " 'hindrances': 'NOUN',\n",
       " 'self-consciousness': 'NOUN',\n",
       " 'Hillyer': 'NOUN',\n",
       " 'romping': 'VERB',\n",
       " 'trotted': 'VERB',\n",
       " 'Cap': 'NOUN',\n",
       " '1,212,000': 'NUM',\n",
       " 'handles': 'VERB',\n",
       " 'spirits': 'NOUN',\n",
       " 'Hwa-Shan': 'NOUN',\n",
       " 'elevates': 'VERB',\n",
       " 'camper': 'NOUN',\n",
       " '$16.00': 'NOUN',\n",
       " 'gowns': 'NOUN',\n",
       " \"Greece's\": 'NOUN',\n",
       " 'mechanization': 'NOUN',\n",
       " 'Comparable': 'ADJ',\n",
       " 'retied': 'VERB',\n",
       " 'Gursel': 'NOUN',\n",
       " 'personifies': 'VERB',\n",
       " 'ransacking': 'VERB',\n",
       " 'divinities': 'NOUN',\n",
       " 'Technology': 'NOUN',\n",
       " 'restrains': 'VERB',\n",
       " 'goal': 'NOUN',\n",
       " 'eye': 'NOUN',\n",
       " \"cap'n\": 'NOUN',\n",
       " 'halides': 'NOUN',\n",
       " 'patriotic': 'ADJ',\n",
       " 'subsoil': 'NOUN',\n",
       " 'canteen': 'NOUN',\n",
       " 'bonzes': 'NOUN',\n",
       " 'secretarial': 'ADJ',\n",
       " 'autocracies': 'NOUN',\n",
       " 'sincerest': 'ADJ',\n",
       " 'tea': 'NOUN',\n",
       " \"Justice's\": 'NOUN',\n",
       " 'Naked': 'ADJ',\n",
       " 'Torpetius': 'NOUN',\n",
       " 'pool-equipment': 'NOUN',\n",
       " 'boss': 'NOUN',\n",
       " 'heredity': 'NOUN',\n",
       " 'Notice': 'VERB',\n",
       " 'urban-fringe': 'NOUN',\n",
       " 'debt': 'NOUN',\n",
       " 'jeers': 'VERB',\n",
       " 'Pink': 'ADJ',\n",
       " 'Nursing': 'VERB',\n",
       " 'seducer': 'NOUN',\n",
       " 'familiarness': 'NOUN',\n",
       " 'cement': 'NOUN',\n",
       " 'Relatives': 'NOUN',\n",
       " 'Jason': 'NOUN',\n",
       " 'slashes': 'NOUN',\n",
       " \"Trustees'\": 'NOUN',\n",
       " 'divination': 'NOUN',\n",
       " 'paean': 'NOUN',\n",
       " 'Wildhack': 'NOUN',\n",
       " 'unrealistic': 'ADJ',\n",
       " 'basting': 'VERB',\n",
       " '1,107': 'NUM',\n",
       " 'loss': 'NOUN',\n",
       " 'barely': 'ADV',\n",
       " 'overeating': 'VERB',\n",
       " 'entering': 'VERB',\n",
       " 'bookers': 'NOUN',\n",
       " 'Booker': 'NOUN',\n",
       " 'formulaic': 'ADJ',\n",
       " 'dines': 'VERB',\n",
       " 'Hastening': 'VERB',\n",
       " 'Weaning': 'VERB',\n",
       " 'grub': 'NOUN',\n",
       " 'metronome': 'NOUN',\n",
       " 'fix': 'VERB',\n",
       " \"family's\": 'NOUN',\n",
       " 'robbery': 'NOUN',\n",
       " 'concurred': 'VERB',\n",
       " 'Childe': 'NOUN',\n",
       " 'Vegas': 'NOUN',\n",
       " '1947': 'NUM',\n",
       " 'hubba': 'PRT',\n",
       " 'antisubmarine': 'ADJ',\n",
       " 'birdied': 'VERB',\n",
       " 'latched': 'VERB',\n",
       " 'liquids': 'NOUN',\n",
       " 'lass': 'NOUN',\n",
       " 'Examination': 'NOUN',\n",
       " 'Rickards': 'NOUN',\n",
       " 'invisibly': 'ADV',\n",
       " 'stair': 'NOUN',\n",
       " 'regulative': 'ADJ',\n",
       " 'low-down': 'NOUN',\n",
       " 'investigators': 'NOUN',\n",
       " 'Almighty': 'NOUN',\n",
       " 'bassinet': 'NOUN',\n",
       " 'Judeo-Christian': 'ADJ',\n",
       " 'grin': 'NOUN',\n",
       " 'advertisers': 'NOUN',\n",
       " 'Magnificent': 'ADJ',\n",
       " 'Rinker': 'NOUN',\n",
       " 'upraised': 'VERB',\n",
       " 'outsmarted': 'VERB',\n",
       " 'enthalpy': 'NOUN',\n",
       " 'nuns': 'NOUN',\n",
       " 'Applying': 'VERB',\n",
       " 'Truck': 'NOUN',\n",
       " 'PABA': 'NOUN',\n",
       " 'newsletter': 'NOUN',\n",
       " \"catcher's\": 'NOUN',\n",
       " 'defend': 'VERB',\n",
       " 'generals': 'NOUN',\n",
       " 'Husky': 'ADJ',\n",
       " \"'tain't\": 'PRT',\n",
       " 'license': 'NOUN',\n",
       " 'credit': 'NOUN',\n",
       " 'Scots': 'NOUN',\n",
       " 'disking': 'VERB',\n",
       " 'death': 'NOUN',\n",
       " 'one-gee': 'ADJ',\n",
       " 'spanned': 'VERB',\n",
       " 'outraged': 'VERB',\n",
       " 'pollen-and-nectar': 'NOUN',\n",
       " 'mistakes': 'NOUN',\n",
       " 'fire-fighting': 'ADJ',\n",
       " 'mice': 'NOUN',\n",
       " 'self-indulgence': 'NOUN',\n",
       " 'Oil': 'NOUN',\n",
       " 'prize-fight': 'NOUN',\n",
       " 'Pratt': 'NOUN',\n",
       " 'enzyme': 'NOUN',\n",
       " 'Citing': 'VERB',\n",
       " 'cobalt': 'NOUN',\n",
       " 'counseling': 'VERB',\n",
       " 'questions': 'NOUN',\n",
       " 'Falstaff': 'NOUN',\n",
       " 'mono-': 'ADJ',\n",
       " 'experiments': 'NOUN',\n",
       " 'dynamo': 'NOUN',\n",
       " 'bars': 'NOUN',\n",
       " 'twirled': 'VERB',\n",
       " 'slept': 'VERB',\n",
       " \"'?'\": 'NOUN',\n",
       " 'E.': 'NOUN',\n",
       " 'boosted': 'VERB',\n",
       " 'ensure': 'VERB',\n",
       " 'sentimentalize': 'VERB',\n",
       " 'Reb': 'NOUN',\n",
       " 'pug-nosed': 'ADJ',\n",
       " 'cliche': 'NOUN',\n",
       " 'ego-adaptive': 'ADJ',\n",
       " \"company's\": 'NOUN',\n",
       " 'segregating': 'VERB',\n",
       " 'wars': 'NOUN',\n",
       " 'recruited': 'VERB',\n",
       " 'Istanbul': 'NOUN',\n",
       " 'filched': 'VERB',\n",
       " 'Inherently': 'ADV',\n",
       " 'Mecca': 'NOUN',\n",
       " 'temporary': 'ADJ',\n",
       " \"what's\": 'PRT',\n",
       " '$46.7': 'NOUN',\n",
       " 'acquittal': 'NOUN',\n",
       " 'transferred': 'VERB',\n",
       " 'cross-top': 'NOUN',\n",
       " 'Johnny': 'NOUN',\n",
       " 'Matisse': 'NOUN',\n",
       " 'Advice': 'NOUN',\n",
       " 'ipso': 'X',\n",
       " 'explorations': 'NOUN',\n",
       " 'Dodington': 'NOUN',\n",
       " 'Kelseyville': 'NOUN',\n",
       " 'caviar': 'NOUN',\n",
       " 'Lighting': 'VERB',\n",
       " 'skeletal': 'ADJ',\n",
       " 'Emerging': 'VERB',\n",
       " 'Raft': 'NOUN',\n",
       " 'manuscript': 'NOUN',\n",
       " 'consultative': 'ADJ',\n",
       " 'Fought': 'VERB',\n",
       " '1846': 'NUM',\n",
       " 'Mahzeer': 'NOUN',\n",
       " 'almost': 'ADV',\n",
       " 'stimulations': 'NOUN',\n",
       " 'beatific': 'ADJ',\n",
       " \"weeks'\": 'NOUN',\n",
       " '2,758': 'NUM',\n",
       " 'gully': 'NOUN',\n",
       " \"attacker's\": 'NOUN',\n",
       " 'Hilo': 'NOUN',\n",
       " 'littering': 'VERB',\n",
       " 'glanced': 'VERB',\n",
       " 'doc': 'NOUN',\n",
       " 'Pallavicini': 'NOUN',\n",
       " 'Sunday-school': 'ADJ',\n",
       " '2:25': 'NUM',\n",
       " 'implied': 'VERB',\n",
       " 'workings': 'NOUN',\n",
       " 'unfounded': 'ADJ',\n",
       " 'sonofabitch': 'NOUN',\n",
       " 'hackneyed': 'ADJ',\n",
       " 'Wolstenholme': 'NOUN',\n",
       " 'gears': 'NOUN',\n",
       " 'boarding': 'VERB',\n",
       " 'quintets': 'NOUN',\n",
       " 'exerting': 'VERB',\n",
       " 'farmed': 'VERB',\n",
       " 'biographer': 'NOUN',\n",
       " \"Holmes'\": 'NOUN',\n",
       " 'runways': 'NOUN',\n",
       " 'inquiring': 'VERB',\n",
       " 'thoroughness': 'NOUN',\n",
       " 'whispered': 'VERB',\n",
       " \"Nate's\": 'NOUN',\n",
       " 'ab': 'NOUN',\n",
       " 'goutte': 'X',\n",
       " 'police': 'NOUN',\n",
       " 'Candy': 'NOUN',\n",
       " \"W.'s\": 'PRT',\n",
       " \"Myra's\": 'NOUN',\n",
       " 'insights': 'NOUN',\n",
       " 'courteously': 'ADV',\n",
       " 'vaccine': 'NOUN',\n",
       " 'Philco-sponsored': 'ADJ',\n",
       " 'influenza': 'NOUN',\n",
       " 'death-locked': 'ADJ',\n",
       " 'aviator': 'NOUN',\n",
       " 'strokes': 'NOUN',\n",
       " 'J.D.H.': 'NOUN',\n",
       " 'Koenigsberg': 'NOUN',\n",
       " 'impatience': 'NOUN',\n",
       " \"Lopatnikoff's\": 'NOUN',\n",
       " 'Reflecting': 'VERB',\n",
       " 'romancers': 'NOUN',\n",
       " 'Uncertainty': 'NOUN',\n",
       " 'BG': 'NOUN',\n",
       " 'Domina': 'X',\n",
       " 'affiliated': 'VERB',\n",
       " 'Perfect': 'ADJ',\n",
       " 'declaring': 'VERB',\n",
       " 'weariness': 'NOUN',\n",
       " 'Fantastic': 'ADJ',\n",
       " 'bottle': 'NOUN',\n",
       " 'compete': 'VERB',\n",
       " 'thirty-foot': 'ADJ',\n",
       " 'listening': 'VERB',\n",
       " 'moon': 'NOUN',\n",
       " 'genesis': 'NOUN',\n",
       " 'twilight': 'NOUN',\n",
       " 'hardy': 'ADJ',\n",
       " 'half-light': 'NOUN',\n",
       " 'Oakland': 'NOUN',\n",
       " 'layout': 'NOUN',\n",
       " 'Whipsnade': 'NOUN',\n",
       " 'Kearton': 'NOUN',\n",
       " '1865': 'NUM',\n",
       " 'compatible': 'ADJ',\n",
       " 'Gorky': 'NOUN',\n",
       " 'whatever': 'DET',\n",
       " 'intersecting': 'VERB',\n",
       " 'Motor': 'NOUN',\n",
       " 'bockwurst': 'X',\n",
       " 'Elbow': 'NOUN',\n",
       " 'esprit': 'X',\n",
       " 'Aylesbury': 'NOUN',\n",
       " 'originate': 'VERB',\n",
       " 'crotchety': 'ADJ',\n",
       " 'priceless': 'ADJ',\n",
       " 'nieces': 'NOUN',\n",
       " 'bellow': 'NOUN',\n",
       " 'briskly': 'ADV',\n",
       " 'Somebody': 'NOUN',\n",
       " 'valleys': 'NOUN',\n",
       " 'lockup': 'NOUN',\n",
       " 'Councilwoman': 'NOUN',\n",
       " 'Dissect': 'VERB',\n",
       " 'multi-': 'ADJ',\n",
       " 'ball': 'NOUN',\n",
       " 'steam': 'NOUN',\n",
       " 'Clarke': 'NOUN',\n",
       " \"50'\": 'NOUN',\n",
       " 'presumptions': 'NOUN',\n",
       " 'creased': 'VERB',\n",
       " 'Feis': 'NOUN',\n",
       " 'workbench': 'NOUN',\n",
       " 'galvanizing': 'VERB',\n",
       " 'self-reliant': 'ADJ',\n",
       " 'wide-cut': 'ADJ',\n",
       " 'drummers': 'NOUN',\n",
       " 'peculiar': 'ADJ',\n",
       " 'perpetually': 'ADV',\n",
       " 'nuper': 'X',\n",
       " 'Bangs': 'NOUN',\n",
       " 'nod': 'NOUN',\n",
       " 'Raw': 'ADJ',\n",
       " 'communication': 'NOUN',\n",
       " 'trunk': 'NOUN',\n",
       " 'peculiarly': 'ADV',\n",
       " 'Westminster': 'NOUN',\n",
       " 'Zeitgeist': 'X',\n",
       " 'upstate': 'ADV',\n",
       " 'Virginians': 'NOUN',\n",
       " 'chinless': 'ADJ',\n",
       " \"Kaiser's\": 'NOUN',\n",
       " 'trellises': 'NOUN',\n",
       " '34': 'NUM',\n",
       " 'fixing': 'VERB',\n",
       " 'newsletters': 'NOUN',\n",
       " 'price': 'NOUN',\n",
       " 'experimenting': 'VERB',\n",
       " 'weightlessness': 'NOUN',\n",
       " 'platoon': 'NOUN',\n",
       " 'washed-out': 'ADJ',\n",
       " 'Opening': 'VERB',\n",
       " 'reins': 'NOUN',\n",
       " 'Aderholds': 'NOUN',\n",
       " 'Ramillies': 'NOUN',\n",
       " 'all-married': 'ADJ',\n",
       " 'abreast': 'ADV',\n",
       " \"patronne's\": 'X',\n",
       " '3.15': 'NUM',\n",
       " '1745': 'NUM',\n",
       " 'wavelength': 'NOUN',\n",
       " 'inserts': 'NOUN',\n",
       " 'go-to-war': 'ADJ',\n",
       " 'Capitol': 'NOUN',\n",
       " 'curettage': 'NOUN',\n",
       " 'tangibly': 'ADV',\n",
       " 'Arterial': 'ADJ',\n",
       " 'Eaton': 'NOUN',\n",
       " 'Burle': 'NOUN',\n",
       " 'Erik': 'NOUN',\n",
       " 'premiere': 'NOUN',\n",
       " 'view': 'NOUN',\n",
       " '420': 'NUM',\n",
       " 'sensation': 'NOUN',\n",
       " 'clapping': 'VERB',\n",
       " 'Vieth': 'NOUN',\n",
       " 'reproaches': 'VERB',\n",
       " 'puerile': 'ADJ',\n",
       " 'Knickerbocker': 'NOUN',\n",
       " 'quantitatively': 'ADV',\n",
       " \"Caravan's\": 'NOUN',\n",
       " 'effectively': 'ADV',\n",
       " 'log-house': 'NOUN',\n",
       " 'less-hurried': 'ADJ',\n",
       " 'Vouillemont': 'NOUN',\n",
       " 'crave': 'VERB',\n",
       " 'unaccountable': 'ADJ',\n",
       " 'carriage': 'NOUN',\n",
       " 'Southfield': 'NOUN',\n",
       " 'extracted': 'VERB',\n",
       " 'phloem': 'NOUN',\n",
       " 'Publicly': 'ADV',\n",
       " 'mid-September': 'NOUN',\n",
       " 'blind': 'ADJ',\n",
       " 'Walsh': 'NOUN',\n",
       " 'compressor': 'NOUN',\n",
       " 'Oceania': 'NOUN',\n",
       " 'Liquid': 'NOUN',\n",
       " 'lush': 'ADJ',\n",
       " 'playwrights': 'NOUN',\n",
       " 'pinks': 'NOUN',\n",
       " 'Mack': 'NOUN',\n",
       " 'lawful': 'ADJ',\n",
       " 'stop': 'VERB',\n",
       " 'Iroquois': 'NOUN',\n",
       " 'adopts': 'VERB',\n",
       " 'psyche': 'NOUN',\n",
       " 'offers': 'VERB',\n",
       " 'lodging': 'VERB',\n",
       " 'Statistique': 'X',\n",
       " 'chromatographic': 'ADJ',\n",
       " 'disqualify': 'VERB',\n",
       " 'Beronio': 'NOUN',\n",
       " 'repair': 'NOUN',\n",
       " 'criminality': 'NOUN',\n",
       " 'Verreau': 'NOUN',\n",
       " 'Mali': 'NOUN',\n",
       " 'always': 'ADV',\n",
       " 'bygone': 'ADJ',\n",
       " 'philologists': 'NOUN',\n",
       " 'SanAntonio': 'NOUN',\n",
       " 'Schweitzer': 'NOUN',\n",
       " 'deposition': 'NOUN',\n",
       " 'emphatically': 'ADV',\n",
       " 'Tossing': 'VERB',\n",
       " 'dialects': 'NOUN',\n",
       " 'advantageously': 'ADV',\n",
       " 'about-faced': 'VERB',\n",
       " 'catheter': 'NOUN',\n",
       " 'or': 'CONJ',\n",
       " 'interrelationships': 'NOUN',\n",
       " 'anyway': 'ADV',\n",
       " 'Baltic': 'NOUN',\n",
       " 'prophesies': 'VERB',\n",
       " 'contending': 'VERB',\n",
       " '37,470': 'NUM',\n",
       " 'disasters': 'NOUN',\n",
       " 'deductive': 'ADJ',\n",
       " 'parade': 'NOUN',\n",
       " 'Elios': 'NOUN',\n",
       " 'I-E': 'NOUN',\n",
       " 'settlement': 'NOUN',\n",
       " 'Mickey': 'NOUN',\n",
       " 'classificatory': 'ADJ',\n",
       " 'thatt': 'ADP',\n",
       " 'kazoo': 'NOUN',\n",
       " 'tax-exemption': 'NOUN',\n",
       " 'moodily': 'ADV',\n",
       " 'Larkin': 'NOUN',\n",
       " 'diva': 'NOUN',\n",
       " 'redwoods': 'NOUN',\n",
       " 'hesitating': 'VERB',\n",
       " 'Khrushchev': 'NOUN',\n",
       " 'legato': 'NOUN',\n",
       " 'sanding': 'VERB',\n",
       " 'center-punch': 'VERB',\n",
       " 'Croydon': 'NOUN',\n",
       " 'conjoined': 'VERB',\n",
       " 'intrude': 'VERB',\n",
       " 'Coyotes': 'NOUN',\n",
       " 'fueled': 'VERB',\n",
       " 'Prime': 'ADJ',\n",
       " 'sheriffs': 'NOUN',\n",
       " 'reflectance': 'NOUN',\n",
       " 'readily': 'ADV',\n",
       " 'screening': 'NOUN',\n",
       " 'remarkably': 'ADV',\n",
       " 'abiding': 'VERB',\n",
       " \"Conrad's\": 'NOUN',\n",
       " 'portwatchers': 'NOUN',\n",
       " 'self-centered': 'ADJ',\n",
       " 'Pulling': 'VERB',\n",
       " 'riding': 'VERB',\n",
       " 'smuggled': 'VERB',\n",
       " 'Birmingham': 'NOUN',\n",
       " 'cons': 'NOUN',\n",
       " 'tinkled': 'VERB',\n",
       " 'pax-ordo': 'X',\n",
       " 'content': 'NOUN',\n",
       " 'crypt': 'NOUN',\n",
       " 'pleasantly': 'ADV',\n",
       " 'gluttons': 'NOUN',\n",
       " 'NW': 'NOUN',\n",
       " 'Chambers': 'NOUN',\n",
       " 'refractory': 'ADJ',\n",
       " 'Mohammed': 'NOUN',\n",
       " 'Parrot': 'NOUN',\n",
       " 'dramatist': 'NOUN',\n",
       " 'self-unloading': 'ADJ',\n",
       " 'pegboard': 'NOUN',\n",
       " '$250,000': 'NOUN',\n",
       " 'altho': 'ADP',\n",
       " 'rejecting': 'VERB',\n",
       " 'understand': 'VERB',\n",
       " 'worn': 'VERB',\n",
       " 'Redundant': 'ADJ',\n",
       " 'orphanage': 'NOUN',\n",
       " 'geographically': 'ADV',\n",
       " 'broiled': 'VERB',\n",
       " 'gambit': 'NOUN',\n",
       " \"gang's\": 'NOUN',\n",
       " 'Princeton': 'NOUN',\n",
       " 'guises': 'NOUN',\n",
       " 'private': 'ADJ',\n",
       " 'psychiatry': 'NOUN',\n",
       " 'Leona': 'NOUN',\n",
       " 'Stratton': 'NOUN',\n",
       " 'prayer-time': 'NOUN',\n",
       " 'anti-B': 'ADJ',\n",
       " 'sportsmen': 'NOUN',\n",
       " '10-milligram': 'ADJ',\n",
       " 'Cannibal': 'NOUN',\n",
       " '1886': 'NUM',\n",
       " 'Hanover-Chalidale': 'NOUN',\n",
       " 'deformities': 'NOUN',\n",
       " 'catsup': 'NOUN',\n",
       " 'initially': 'ADV',\n",
       " \"Berman's\": 'NOUN',\n",
       " 'envelopes': 'NOUN',\n",
       " 'strictures': 'NOUN',\n",
       " 'Factors': 'NOUN',\n",
       " 'breakwaters': 'NOUN',\n",
       " 'Carletonian': 'NOUN',\n",
       " 'imputation': 'NOUN',\n",
       " 'Ella': 'NOUN',\n",
       " 'sufferings': 'NOUN',\n",
       " 'Newburger': 'NOUN',\n",
       " 'co-ordinating': 'VERB',\n",
       " \"Indiana's\": 'NOUN',\n",
       " '20,000': 'NUM',\n",
       " 'furthered': 'VERB',\n",
       " 'Clyfford': 'NOUN',\n",
       " ...}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfc_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Predictions with a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_unknown(sequence):\n",
    "\n",
    "    return [w if w in data.training_set.vocab else 'nan' for w in sequence]\n",
    "\n",
    "def simplify_decoding(X, model):\n",
    "    _, state_path = model.viterbi(replace_unknown(X))\n",
    "    return [state[1].name for state in state_path[1:-1]]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Decoding Sequences with MFC Tagger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence Key: b100-28144\n",
      "\n",
      "Predicted labels:\n",
      "-----------------\n",
      "['CONJ', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'CONJ', 'NOUN', 'NUM', '.', '.', 'NOUN', '.', '.']\n",
      "\n",
      "Actual labels:\n",
      "--------------\n",
      "('CONJ', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'CONJ', 'NOUN', 'NUM', '.', '.', 'NOUN', '.', '.')\n",
      "\n",
      "\n",
      "Sentence Key: b100-23146\n",
      "\n",
      "Predicted labels:\n",
      "-----------------\n",
      "['PRON', 'VERB', 'DET', 'NOUN', 'ADP', 'ADJ', 'ADJ', 'NOUN', 'VERB', 'VERB', '.', 'ADP', 'VERB', 'DET', 'NOUN', 'ADP', 'NOUN', 'ADP', 'DET', 'NOUN', '.']\n",
      "\n",
      "Actual labels:\n",
      "--------------\n",
      "('PRON', 'VERB', 'DET', 'NOUN', 'ADP', 'ADJ', 'ADJ', 'NOUN', 'VERB', 'VERB', '.', 'ADP', 'VERB', 'DET', 'NOUN', 'ADP', 'NOUN', 'ADP', 'DET', 'NOUN', '.')\n",
      "\n",
      "\n",
      "Sentence Key: b100-35462\n",
      "\n",
      "Predicted labels:\n",
      "-----------------\n",
      "['DET', 'ADJ', 'NOUN', 'VERB', 'VERB', 'VERB', 'ADP', 'DET', 'ADJ', 'ADJ', 'NOUN', 'ADP', 'DET', 'ADJ', 'NOUN', '.', 'ADP', 'ADJ', 'NOUN', '.', 'CONJ', 'ADP', 'DET', '<MISSING>', 'ADP', 'ADJ', 'ADJ', '.', 'ADJ', '.', 'CONJ', 'ADJ', 'NOUN', 'ADP', 'ADV', 'NOUN', '.']\n",
      "\n",
      "Actual labels:\n",
      "--------------\n",
      "('DET', 'ADJ', 'NOUN', 'VERB', 'VERB', 'VERB', 'ADP', 'DET', 'ADJ', 'ADJ', 'NOUN', 'ADP', 'DET', 'ADJ', 'NOUN', '.', 'ADP', 'ADJ', 'NOUN', '.', 'CONJ', 'ADP', 'DET', 'NOUN', 'ADP', 'ADJ', 'ADJ', '.', 'ADJ', '.', 'CONJ', 'ADJ', 'NOUN', 'ADP', 'ADJ', 'NOUN', '.')\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for key in data.testing_set.keys[:3]:\n",
    "    print(\"Sentence Key: {}\\n\".format(key))\n",
    "    print(\"Predicted labels:\\n-----------------\")\n",
    "    print(simplify_decoding(data.sentences[key].words, mfc_model))\n",
    "    \n",
    "    print()\n",
    "    print(\"Actual labels:\\n--------------\")\n",
    "    print(data.sentences[key].tags)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating Model Accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(X, Y, model):\n",
    "    correct = total_predictions = 0\n",
    "    for observations, actual_tags in zip(X, Y):\n",
    "\n",
    "        try:\n",
    "            most_likely_tags = simplify_decoding(observations, model)\n",
    "            \n",
    "            correct += sum(p == t for p, t in zip(most_likely_tags, actual_tags))\n",
    "        except:\n",
    "            pass\n",
    "        total_predictions += len(observations)\n",
    "    return correct / total_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the accuracy of the MFC tagger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy mfc_model: 95.71%\n",
      "testing accuracy mfc_model: 93.13%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"alert alert-block alert-success\">Your MFC tagger accuracy looks correct!</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfc_training_acc = accuracy(data.training_set.X, data.training_set.Y, mfc_model)\n",
    "print(\"training accuracy mfc_model: {:.2f}%\".format(100 * mfc_training_acc))\n",
    "\n",
    "mfc_testing_acc = accuracy(data.testing_set.X, data.testing_set.Y, mfc_model)\n",
    "print(\"testing accuracy mfc_model: {:.2f}%\".format(100 * mfc_testing_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bigram Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"alert alert-block alert-success\">Your tag bigrams look good!</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import itertools\n",
    "def pairwise(iterable):\n",
    "    t, t_1 = itertools.tee(iterable)\n",
    "    next(t_1, 'end')\n",
    "    return zip(t, t_1)\n",
    "\n",
    "def bigram_counts(sequences):\n",
    "    return(dict(Counter(pairwise(sequences))))\n",
    "\n",
    "tag_bigrams = bigram_counts(tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequence Starting Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"alert alert-block alert-success\">Your starting tag counts look good!</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def starting_counts(sequences):\n",
    "\n",
    "    return(dict(Counter([i[0] for i in sequences])))\n",
    "\n",
    "tags_label= data.Y\n",
    "tag_starts = starting_counts(tags_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NOUN': 8093,\n",
       " 'CONJ': 2817,\n",
       " 'PRT': 2103,\n",
       " 'DET': 12238,\n",
       " 'PRON': 9157,\n",
       " 'ADP': 7044,\n",
       " 'ADJ': 1969,\n",
       " 'ADV': 5238,\n",
       " 'VERB': 2588,\n",
       " 'NUM': 964,\n",
       " '.': 5099,\n",
       " 'X': 30}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_starts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequence Ending Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"alert alert-block alert-success\">Your ending tag counts look good!</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def ending_counts(sequences):\n",
    "    return(dict(Counter([i[-1] for i in sequences])))\n",
    "\n",
    "tag_ends = ending_counts(tags_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'.': 56149,\n",
       " 'ADJ': 31,\n",
       " 'ADV': 20,\n",
       " 'NOUN': 914,\n",
       " 'NUM': 80,\n",
       " 'PRON': 5,\n",
       " 'ADP': 8,\n",
       " 'VERB': 102,\n",
       " 'DET': 18,\n",
       " 'PRT': 9,\n",
       " 'CONJ': 2,\n",
       " 'X': 2}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_ends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic HMM Tagger\n",
    "- Use the tag unigrams and bigrams calculated above to construct a hidden Markov tagger.\n",
    "\n",
    "- Add one state per tag\n",
    "    - The emission distribution at each state should be estimated with the formula: $P(w|t) = \\frac{C(t, w)}{C(t)}$\n",
    "- Add an edge from the starting state `basic_model.start` to each tag\n",
    "    - The transition probability should be estimated with the formula: $P(t|start) = \\frac{C(start, t)}{C(start)}$\n",
    "- Add an edge from each tag to the end state `basic_model.end`\n",
    "    - The transition probability should be estimated with the formula: $P(end|t) = \\frac{C(t, end)}{C(t)}$\n",
    "- Add an edge between _every_ pair of tags\n",
    "    - The transition probability should be estimated with the formula: $P(t_2|t_1) = \\frac{C(t_1, t_2)}{C(t_1)}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_model = HiddenMarkovModel(name=\"base-hmm-tagger\")\n",
    "\n",
    "tags = [tag for _, tag in data.stream()]\n",
    "words = [word for word, _ in data.stream()]\n",
    "emission_counts = pair_counts(tags, words)\n",
    "states = {}\n",
    "\n",
    "for pos_tag in data.tagset:\n",
    "    emission_probabilities = dict()\n",
    "    \n",
    "    for word, occurance in emission_counts[pos_tag].items(): \n",
    "        emission_probabilities[word] = occurance / tag_unigrams[pos_tag] \n",
    "\n",
    "    tag_distribution = DiscreteDistribution(emission_probabilities) \n",
    "    state = State(tag_distribution, name=pos_tag)\n",
    "    states[pos_tag] = state\n",
    "    basic_model.add_state(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': 0.03536372193088611,\n",
       " 'One': 0.023060373806642463,\n",
       " '1957': 0.003227107704719645,\n",
       " 'Six': 0.00154632244184483,\n",
       " '2:30.3-:36.1': 0.0001344628210299852,\n",
       " 'two': 0.0881403791851553,\n",
       " 'one': 0.16061583972031734,\n",
       " '26': 0.002151405136479763,\n",
       " '2000': 0.000336157052574963,\n",
       " 'twenty-one': 0.00047061987360494823,\n",
       " '3': 0.019160951996772892,\n",
       " '4': 0.013849670566088476,\n",
       " '1721': 0.0001344628210299852,\n",
       " '6': 0.007664380798709157,\n",
       " '500': 0.0010757025682398816,\n",
       " '2100': 0.0002016942315449778,\n",
       " 'six': 0.01317735646093855,\n",
       " 'three': 0.03717897001479091,\n",
       " '1883': 0.0004033884630899556,\n",
       " 'fifteen': 0.003227107704719645,\n",
       " 'forty': 0.002151405136479763,\n",
       " 'twenty': 0.00490789296759446,\n",
       " 'Twenty-four': 0.0002016942315449778,\n",
       " '20': 0.00618528976737932,\n",
       " '1932': 0.0002016942315449778,\n",
       " '707': 0.0002689256420599704,\n",
       " 'five': 0.01627000134462821,\n",
       " '25': 0.0055129756622293935,\n",
       " 'thirty': 0.003764958988839586,\n",
       " 'sixty': 0.0012101653892698668,\n",
       " '17': 0.0027564878311146968,\n",
       " 'nine': 0.004975124378109453,\n",
       " 'four': 0.02191743982788759,\n",
       " \"'49\": 0.0001344628210299852,\n",
       " '1960': 0.011563802608578729,\n",
       " '2': 0.02991797767917171,\n",
       " 'thousand': 0.006386983998924297,\n",
       " '10': 0.010219174398278876,\n",
       " '12': 0.00645421540943929,\n",
       " '14': 0.003764958988839586,\n",
       " '55': 0.0006050826946349334,\n",
       " '1788': 0.0002016942315449778,\n",
       " '2:04': 6.72314105149926e-05,\n",
       " '2:24': 6.72314105149926e-05,\n",
       " '61': 0.0002689256420599704,\n",
       " '1946': 0.001344628210299852,\n",
       " '22': 0.00309264488368966,\n",
       " '13': 0.0032943391152346377,\n",
       " '60': 0.00309264488368966,\n",
       " '1935': 0.000672314105149926,\n",
       " '28': 0.0014118596208148447,\n",
       " '1959': 0.006857603872529245,\n",
       " 'Fifteen': 0.0005378512841199408,\n",
       " '1950': 0.0014118596208148447,\n",
       " '375': 0.0002016942315449778,\n",
       " '161': 6.72314105149926e-05,\n",
       " '0.36': 6.72314105149926e-05,\n",
       " '0.16': 0.0001344628210299852,\n",
       " 'seventeen': 0.0014118596208148447,\n",
       " 'hundred': 0.011496571198063736,\n",
       " '540': 0.0001344628210299852,\n",
       " '1600': 0.0002016942315449778,\n",
       " 'eighty': 0.000672314105149926,\n",
       " 'one-': 0.000336157052574963,\n",
       " '8': 0.007126529514589216,\n",
       " '7': 0.006118058356864327,\n",
       " '1914': 0.0005378512841199408,\n",
       " '1915': 0.000336157052574963,\n",
       " 'ten': 0.010017480166733898,\n",
       " 'million': 0.01371520774505849,\n",
       " '1962': 0.0023530993680247413,\n",
       " '15': 0.007597149388194164,\n",
       " '50': 0.004101116041414549,\n",
       " '14-1/2': 0.0002689256420599704,\n",
       " '75': 0.001344628210299852,\n",
       " '104': 0.0008067769261799112,\n",
       " '5': 0.00954686029312895,\n",
       " 'Fourteen': 0.0002016942315449778,\n",
       " '1958': 0.006118058356864327,\n",
       " '790': 6.72314105149926e-05,\n",
       " '1900': 0.0008740083366949038,\n",
       " '21': 0.0031598762942046522,\n",
       " '30': 0.007529917977679172,\n",
       " '40': 0.002689256420599704,\n",
       " '5777': 6.72314105149926e-05,\n",
       " '6,000': 0.000336157052574963,\n",
       " '110': 0.00047061987360494823,\n",
       " 'twelve': 0.0030254134731746672,\n",
       " '1949': 0.001344628210299852,\n",
       " 'Nineteen': 0.0004033884630899556,\n",
       " 'Eighty-Four': 0.0002016942315449778,\n",
       " '101': 0.0001344628210299852,\n",
       " '11:20': 0.0002689256420599704,\n",
       " '9': 0.004168347451929542,\n",
       " 'Five': 0.0029581820626596745,\n",
       " 'eight': 0.006252521177894313,\n",
       " '16': 0.003630496167809601,\n",
       " '54,320': 6.72314105149926e-05,\n",
       " '642': 6.72314105149926e-05,\n",
       " '228': 6.72314105149926e-05,\n",
       " 'Ten': 0.0009412397472098965,\n",
       " '1980': 0.0002689256420599704,\n",
       " 'Ab4,500': 6.72314105149926e-05,\n",
       " '602.2': 6.72314105149926e-05,\n",
       " '260': 0.000336157052574963,\n",
       " '11,330': 6.72314105149926e-05,\n",
       " 'Seven': 0.0012773967997848595,\n",
       " '230': 0.0002689256420599704,\n",
       " '18': 0.003697727578324593,\n",
       " '33': 0.00047061987360494823,\n",
       " '08': 0.0001344628210299852,\n",
       " '0.906': 6.72314105149926e-05,\n",
       " '24': 0.003227107704719645,\n",
       " '2705': 6.72314105149926e-05,\n",
       " '1920': 0.0007395455156649186,\n",
       " 'Three': 0.0038321903993545786,\n",
       " 'eighty-five': 6.72314105149926e-05,\n",
       " '0.24': 6.72314105149926e-05,\n",
       " '0.28': 6.72314105149926e-05,\n",
       " '0.12': 6.72314105149926e-05,\n",
       " '1955': 0.001882479494419793,\n",
       " '1937': 0.000672314105149926,\n",
       " '11': 0.004168347451929542,\n",
       " '1,500': 0.0002016942315449778,\n",
       " 'Two': 0.0067903724620142535,\n",
       " '360': 0.0002689256420599704,\n",
       " '1543': 6.72314105149926e-05,\n",
       " '369': 6.72314105149926e-05,\n",
       " '23-36': 6.72314105149926e-05,\n",
       " '1626': 6.72314105149926e-05,\n",
       " 'one-thirty': 0.0001344628210299852,\n",
       " '1908': 0.0005378512841199408,\n",
       " '8861': 6.72314105149926e-05,\n",
       " '950': 6.72314105149926e-05,\n",
       " '2,418': 6.72314105149926e-05,\n",
       " '869': 6.72314105149926e-05,\n",
       " '2:32': 0.0001344628210299852,\n",
       " \"'52\": 0.0002016942315449778,\n",
       " 'seven': 0.0063197525884093044,\n",
       " '9-7': 6.72314105149926e-05,\n",
       " 'eighteen': 0.001008471157724889,\n",
       " 'nineteen': 0.0008067769261799112,\n",
       " '1907': 0.0002689256420599704,\n",
       " \"'13\": 0.0001344628210299852,\n",
       " '2500': 6.72314105149926e-05,\n",
       " '1.5': 0.0008067769261799112,\n",
       " '480': 0.0001344628210299852,\n",
       " '200': 0.002554793599569719,\n",
       " '2544': 6.72314105149926e-05,\n",
       " '1886': 0.0001344628210299852,\n",
       " '29': 0.0014790910313298372,\n",
       " '70': 0.0016135538523598225,\n",
       " '23': 0.0031598762942046522,\n",
       " '113': 0.0002016942315449778,\n",
       " '9.8': 0.0001344628210299852,\n",
       " '2:22': 0.0001344628210299852,\n",
       " '1961': 0.009009009009009009,\n",
       " '1936': 0.0004033884630899556,\n",
       " '1911': 0.0002016942315449778,\n",
       " '1906': 0.0002016942315449778,\n",
       " '1945': 0.0012101653892698668,\n",
       " '1848': 0.0002016942315449778,\n",
       " 'Four': 0.0022858679575097486,\n",
       " 'fifty': 0.0042355788624445345,\n",
       " 'fifty-odd': 0.0001344628210299852,\n",
       " '1902': 0.0002016942315449778,\n",
       " '1916': 0.000336157052574963,\n",
       " '1919': 0.0004033884630899556,\n",
       " '68': 0.0004033884630899556,\n",
       " '32,000': 6.72314105149926e-05,\n",
       " '2,700,877': 6.72314105149926e-05,\n",
       " '300': 0.0016807852628748152,\n",
       " 'thirty-four': 0.0004033884630899556,\n",
       " '80,000': 0.0001344628210299852,\n",
       " 'zero': 0.0011429339787548743,\n",
       " '400': 0.0012101653892698668,\n",
       " '8,280': 0.0001344628210299852,\n",
       " '1861': 0.0005378512841199408,\n",
       " '1859': 0.000672314105149926,\n",
       " '1577': 0.0001344628210299852,\n",
       " '355': 0.0001344628210299852,\n",
       " '149': 0.0001344628210299852,\n",
       " '126': 0.0002016942315449778,\n",
       " '187.5': 6.72314105149926e-05,\n",
       " '189': 0.0001344628210299852,\n",
       " '49': 0.0002016942315449778,\n",
       " '1793': 0.000336157052574963,\n",
       " '1479': 6.72314105149926e-05,\n",
       " '1923': 0.00047061987360494823,\n",
       " '3,000': 0.0006050826946349334,\n",
       " 'fourteen': 0.001882479494419793,\n",
       " '1948': 0.0014118596208148447,\n",
       " '42': 0.0010757025682398816,\n",
       " '1816': 6.72314105149926e-05,\n",
       " '1593': 0.0001344628210299852,\n",
       " 'billion': 0.004168347451929542,\n",
       " '1933': 0.0006050826946349334,\n",
       " '600': 0.0005378512841199408,\n",
       " '1607': 0.0001344628210299852,\n",
       " '1622': 6.72314105149926e-05,\n",
       " '98': 0.0002016942315449778,\n",
       " '3.5': 0.0002689256420599704,\n",
       " '21-22': 6.72314105149926e-05,\n",
       " '2:20': 0.0001344628210299852,\n",
       " '71': 0.000672314105149926,\n",
       " '2130': 6.72314105149926e-05,\n",
       " '1925': 0.0006050826946349334,\n",
       " '100': 0.005176818609654431,\n",
       " 'thirty-five': 0.000672314105149926,\n",
       " '1984': 6.72314105149926e-05,\n",
       " '1953': 0.0017480166733898077,\n",
       " '8:30': 0.0002689256420599704,\n",
       " '43': 0.000336157052574963,\n",
       " '150': 0.0006050826946349334,\n",
       " 'eighty-three': 0.0002016942315449778,\n",
       " '1801': 0.0002689256420599704,\n",
       " '80': 0.0012101653892698668,\n",
       " \"'48\": 0.0001344628210299852,\n",
       " '1927': 0.0012773967997848595,\n",
       " '3.15': 0.0002689256420599704,\n",
       " '10.3': 6.72314105149926e-05,\n",
       " '30-odd': 0.0001344628210299852,\n",
       " '1855': 0.0001344628210299852,\n",
       " '7599': 6.72314105149926e-05,\n",
       " 'Nineteen-sixty': 6.72314105149926e-05,\n",
       " '2:36': 0.0008067769261799112,\n",
       " '1951': 0.001344628210299852,\n",
       " '353': 0.000336157052574963,\n",
       " '607': 6.72314105149926e-05,\n",
       " '4:30': 0.0002016942315449778,\n",
       " '368(a)(1)': 0.0002016942315449778,\n",
       " '332': 0.0002016942315449778,\n",
       " '1959-1960': 0.0002016942315449778,\n",
       " '1868': 0.000336157052574963,\n",
       " '160': 0.000336157052574963,\n",
       " '270': 0.0001344628210299852,\n",
       " '1849': 0.0001344628210299852,\n",
       " '1819': 0.00047061987360494823,\n",
       " '1820': 0.0002016942315449778,\n",
       " '350': 0.0005378512841199408,\n",
       " '5-1': 0.0002016942315449778,\n",
       " '15-1': 6.72314105149926e-05,\n",
       " '1845': 0.000336157052574963,\n",
       " '111': 0.000336157052574963,\n",
       " '603': 6.72314105149926e-05,\n",
       " '452': 6.72314105149926e-05,\n",
       " '2.1': 0.0002689256420599704,\n",
       " '1888': 0.0004033884630899556,\n",
       " '1891': 0.0002689256420599704,\n",
       " '1827': 0.0001344628210299852,\n",
       " '85': 0.000672314105149926,\n",
       " '64': 0.0002016942315449778,\n",
       " '41': 0.0007395455156649186,\n",
       " '30-30': 6.72314105149926e-05,\n",
       " '1928': 0.000336157052574963,\n",
       " '145': 6.72314105149926e-05,\n",
       " '1885': 0.0002016942315449778,\n",
       " '1910': 0.0010757025682398816,\n",
       " '1909': 0.000336157052574963,\n",
       " '1944': 0.001008471157724889,\n",
       " '2.4': 0.0002016942315449778,\n",
       " '65': 0.0008740083366949038,\n",
       " '325': 6.72314105149926e-05,\n",
       " '1943': 0.0008067769261799112,\n",
       " '821,220': 6.72314105149926e-05,\n",
       " '4.3': 0.0002016942315449778,\n",
       " '40,000,000': 0.0001344628210299852,\n",
       " '1941': 0.0007395455156649186,\n",
       " '1000': 0.0007395455156649186,\n",
       " '1931': 0.0005378512841199408,\n",
       " 'Thousand': 0.0001344628210299852,\n",
       " '5-7': 0.0001344628210299852,\n",
       " '13.5': 0.0002016942315449778,\n",
       " '1940': 0.001008471157724889,\n",
       " '47.6': 6.72314105149926e-05,\n",
       " '130': 0.000336157052574963,\n",
       " 'mid-1948': 6.72314105149926e-05,\n",
       " '1,000': 0.0007395455156649186,\n",
       " '46': 0.0006050826946349334,\n",
       " '1040': 0.0005378512841199408,\n",
       " 'Twenty-two': 0.0001344628210299852,\n",
       " '70,000': 0.0002689256420599704,\n",
       " '1823': 6.72314105149926e-05,\n",
       " '06-05': 0.0001344628210299852,\n",
       " '192': 0.0001344628210299852,\n",
       " '0': 0.0014790910313298372,\n",
       " '95': 0.0004033884630899556,\n",
       " 'twenty-five': 0.00154632244184483,\n",
       " 'eleven': 0.002554793599569719,\n",
       " '140': 0.0002016942315449778,\n",
       " '5.1': 6.72314105149926e-05,\n",
       " '1966': 0.0002689256420599704,\n",
       " '1720': 6.72314105149926e-05,\n",
       " '2-4': 6.72314105149926e-05,\n",
       " '1.1': 0.0002016942315449778,\n",
       " '1926': 0.0004033884630899556,\n",
       " '1954': 0.00309264488368966,\n",
       " '3:300': 6.72314105149926e-05,\n",
       " '700': 0.0004033884630899556,\n",
       " '1947': 0.0009412397472098965,\n",
       " '963': 6.72314105149926e-05,\n",
       " '19': 0.002151405136479763,\n",
       " '704': 6.72314105149926e-05,\n",
       " '389': 6.72314105149926e-05,\n",
       " '1797': 0.0002016942315449778,\n",
       " '0600': 6.72314105149926e-05,\n",
       " '1956': 0.001882479494419793,\n",
       " '1550': 6.72314105149926e-05,\n",
       " '1777': 6.72314105149926e-05,\n",
       " '2:31': 6.72314105149926e-05,\n",
       " '36': 0.000672314105149926,\n",
       " '1938': 0.0005378512841199408,\n",
       " '11-5': 6.72314105149926e-05,\n",
       " '362': 0.0001344628210299852,\n",
       " '1223': 6.72314105149926e-05,\n",
       " '32': 0.0008740083366949038,\n",
       " 'Fifty-two': 6.72314105149926e-05,\n",
       " '1,418,000': 6.72314105149926e-05,\n",
       " '271': 0.0001344628210299852,\n",
       " '307': 6.72314105149926e-05,\n",
       " '8.6': 0.0001344628210299852,\n",
       " '0.075': 6.72314105149926e-05,\n",
       " '1924': 0.0006050826946349334,\n",
       " '1105': 0.00047061987360494823,\n",
       " '1912': 0.001008471157724889,\n",
       " '16,000': 0.0002016942315449778,\n",
       " '1844': 0.000336157052574963,\n",
       " 'twenty-four': 0.0007395455156649186,\n",
       " '1921': 0.0005378512841199408,\n",
       " '1922': 0.0005378512841199408,\n",
       " 'forty-three': 0.0001344628210299852,\n",
       " '2:34': 0.0002689256420599704,\n",
       " '5-4': 6.72314105149926e-05,\n",
       " '1815': 0.0004033884630899556,\n",
       " '99': 0.0005378512841199408,\n",
       " '52': 0.0002689256420599704,\n",
       " '5.5': 0.0002016942315449778,\n",
       " '8,000': 0.00047061987360494823,\n",
       " '10,000': 0.0009412397472098965,\n",
       " '2269': 6.72314105149926e-05,\n",
       " '22111': 6.72314105149926e-05,\n",
       " '1913': 0.0008067769261799112,\n",
       " '1750': 6.72314105149926e-05,\n",
       " '1833': 6.72314105149926e-05,\n",
       " '470': 6.72314105149926e-05,\n",
       " '605': 0.0001344628210299852,\n",
       " '542,250': 6.72314105149926e-05,\n",
       " '1789': 0.0002016942315449778,\n",
       " '4.2': 0.0002689256420599704,\n",
       " 'forty-two': 0.0001344628210299852,\n",
       " 'Nine': 0.00047061987360494823,\n",
       " '767': 6.72314105149926e-05,\n",
       " '59': 0.0002016942315449778,\n",
       " '2.21.6': 6.72314105149926e-05,\n",
       " '2:37': 0.00047061987360494823,\n",
       " '0.8': 0.0002689256420599704,\n",
       " '30,000': 0.000336157052574963,\n",
       " '1298': 6.72314105149926e-05,\n",
       " '38': 0.00047061987360494823,\n",
       " '1/16': 0.0001344628210299852,\n",
       " '0.5': 0.0006050826946349334,\n",
       " '1872': 0.0002016942315449778,\n",
       " '1942': 0.0007395455156649186,\n",
       " 'Twenty': 0.00047061987360494823,\n",
       " '31': 0.002554793599569719,\n",
       " '34': 0.0014118596208148447,\n",
       " '2-1/2': 0.0002016942315449778,\n",
       " '6-3-3': 6.72314105149926e-05,\n",
       " '8-4': 6.72314105149926e-05,\n",
       " '142': 6.72314105149926e-05,\n",
       " '1930': 0.0007395455156649186,\n",
       " '1.00': 0.0001344628210299852,\n",
       " '1812': 0.0002689256420599704,\n",
       " '1813': 0.0001344628210299852,\n",
       " '1851': 0.0002689256420599704,\n",
       " '5,000': 0.00047061987360494823,\n",
       " '2:38': 0.0001344628210299852,\n",
       " '1643': 0.0002016942315449778,\n",
       " '39': 0.000336157052574963,\n",
       " '125': 0.0002016942315449778,\n",
       " '1642': 6.72314105149926e-05,\n",
       " '6-3': 0.0002016942315449778,\n",
       " '949': 6.72314105149926e-05,\n",
       " 'Twenty-five': 0.0001344628210299852,\n",
       " '1905': 0.0004033884630899556,\n",
       " 'Eighty': 6.72314105149926e-05,\n",
       " '1871': 6.72314105149926e-05,\n",
       " '2,000': 0.0005378512841199408,\n",
       " '1952': 0.002084173725964771,\n",
       " '122': 0.0001344628210299852,\n",
       " '1870': 0.0001344628210299852,\n",
       " '4.0': 6.72314105149926e-05,\n",
       " '3.4': 6.72314105149926e-05,\n",
       " '60-66': 6.72314105149926e-05,\n",
       " '2:30': 0.0002016942315449778,\n",
       " '4-d': 6.72314105149926e-05,\n",
       " '500,000': 0.0002016942315449778,\n",
       " '47': 0.0006050826946349334,\n",
       " '9230': 6.72314105149926e-05,\n",
       " '420': 0.0002016942315449778,\n",
       " '1838': 6.72314105149926e-05,\n",
       " '0.3': 0.0002016942315449778,\n",
       " '37': 0.00047061987360494823,\n",
       " '1601': 0.0001344628210299852,\n",
       " '124': 0.0001344628210299852,\n",
       " '90': 0.0007395455156649186,\n",
       " '1817': 0.0002689256420599704,\n",
       " '15-17': 6.72314105149926e-05,\n",
       " '154': 0.0002689256420599704,\n",
       " 'sixty-five': 0.00047061987360494823,\n",
       " '0.1': 0.0006050826946349334,\n",
       " 'twenty-eight': 0.0002689256420599704,\n",
       " 'Thirty': 0.0002016942315449778,\n",
       " '1610': 0.0002016942315449778,\n",
       " '27': 0.0017480166733898077,\n",
       " '1928-29': 6.72314105149926e-05,\n",
       " '1835': 0.0002016942315449778,\n",
       " '795,586': 6.72314105149926e-05,\n",
       " '3,399': 6.72314105149926e-05,\n",
       " '2,963': 6.72314105149926e-05,\n",
       " '1853': 6.72314105149926e-05,\n",
       " '2:32.2': 0.0001344628210299852,\n",
       " 'Eighteen': 0.0001344628210299852,\n",
       " '1/2': 0.0010757025682398816,\n",
       " '1938-39': 0.0001344628210299852,\n",
       " '15,000': 0.0001344628210299852,\n",
       " '20,000': 0.000336157052574963,\n",
       " '50,000': 6.72314105149926e-05,\n",
       " '45': 0.0009412397472098965,\n",
       " '100,000': 0.0006050826946349334,\n",
       " '1965': 0.000336157052574963,\n",
       " '38-7': 6.72314105149926e-05,\n",
       " '7.5': 0.0002689256420599704,\n",
       " '1963': 0.0005378512841199408,\n",
       " '1780': 0.0001344628210299852,\n",
       " 'sixty-one': 0.0005378512841199408,\n",
       " '1917': 0.000672314105149926,\n",
       " '6.9': 0.0001344628210299852,\n",
       " '1688': 6.72314105149926e-05,\n",
       " '90,000': 6.72314105149926e-05,\n",
       " '62-63': 6.72314105149926e-05,\n",
       " '431': 6.72314105149926e-05,\n",
       " 'hundred-and-fifty': 6.72314105149926e-05,\n",
       " '1714': 0.0001344628210299852,\n",
       " 'Twenty-eight': 6.72314105149926e-05,\n",
       " '1884': 6.72314105149926e-05,\n",
       " 'thirty-six': 0.0002016942315449778,\n",
       " '6,768': 6.72314105149926e-05,\n",
       " '7,360,187': 6.72314105149926e-05,\n",
       " '327': 6.72314105149926e-05,\n",
       " '114': 0.0002689256420599704,\n",
       " '7:30': 0.00047061987360494823,\n",
       " '2-56': 0.0005378512841199408,\n",
       " '1896': 0.0005378512841199408,\n",
       " '5000': 0.0002689256420599704,\n",
       " '4.00': 6.72314105149926e-05,\n",
       " '60-1': 6.72314105149926e-05,\n",
       " '6-1': 6.72314105149926e-05,\n",
       " '258': 0.0001344628210299852,\n",
       " '78': 0.0001344628210299852,\n",
       " '1731': 6.72314105149926e-05,\n",
       " '1,450,000': 6.72314105149926e-05,\n",
       " '275': 0.0002689256420599704,\n",
       " 'six-four': 6.72314105149926e-05,\n",
       " '30-': 0.0001344628210299852,\n",
       " '1917-18': 6.72314105149926e-05,\n",
       " '1.0': 0.0002016942315449778,\n",
       " '220': 0.0001344628210299852,\n",
       " '9.3': 6.72314105149926e-05,\n",
       " '334': 6.72314105149926e-05,\n",
       " '81': 0.0002016942315449778,\n",
       " '1/4': 0.0006050826946349334,\n",
       " '1624': 0.0001344628210299852,\n",
       " '1629': 0.0002689256420599704,\n",
       " '4,585': 6.72314105149926e-05,\n",
       " '1860': 0.0002016942315449778,\n",
       " '885': 6.72314105149926e-05,\n",
       " '254': 6.72314105149926e-05,\n",
       " '1800': 0.0001344628210299852,\n",
       " '1040A': 0.0002689256420599704,\n",
       " '1803': 0.0002016942315449778,\n",
       " 'quintillion': 6.72314105149926e-05,\n",
       " '25,000': 0.00047061987360494823,\n",
       " '1065': 0.0001344628210299852,\n",
       " '76': 0.000336157052574963,\n",
       " '3:00': 6.72314105149926e-05,\n",
       " '1066': 0.0001344628210299852,\n",
       " '1846': 6.72314105149926e-05,\n",
       " '15-20': 6.72314105149926e-05,\n",
       " '1687': 6.72314105149926e-05,\n",
       " '1862': 0.0001344628210299852,\n",
       " '1707': 0.0002016942315449778,\n",
       " '180': 0.0005378512841199408,\n",
       " '1709': 6.72314105149926e-05,\n",
       " '92': 0.0002016942315449778,\n",
       " '3000': 6.72314105149926e-05,\n",
       " 'five-and-a-half': 6.72314105149926e-05,\n",
       " '954': 6.72314105149926e-05,\n",
       " '73': 0.0002689256420599704,\n",
       " 'Ab1,040': 6.72314105149926e-05,\n",
       " '677': 0.0001344628210299852,\n",
       " '133': 0.0002689256420599704,\n",
       " '1044': 6.72314105149926e-05,\n",
       " '1594-1674': 6.72314105149926e-05,\n",
       " '1628': 6.72314105149926e-05,\n",
       " '400,000,000': 6.72314105149926e-05,\n",
       " '2:35': 0.0006050826946349334,\n",
       " '69': 0.00047061987360494823,\n",
       " '206': 6.72314105149926e-05,\n",
       " 'Thirteen': 0.0001344628210299852,\n",
       " '1850': 0.0002016942315449778,\n",
       " '1893': 0.0002016942315449778,\n",
       " 'sixty-nine': 6.72314105149926e-05,\n",
       " 'sixty-two': 0.0001344628210299852,\n",
       " 'forty-four': 0.0004033884630899556,\n",
       " '510': 6.72314105149926e-05,\n",
       " '2:01.1': 0.0002016942315449778,\n",
       " '2:12': 6.72314105149926e-05,\n",
       " '2.3': 6.72314105149926e-05,\n",
       " '166': 6.72314105149926e-05,\n",
       " 'eight-thirty': 6.72314105149926e-05,\n",
       " '18,792': 6.72314105149926e-05,\n",
       " '2-2': 6.72314105149926e-05,\n",
       " '1068': 6.72314105149926e-05,\n",
       " '1159': 6.72314105149926e-05,\n",
       " '871': 6.72314105149926e-05,\n",
       " '892': 6.72314105149926e-05,\n",
       " '7070': 0.0002016942315449778,\n",
       " '250': 0.0007395455156649186,\n",
       " '817': 6.72314105149926e-05,\n",
       " '619,000': 6.72314105149926e-05,\n",
       " '48': 0.000672314105149926,\n",
       " '74': 0.0004033884630899556,\n",
       " '1837': 0.0001344628210299852,\n",
       " '1892': 0.0002689256420599704,\n",
       " 'mid-1958': 6.72314105149926e-05,\n",
       " '215': 6.72314105149926e-05,\n",
       " '1162': 6.72314105149926e-05,\n",
       " '1,230': 6.72314105149926e-05,\n",
       " '1929': 0.00047061987360494823,\n",
       " '203': 0.00047061987360494823,\n",
       " 'Sixty-seven': 6.72314105149926e-05,\n",
       " '54': 0.0002016942315449778,\n",
       " '11:30': 0.0001344628210299852,\n",
       " 'Thirty-five': 0.0002689256420599704,\n",
       " '1897-8': 6.72314105149926e-05,\n",
       " '101b': 6.72314105149926e-05,\n",
       " '75,000': 0.0002016942315449778,\n",
       " 'thirty-two': 0.0002016942315449778,\n",
       " '1,000,000': 6.72314105149926e-05,\n",
       " '34220': 6.72314105149926e-05,\n",
       " '1,850': 6.72314105149926e-05,\n",
       " '1602': 0.0002016942315449778,\n",
       " 'forty-five': 0.0004033884630899556,\n",
       " '381(a)': 0.0002689256420599704,\n",
       " '97': 0.0002689256420599704,\n",
       " 'Ninety-nine': 6.72314105149926e-05,\n",
       " '1516': 6.72314105149926e-05,\n",
       " '350,000': 0.0001344628210299852,\n",
       " '4.4': 6.72314105149926e-05,\n",
       " '53': 0.000336157052574963,\n",
       " '1.07': 0.0001344628210299852,\n",
       " '1787-89': 0.0001344628210299852,\n",
       " '27-30': 6.72314105149926e-05,\n",
       " '2:33.2': 6.72314105149926e-05,\n",
       " '108': 0.0002689256420599704,\n",
       " '39,000': 0.0001344628210299852,\n",
       " 'ninety': 0.0007395455156649186,\n",
       " '1,800,000': 6.72314105149926e-05,\n",
       " '162': 6.72314105149926e-05,\n",
       " '2.54': 6.72314105149926e-05,\n",
       " '16.38': 6.72314105149926e-05,\n",
       " '1625': 0.000336157052574963,\n",
       " '1657': 6.72314105149926e-05,\n",
       " '60,000': 0.0002016942315449778,\n",
       " '214': 0.0001344628210299852,\n",
       " '35': 0.0012101653892698668,\n",
       " '115': 6.72314105149926e-05,\n",
       " '770': 0.0001344628210299852,\n",
       " '2.5': 0.0002689256420599704,\n",
       " '7070/7074': 0.0002689256420599704,\n",
       " '86': 0.0004033884630899556,\n",
       " '56': 0.0004033884630899556,\n",
       " 'twenty-three': 0.00047061987360494823,\n",
       " '51': 0.0002689256420599704,\n",
       " '158': 6.72314105149926e-05,\n",
       " '1,100': 6.72314105149926e-05,\n",
       " '15.8': 6.72314105149926e-05,\n",
       " '10,000,000': 0.0001344628210299852,\n",
       " '150,000,000': 0.0001344628210299852,\n",
       " '14.7': 0.0001344628210299852,\n",
       " '29.2': 0.0001344628210299852,\n",
       " '562': 6.72314105149926e-05,\n",
       " '1958-60': 6.72314105149926e-05,\n",
       " '1.8': 0.0002016942315449778,\n",
       " '1832': 0.00047061987360494823,\n",
       " '1,257,700': 6.72314105149926e-05,\n",
       " '1200': 6.72314105149926e-05,\n",
       " '-.50': 6.72314105149926e-05,\n",
       " '-.10': 6.72314105149926e-05,\n",
       " '7-6': 0.0001344628210299852,\n",
       " '32,589': 6.72314105149926e-05,\n",
       " 'Eight': 0.0007395455156649186,\n",
       " '72': 0.000672314105149926,\n",
       " '1981': 6.72314105149926e-05,\n",
       " '1821': 0.000336157052574963,\n",
       " '165': 0.0001344628210299852,\n",
       " '6-6': 6.72314105149926e-05,\n",
       " 'seventy': 0.0002689256420599704,\n",
       " 'hundred-odd': 0.0001344628210299852,\n",
       " '100-200': 6.72314105149926e-05,\n",
       " '9e': 6.72314105149926e-05,\n",
       " '2.26': 6.72314105149926e-05,\n",
       " '1-0': 6.72314105149926e-05,\n",
       " 'Thirty-three': 6.72314105149926e-05,\n",
       " '139': 6.72314105149926e-05,\n",
       " '2,800,000': 6.72314105149926e-05,\n",
       " '447': 0.0001344628210299852,\n",
       " '469': 6.72314105149926e-05,\n",
       " '22-29': 6.72314105149926e-05,\n",
       " '25-30': 6.72314105149926e-05,\n",
       " 'sixteen': 0.0012101653892698668,\n",
       " '1,700': 0.0001344628210299852,\n",
       " 'ninety-eight': 6.72314105149926e-05,\n",
       " 'septillion': 6.72314105149926e-05,\n",
       " '10.2': 6.72314105149926e-05,\n",
       " '3.03': 0.0001344628210299852,\n",
       " '450': 0.0002016942315449778,\n",
       " '66': 0.0001344628210299852,\n",
       " '1,512': 6.72314105149926e-05,\n",
       " '1,065': 6.72314105149926e-05,\n",
       " '7:00': 0.0001344628210299852,\n",
       " 'fifty-three': 6.72314105149926e-05,\n",
       " '6-2/3': 6.72314105149926e-05,\n",
       " '0.7': 6.72314105149926e-05,\n",
       " '172': 6.72314105149926e-05,\n",
       " '2:00.2': 6.72314105149926e-05,\n",
       " '2:04.2': 0.0001344628210299852,\n",
       " '2:03': 6.72314105149926e-05,\n",
       " '2:02.3': 0.0001344628210299852,\n",
       " '16-22': 6.72314105149926e-05,\n",
       " '15-': 6.72314105149926e-05,\n",
       " '46,000': 6.72314105149926e-05,\n",
       " '2.6': 0.0002689256420599704,\n",
       " '2.7': 6.72314105149926e-05,\n",
       " '2,800': 6.72314105149926e-05,\n",
       " '1,524': 6.72314105149926e-05,\n",
       " 'seven-': 6.72314105149926e-05,\n",
       " '543': 6.72314105149926e-05,\n",
       " '1810': 0.000336157052574963,\n",
       " 'Fifty': 0.000336157052574963,\n",
       " '2:33': 0.0002016942315449778,\n",
       " '96': 0.000336157052574963,\n",
       " '285': 6.72314105149926e-05,\n",
       " '312': 6.72314105149926e-05,\n",
       " '392': 0.0001344628210299852,\n",
       " '1964': 0.0002016942315449778,\n",
       " \"'51\": 6.72314105149926e-05,\n",
       " '3.0': 6.72314105149926e-05,\n",
       " '-.5': 6.72314105149926e-05,\n",
       " '391': 6.72314105149926e-05,\n",
       " '1453': 0.0001344628210299852,\n",
       " '$50': 6.72314105149926e-05,\n",
       " '4,000': 0.0002016942315449778,\n",
       " 'eight-by-ten': 6.72314105149926e-05,\n",
       " 'eighty-nine': 6.72314105149926e-05,\n",
       " '1310': 6.72314105149926e-05,\n",
       " '1745': 6.72314105149926e-05,\n",
       " '1764': 6.72314105149926e-05,\n",
       " '1782': 0.0001344628210299852,\n",
       " \"'58\": 0.0002016942315449778,\n",
       " '762': 6.72314105149926e-05,\n",
       " '442': 6.72314105149926e-05,\n",
       " '390': 6.72314105149926e-05,\n",
       " '2-and-2': 6.72314105149926e-05,\n",
       " '1592': 0.0001344628210299852,\n",
       " 'fifty-four': 6.72314105149926e-05,\n",
       " '520': 6.72314105149926e-05,\n",
       " '694': 6.72314105149926e-05,\n",
       " '2.55': 6.72314105149926e-05,\n",
       " '2.58': 6.72314105149926e-05,\n",
       " '035': 6.72314105149926e-05,\n",
       " '40,000': 0.0006050826946349334,\n",
       " '800': 0.0008740083366949038,\n",
       " '247': 0.0001344628210299852,\n",
       " '83': 0.0002016942315449778,\n",
       " 'Fifty-seven': 6.72314105149926e-05,\n",
       " '265': 6.72314105149926e-05,\n",
       " '64-13': 6.72314105149926e-05,\n",
       " '1776': 0.000336157052574963,\n",
       " 'quadrillion': 6.72314105149926e-05,\n",
       " '9-6': 6.72314105149926e-05,\n",
       " '7-9': 6.72314105149926e-05,\n",
       " '3211': 0.0001344628210299852,\n",
       " '185': 6.72314105149926e-05,\n",
       " '10.6': 6.72314105149926e-05,\n",
       " '1597/8': 6.72314105149926e-05,\n",
       " '5847': 6.72314105149926e-05,\n",
       " '6124': 6.72314105149926e-05,\n",
       " '12:50': 6.72314105149926e-05,\n",
       " '7,484,268': 6.72314105149926e-05,\n",
       " '9,910,741': 6.72314105149926e-05,\n",
       " '12-to-one': 6.72314105149926e-05,\n",
       " '1950-1953': 6.72314105149926e-05,\n",
       " '2688': 6.72314105149926e-05,\n",
       " '823': 6.72314105149926e-05,\n",
       " '9a': 6.72314105149926e-05,\n",
       " '57': 0.000672314105149926,\n",
       " '7-1': 0.0004033884630899556,\n",
       " '381': 0.0002689256420599704,\n",
       " '2.0': 0.0001344628210299852,\n",
       " '200,000': 0.0002689256420599704,\n",
       " 'thirty-nine': 0.0001344628210299852,\n",
       " '7:50': 6.72314105149926e-05,\n",
       " '1540': 6.72314105149926e-05,\n",
       " '1882': 0.0002016942315449778,\n",
       " '63,000,000': 6.72314105149926e-05,\n",
       " '7,500': 6.72314105149926e-05,\n",
       " '2:40': 0.0001344628210299852,\n",
       " '20-to-1': 0.0001344628210299852,\n",
       " '116': 6.72314105149926e-05,\n",
       " '400,000': 0.0001344628210299852,\n",
       " '300,000': 0.0004033884630899556,\n",
       " '360,000': 6.72314105149926e-05,\n",
       " '270,000': 6.72314105149926e-05,\n",
       " '1630': 6.72314105149926e-05,\n",
       " '170': 0.0001344628210299852,\n",
       " '105': 0.0001344628210299852,\n",
       " '430,000': 6.72314105149926e-05,\n",
       " '7074': 6.72314105149926e-05,\n",
       " '4.21': 6.72314105149926e-05,\n",
       " '2.16': 6.72314105149926e-05,\n",
       " '1-1/2': 0.0002689256420599704,\n",
       " '132,000': 6.72314105149926e-05,\n",
       " '923,076': 6.72314105149926e-05,\n",
       " '4:00': 0.0001344628210299852,\n",
       " '1811': 6.72314105149926e-05,\n",
       " '7-3': 0.0001344628210299852,\n",
       " '1609': 0.0002016942315449778,\n",
       " '2/3': 6.72314105149926e-05,\n",
       " '1938-1939': 6.72314105149926e-05,\n",
       " '1,509': 6.72314105149926e-05,\n",
       " '2:30-:34.3': 0.0001344628210299852,\n",
       " '250,000': 0.0002689256420599704,\n",
       " '511': 6.72314105149926e-05,\n",
       " '1644': 6.72314105149926e-05,\n",
       " '0.001': 0.0001344628210299852,\n",
       " '4,122,354': 6.72314105149926e-05,\n",
       " '1818': 0.0002689256420599704,\n",
       " '1911-1912': 6.72314105149926e-05,\n",
       " '1631': 6.72314105149926e-05,\n",
       " '1311': 6.72314105149926e-05,\n",
       " '451': 6.72314105149926e-05,\n",
       " '1783': 0.0002016942315449778,\n",
       " '18-1/2': 6.72314105149926e-05,\n",
       " '495': 6.72314105149926e-05,\n",
       " '55,000': 6.72314105149926e-05,\n",
       " '637': 6.72314105149926e-05,\n",
       " 'thirteen': 0.0006050826946349334,\n",
       " '7:25': 6.72314105149926e-05,\n",
       " '4-0': 0.0001344628210299852,\n",
       " '1881-85': 6.72314105149926e-05,\n",
       " '1901': 0.0002689256420599704,\n",
       " '1863': 0.0002016942315449778,\n",
       " '94': 0.0001344628210299852,\n",
       " '1-a': 0.0002689256420599704,\n",
       " '1,488': 6.72314105149926e-05,\n",
       " '7-2': 0.0002016942315449778,\n",
       " '1939': 0.0004033884630899556,\n",
       " 'fifty-one': 6.72314105149926e-05,\n",
       " '0.2': 0.0002689256420599704,\n",
       " '0.4': 0.0002689256420599704,\n",
       " '1733': 6.72314105149926e-05,\n",
       " '1.24': 0.0001344628210299852,\n",
       " '3-5': 6.72314105149926e-05,\n",
       " '1751': 6.72314105149926e-05,\n",
       " '1899': 0.0002016942315449778,\n",
       " '13,200': 6.72314105149926e-05,\n",
       " '117': 6.72314105149926e-05,\n",
       " '79,400': 6.72314105149926e-05,\n",
       " '1904': 0.0002016942315449778,\n",
       " '144': 6.72314105149926e-05,\n",
       " '570': 6.72314105149926e-05,\n",
       " '44': 0.0002016942315449778,\n",
       " '36-A': 6.72314105149926e-05,\n",
       " '1597': 6.72314105149926e-05,\n",
       " '0.005': 6.72314105149926e-05,\n",
       " '0.039': 6.72314105149926e-05,\n",
       " '7/2': 0.0001344628210299852,\n",
       " '1897': 0.0002016942315449778,\n",
       " 'octillion': 0.0002016942315449778,\n",
       " '8-b-2': 6.72314105149926e-05,\n",
       " '2,300': 6.72314105149926e-05,\n",
       " '4.5': 0.0001344628210299852,\n",
       " '1787': 0.0001344628210299852,\n",
       " '710': 6.72314105149926e-05,\n",
       " '3.8': 6.72314105149926e-05,\n",
       " '13.9': 6.72314105149926e-05,\n",
       " '05': 0.0002016942315449778,\n",
       " '5.3': 6.72314105149926e-05,\n",
       " '1492': 6.72314105149926e-05,\n",
       " '1598/9': 6.72314105149926e-05,\n",
       " 'forty-seven': 0.0002016942315449778,\n",
       " '1579': 0.0001344628210299852,\n",
       " 'Ninety': 6.72314105149926e-05,\n",
       " '153': 0.0001344628210299852,\n",
       " '1970': 0.0002016942315449778,\n",
       " '1700': 0.0002016942315449778,\n",
       " '635': 6.72314105149926e-05,\n",
       " '6.6': 6.72314105149926e-05,\n",
       " '278': 6.72314105149926e-05,\n",
       " '31730': 6.72314105149926e-05,\n",
       " '1693': 6.72314105149926e-05,\n",
       " '646': 6.72314105149926e-05,\n",
       " '11-18': 6.72314105149926e-05,\n",
       " '1,212,000': 6.72314105149926e-05,\n",
       " '5-': 6.72314105149926e-05,\n",
       " '1-701': 6.72314105149926e-05,\n",
       " '168': 0.0002016942315449778,\n",
       " '381(c)(6)': 0.0001344628210299852,\n",
       " '140,000': 6.72314105149926e-05,\n",
       " 'two-to-three': 6.72314105149926e-05,\n",
       " '126,000': 6.72314105149926e-05,\n",
       " '1875': 0.0002689256420599704,\n",
       " '7-5': 6.72314105149926e-05,\n",
       " '1307': 6.72314105149926e-05,\n",
       " \"'50\": 0.0002016942315449778,\n",
       " \"'53\": 6.72314105149926e-05,\n",
       " 'Forty-six': 0.0001344628210299852,\n",
       " '1184': 6.72314105149926e-05,\n",
       " '643': 6.72314105149926e-05,\n",
       " '541': 6.72314105149926e-05,\n",
       " '31978': 6.72314105149926e-05,\n",
       " '14-1': 0.0001344628210299852,\n",
       " '2:25': 0.0001344628210299852,\n",
       " '6-12': 6.72314105149926e-05,\n",
       " '1582': 0.0002016942315449778,\n",
       " '120': 0.0004033884630899556,\n",
       " '4.8': 6.72314105149926e-05,\n",
       " '135': 0.0002016942315449778,\n",
       " '3.9': 6.72314105149926e-05,\n",
       " '4.7': 6.72314105149926e-05,\n",
       " '2.512': 0.0001344628210299852,\n",
       " '1858': 0.0001344628210299852,\n",
       " '1665.32': 6.72314105149926e-05,\n",
       " '1667.36': 6.72314105149926e-05,\n",
       " '0.10': 6.72314105149926e-05,\n",
       " '443': 6.72314105149926e-05,\n",
       " '106': 0.0001344628210299852,\n",
       " '2/4': 6.72314105149926e-05,\n",
       " '4/4': 6.72314105149926e-05,\n",
       " 'Forty': 0.0002689256420599704,\n",
       " '3-0': 6.72314105149926e-05,\n",
       " '1558': 6.72314105149926e-05,\n",
       " '1,419,833': 6.72314105149926e-05,\n",
       " '1,541,991': 6.72314105149926e-05,\n",
       " '122,158': 6.72314105149926e-05,\n",
       " '33-1/2': 6.72314105149926e-05,\n",
       " '116,000': 6.72314105149926e-05,\n",
       " '3.3': 0.0001344628210299852,\n",
       " '381(c)': 0.0001344628210299852,\n",
       " '3-1/2': 0.0002016942315449778,\n",
       " '1728': 6.72314105149926e-05,\n",
       " '1,900': 6.72314105149926e-05,\n",
       " '1637': 6.72314105149926e-05,\n",
       " 'twenty-two': 0.0004033884630899556,\n",
       " '1963-1972': 6.72314105149926e-05,\n",
       " '1a': 6.72314105149926e-05,\n",
       " '10:45': 6.72314105149926e-05,\n",
       " '357': 6.72314105149926e-05,\n",
       " '1830': 6.72314105149926e-05,\n",
       " '12,500': 6.72314105149926e-05,\n",
       " '1,119': 6.72314105149926e-05,\n",
       " '402': 6.72314105149926e-05,\n",
       " '22-1/2': 6.72314105149926e-05,\n",
       " '2:67': 6.72314105149926e-05,\n",
       " '63': 0.000336157052574963,\n",
       " '394': 6.72314105149926e-05,\n",
       " '7,827': 6.72314105149926e-05,\n",
       " '5-to-2': 6.72314105149926e-05,\n",
       " '151': 6.72314105149926e-05,\n",
       " '5-1/2': 6.72314105149926e-05,\n",
       " '1774': 6.72314105149926e-05,\n",
       " '128': 0.0002016942315449778,\n",
       " '198': 6.72314105149926e-05,\n",
       " '1859-1929': 6.72314105149926e-05,\n",
       " '211': 0.0001344628210299852,\n",
       " '1895': 0.0004033884630899556,\n",
       " '1.10.1': 6.72314105149926e-05,\n",
       " '50-50': 6.72314105149926e-05,\n",
       " '78-79': 6.72314105149926e-05,\n",
       " '87-1/2': 6.72314105149926e-05,\n",
       " '1947-49': 6.72314105149926e-05,\n",
       " '1,600': 0.0001344628210299852,\n",
       " '2-5': 0.0001344628210299852,\n",
       " '2:31.3-:35.3': 6.72314105149926e-05,\n",
       " '19-1/2': 6.72314105149926e-05,\n",
       " '6-1/2': 0.0001344628210299852,\n",
       " '64-66': 6.72314105149926e-05,\n",
       " '343': 6.72314105149926e-05,\n",
       " '169': 0.0001344628210299852,\n",
       " '22,000': 0.0001344628210299852,\n",
       " '44,000': 6.72314105149926e-05,\n",
       " 'thirty-eight': 6.72314105149926e-05,\n",
       " '836-901': 6.72314105149926e-05,\n",
       " '1876': 0.0002689256420599704,\n",
       " '1865': 0.0004033884630899556,\n",
       " '186': 0.0001344628210299852,\n",
       " '2:00.3': 6.72314105149926e-05,\n",
       " '2:05.1': 6.72314105149926e-05,\n",
       " '121,000': 6.72314105149926e-05,\n",
       " '11:00': 0.0001344628210299852,\n",
       " '12:00': 6.72314105149926e-05,\n",
       " '1898': 0.0002689256420599704,\n",
       " '1792': 0.0002016942315449778,\n",
       " '1:00': 0.0001344628210299852,\n",
       " '195': 0.0001344628210299852,\n",
       " '7-4': 0.0001344628210299852,\n",
       " 'ten-fifty-five': 6.72314105149926e-05,\n",
       " '1.10.8': 6.72314105149926e-05,\n",
       " '540-K': 6.72314105149926e-05,\n",
       " 'ninety-six': 0.0001344628210299852,\n",
       " 'seven-thirty': 6.72314105149926e-05,\n",
       " '200-odd': 6.72314105149926e-05,\n",
       " '828': 6.72314105149926e-05,\n",
       " '354': 0.0001344628210299852,\n",
       " 'Twenty-six': 6.72314105149926e-05,\n",
       " '59,780': 6.72314105149926e-05,\n",
       " '750': 0.0002689256420599704,\n",
       " '19-12': 6.72314105149926e-05,\n",
       " '1632': 0.0002689256420599704,\n",
       " '1639': 6.72314105149926e-05,\n",
       " '1565': 0.0001344628210299852,\n",
       " '512': 0.0001344628210299852,\n",
       " '156': 0.0002016942315449778,\n",
       " '1814': 0.0001344628210299852,\n",
       " '346': 0.0001344628210299852,\n",
       " '5-6': 6.72314105149926e-05,\n",
       " '2:01.3': 6.72314105149926e-05,\n",
       " '2:02': 6.72314105149926e-05,\n",
       " '2:06.3': 6.72314105149926e-05,\n",
       " '2:02.2': 6.72314105149926e-05,\n",
       " '22,807': 6.72314105149926e-05,\n",
       " '37,081': 6.72314105149926e-05,\n",
       " 'five-seventeen': 6.72314105149926e-05,\n",
       " 'eighty-one': 6.72314105149926e-05,\n",
       " '1934': 0.0004033884630899556,\n",
       " '1640': 6.72314105149926e-05,\n",
       " '1747': 6.72314105149926e-05,\n",
       " '1878': 6.72314105149926e-05,\n",
       " '1957-1958': 6.72314105149926e-05,\n",
       " '1290': 6.72314105149926e-05,\n",
       " '1655': 6.72314105149926e-05,\n",
       " '1596/7': 6.72314105149926e-05,\n",
       " '1633': 6.72314105149926e-05,\n",
       " '1598': 0.0001344628210299852,\n",
       " '6-7': 6.72314105149926e-05,\n",
       " '88': 6.72314105149926e-05,\n",
       " '1746': 6.72314105149926e-05,\n",
       " '1748': 6.72314105149926e-05,\n",
       " '1967': 0.0001344628210299852,\n",
       " '650': 6.72314105149926e-05,\n",
       " '63-64': 6.72314105149926e-05,\n",
       " '1923-27': 6.72314105149926e-05,\n",
       " '1972': 6.72314105149926e-05,\n",
       " '4-7/8': 6.72314105149926e-05,\n",
       " '1986': 0.0002016942315449778,\n",
       " '1991': 6.72314105149926e-05,\n",
       " '37,470': 6.72314105149926e-05,\n",
       " '6-5': 6.72314105149926e-05,\n",
       " 'forty-nine': 0.0002016942315449778,\n",
       " '213': 0.0001344628210299852,\n",
       " '060': 6.72314105149926e-05,\n",
       " '5-3': 6.72314105149926e-05,\n",
       " '5.6': 0.0001344628210299852,\n",
       " '6.2': 6.72314105149926e-05,\n",
       " '1806': 6.72314105149926e-05,\n",
       " '256': 0.0002016942315449778,\n",
       " '1024': 6.72314105149926e-05,\n",
       " '6.4': 0.0001344628210299852,\n",
       " '9,748,000': 6.72314105149926e-05,\n",
       " '2,887,671': 6.72314105149926e-05,\n",
       " 'Twelve': 0.0002016942315449778,\n",
       " '4.6': 6.72314105149926e-05,\n",
       " '573': 6.72314105149926e-05,\n",
       " '1847': 0.0001344628210299852,\n",
       " '2:26': 0.0001344628210299852,\n",
       " '1.25': 6.72314105149926e-05,\n",
       " \"'31\": 6.72314105149926e-05,\n",
       " '2.44': 6.72314105149926e-05,\n",
       " '1769': 6.72314105149926e-05,\n",
       " 'seventy-eight': 6.72314105149926e-05,\n",
       " ':35.3': 6.72314105149926e-05,\n",
       " '381(c)(4)': 6.72314105149926e-05,\n",
       " '5-5': 0.0001344628210299852,\n",
       " '5:30': 6.72314105149926e-05,\n",
       " '27,000': 0.0001344628210299852,\n",
       " '4:05': 6.72314105149926e-05,\n",
       " '13-1/2': 6.72314105149926e-05,\n",
       " '9329': 6.72314105149926e-05,\n",
       " ...}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emission_probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes or states:  14\n",
      "Number of edges:  168\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"alert alert-block alert-success\">Your HMM network topology looks good!</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for pos_tag in data.tagset:\n",
    "    \n",
    "    state = states[pos_tag]\n",
    "    \n",
    "    tags_label= data.Y\n",
    "    tag_starts = starting_counts(tags_label)\n",
    "    tag_ends = ending_counts(tags_label)\n",
    "    start_probability = tag_starts[pos_tag] / sum(tag_starts.values())\n",
    "    basic_model.add_transition(basic_model.start, state, start_probability)\n",
    "    end_probability = tag_ends[pos_tag] / sum(tag_ends.values())\n",
    "    \n",
    "    basic_model.add_transition(state, basic_model.end, end_probability)\n",
    "\n",
    "for tag_1 in data.tagset:\n",
    "    \n",
    "    state_1 = states[tag_1]\n",
    "    \n",
    "    sum_of_probabilities = 0\n",
    "    \n",
    "    for tag_2 in data.tagset:\n",
    "        state_2 = states[tag_2]\n",
    "        bigram = (tag_1, tag_2)\n",
    "        transition_probability = tag_bigrams[bigram] / tag_unigrams[tag_1]\n",
    "        sum_of_probabilities += transition_probability\n",
    "        basic_model.add_transition(state_1, state_2, transition_probability)    \n",
    "    \n",
    "basic_model.bake()\n",
    "print(\"Number of nodes or states: \", basic_model.node_count())\n",
    "print(\"Number of edges: \", basic_model.edge_count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy basic hmm model: 97.53%\n",
      "testing accuracy basic hmm model: 96.16%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div class=\"alert alert-block alert-success\">Your HMM tagger accuracy looks correct! Congratulations, you've finished the project.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hmm_training_acc = accuracy(data.training_set.X, data.training_set.Y, basic_model)\n",
    "print(\"training accuracy basic hmm model: {:.2f}%\".format(100 * hmm_training_acc))\n",
    "\n",
    "hmm_testing_acc = accuracy(data.testing_set.X, data.testing_set.Y, basic_model)\n",
    "print(\"testing accuracy basic hmm model: {:.2f}%\".format(100 * hmm_testing_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example Decoding Sequences with the HMM Tagger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence Key: b100-28144\n",
      "\n",
      "Predicted labels:\n",
      "-----------------\n",
      "['CONJ', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'CONJ', 'NOUN', 'NUM', '.', '.', 'NOUN', '.', '.']\n",
      "\n",
      "Actual labels:\n",
      "--------------\n",
      "('CONJ', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'CONJ', 'NOUN', 'NUM', '.', '.', 'NOUN', '.', '.')\n",
      "\n",
      "\n",
      "Sentence Key: b100-23146\n",
      "\n",
      "Predicted labels:\n",
      "-----------------\n",
      "['PRON', 'VERB', 'DET', 'NOUN', 'ADP', 'ADJ', 'ADJ', 'NOUN', 'VERB', 'VERB', '.', 'ADP', 'VERB', 'DET', 'NOUN', 'ADP', 'NOUN', 'ADP', 'DET', 'NOUN', '.']\n",
      "\n",
      "Actual labels:\n",
      "--------------\n",
      "('PRON', 'VERB', 'DET', 'NOUN', 'ADP', 'ADJ', 'ADJ', 'NOUN', 'VERB', 'VERB', '.', 'ADP', 'VERB', 'DET', 'NOUN', 'ADP', 'NOUN', 'ADP', 'DET', 'NOUN', '.')\n",
      "\n",
      "\n",
      "Sentence Key: b100-35462\n",
      "\n",
      "Predicted labels:\n",
      "-----------------\n",
      "['DET', 'ADJ', 'NOUN', 'VERB', 'VERB', 'VERB', 'ADP', 'DET', 'ADJ', 'ADJ', 'NOUN', 'ADP', 'DET', 'ADJ', 'NOUN', '.', 'ADP', 'ADJ', 'NOUN', '.', 'CONJ', 'ADP', 'DET', 'NOUN', 'ADP', 'ADJ', 'ADJ', '.', 'ADJ', '.', 'CONJ', 'ADJ', 'NOUN', 'ADP', 'ADJ', 'NOUN', '.']\n",
      "\n",
      "Actual labels:\n",
      "--------------\n",
      "('DET', 'ADJ', 'NOUN', 'VERB', 'VERB', 'VERB', 'ADP', 'DET', 'ADJ', 'ADJ', 'NOUN', 'ADP', 'DET', 'ADJ', 'NOUN', '.', 'ADP', 'ADJ', 'NOUN', '.', 'CONJ', 'ADP', 'DET', 'NOUN', 'ADP', 'ADJ', 'ADJ', '.', 'ADJ', '.', 'CONJ', 'ADJ', 'NOUN', 'ADP', 'ADJ', 'NOUN', '.')\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for key in data.testing_set.keys[:3]:\n",
    "    print(\"Sentence Key: {}\\n\".format(key))\n",
    "    print(\"Predicted labels:\\n-----------------\")\n",
    "    print(simplify_decoding(data.sentences[key].words, basic_model))\n",
    "    print()\n",
    "    print(\"Actual labels:\\n--------------\")\n",
    "    print(data.sentences[key].tags)\n",
    "    print(\"\\n\")"
   ]
  }
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