2.3 NLTK and Wordnet.ipynb

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     "text": [
      "Exception reporting mode: Plain\n",
      "Doctest mode is: ON\n"
     ]
    }
   ],
   "source": [
    "%doctest_mode\n",
    "\n",
    "from IPython.display import Image\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# WordNet\n",
    "\n",
    "WordNet is a lexical database for the English language:\n",
    "- groups English words into sets of synonyms called *synsets*; \n",
    "- provides short, general definitions (glosses);\n",
    "- records the various semantic relations between these synonym sets (antonymy, meronymy etc. - POS specific)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Structure of WordNet\n",
    "\n",
    "Relations between synsets are POS specific (noun, verb, adjective).\n",
    "\n",
    "For nouns we have:\n",
    "- hypernyms: (canine is a hypernym of dog, because every dog is a member of the larger category of canines)\n",
    "- hyponyms: (dog is a hyponym of canine)\n",
    "- coordinate terms: (wolf is a coordinate term of dog, and dog is a coordinate term of wolf)\n",
    "- holonym: (building is a holonym of window)\n",
    "- meronym: (window is a meronym of building)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "slide_type": "subslide"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://courses.washington.edu/hypertxt/cgi-bin/book/maps/imaginediag.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
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   "source": [
    "Image(url=\"https://courses.washington.edu/hypertxt/cgi-bin/book/maps/imaginediag.gif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# NLTK and WordNet\n",
    "\n",
    "- NLTK is very useful for accessing the information in WordNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# we need to import wordnet first\n",
    "from nltk.corpus import wordnet as wn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Working with synsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('car.n.01')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve all the synsets for a word\n",
    "wn.synsets('motorcar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- the name of a synset is structured as follows: **word**.**POS**.**sense number**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('suppose.v.01'), Synset('think.v.02'), Synset('speculate.v.01'), Synset('presuppose.v.01'), Synset('presuppose.v.02')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve all the synsets for a word and a POS\n",
    "wn.synsets('suppose', wn.VERB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['car', 'auto', 'automobile', 'machine', 'motorcar']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access directly information about a synset. \n",
    "wn.synset('car.n.01').lemma_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('car.n.01'), Synset('car.n.02'), Synset('car.n.03'), Synset('car.n.04'), Synset('cable_car.n.01')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synsets('car')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('car.n.01.car'), Lemma('car.n.02.car'), Lemma('car.n.03.car'), Lemma('car.n.04.car'), Lemma('cable_car.n.01.car')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.lemmas('car')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('banks.n.01'), Synset('bank.n.01'), Synset('depository_financial_institution.n.01'), Synset('bank.n.03'), Synset('bank.n.04'), Synset('bank.n.05'), Synset('bank.n.06'), Synset('bank.n.07'), Synset('savings_bank.n.02'), Synset('bank.n.09'), Synset('bank.n.10'), Synset('bank.v.01'), Synset('bank.v.02'), Synset('bank.v.03'), Synset('bank.v.04'), Synset('bank.v.05'), Synset('deposit.v.02'), Synset('bank.v.07'), Synset('trust.v.01')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = wn.synsets(\"banks\")\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'English botanist who accompanied Captain Cook on his first voyage to the Pacific Ocean (1743-1820)'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[0].definition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[0].examples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Definition for sense 1 is English botanist who accompanied Captain Cook on his first voyage to the Pacific Ocean (1743-1820)\n",
      "Definition for sense 2 is sloping land (especially the slope beside a body of water)\n",
      "Definition for sense 3 is a financial institution that accepts deposits and channels the money into lending activities\n",
      "Definition for sense 4 is a long ridge or pile\n",
      "Definition for sense 5 is an arrangement of similar objects in a row or in tiers\n",
      "Definition for sense 6 is a supply or stock held in reserve for future use (especially in emergencies)\n",
      "Definition for sense 7 is the funds held by a gambling house or the dealer in some gambling games\n",
      "Definition for sense 8 is a slope in the turn of a road or track; the outside is higher than the inside in order to reduce the effects of centrifugal force\n",
      "Definition for sense 9 is a container (usually with a slot in the top) for keeping money at home\n",
      "Definition for sense 10 is a building in which the business of banking transacted\n",
      "Definition for sense 11 is a flight maneuver; aircraft tips laterally about its longitudinal axis (especially in turning)\n",
      "Definition for sense 12 is tip laterally\n",
      "Definition for sense 13 is enclose with a bank\n",
      "Definition for sense 14 is do business with a bank or keep an account at a bank\n",
      "Definition for sense 15 is act as the banker in a game or in gambling\n",
      "Definition for sense 16 is be in the banking business\n",
      "Definition for sense 17 is put into a bank account\n",
      "Definition for sense 18 is cover with ashes so to control the rate of burning\n",
      "Definition for sense 19 is have confidence or faith in\n"
     ]
    }
   ],
   "source": [
    "# how to display all the definitions\n",
    "for i, synset in enumerate(wn.synsets(\"banks\")):\n",
    "    print(\"Definition for sense\", i + 1, \"is\", synset.definition())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Hyponyms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Synset('ambulance.n.01'), Synset('beach_wagon.n.01'), Synset('bus.n.04'), Synset('cab.n.03'), Synset('compact.n.03'), Synset('convertible.n.01'), Synset('coupe.n.01'), Synset('cruiser.n.01'), Synset('electric.n.01'), Synset('gas_guzzler.n.01'), Synset('hardtop.n.01'), Synset('hatchback.n.01'), Synset('horseless_carriage.n.01'), Synset('hot_rod.n.01'), Synset('jeep.n.01'), Synset('limousine.n.01'), Synset('loaner.n.02'), Synset('minicar.n.01'), Synset('minivan.n.01'), Synset('model_t.n.01'), Synset('pace_car.n.01'), Synset('racer.n.02'), Synset('roadster.n.01'), Synset('sedan.n.01'), Synset('sport_utility.n.01'), Synset('sports_car.n.01'), Synset('stanley_steamer.n.01'), Synset('stock_car.n.01'), Synset('subcompact.n.01'), Synset('touring_car.n.01'), Synset('used-car.n.01')]\n"
     ]
    }
   ],
   "source": [
    "motorcar = wn.synset('car.n.01')\n",
    "types_of_motorcar = motorcar.hyponyms()\n",
    "print(types_of_motorcar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Hypernyms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('motor_vehicle.n.01')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motorcar = wn.synset('car.n.01')\n",
    "motorcar.hypernyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('entity.n.01')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most general hypernym\n",
    "motorcar.root_hypernyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('move.v.02')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('bank.v.01').root_hypernyms()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Meronyms and holonyms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('burl.n.02'), Synset('crown.n.07'), Synset('limb.n.02'), Synset('stump.n.01'), Synset('trunk.n.01')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can explore meronyms and holonyms of nouns if they have\n",
    "wn.synset('tree.n.01').part_meronyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('tree.n.01')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('crown.n.07').part_holonyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('heartwood.n.01'), Synset('sapwood.n.01')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('tree.n.01').substance_meronyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('forest.n.01')]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wn.synset('tree.n.01').member_holonyms()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Antonyms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('bright.a.01'), Synset('bright.s.02'), Synset('bright.s.03'), Synset('bright.s.04'), Synset('bright.s.05'), Synset('bright.s.06'), Synset('undimmed.a.01'), Synset('bright.s.08'), Synset('bright.s.09'), Synset('bright.s.10'), Synset('brilliantly.r.01')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we want to find the antonyms of the word bright\n",
    "bright = wn.synsets(\"bright\")\n",
    "bright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "emitting or reflecting light readily or in large amounts\n"
     ]
    }
   ],
   "source": [
    "print(bright[0].definition())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Synset' object has no attribute 'antonyms'",
     "output_type": "error",
     "traceback": [
      "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-26-6e86e9a83331>\"\u001b[1;36m, line \u001b[1;32m2\u001b[1;36m, in \u001b[1;35m<module>\u001b[1;36m\u001b[0m\n\u001b[1;33m    bright[0].antonyms()\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m\u001b[1;31m:\u001b[0m 'Synset' object has no attribute 'antonyms'\n"
     ]
    }
   ],
   "source": [
    "# some of the relations are not between synsets, but between lemmas\n",
    "bright[0].antonyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('bright.a.01.bright')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bright[0].lemmas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Lemma('dull.a.02.dull')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bright[0].lemmas()[0].antonyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Synset('dull.a.02')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bright[0].lemmas()[0].antonyms()[0].synset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'emitting or reflecting very little light'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bright[0].lemmas()[0].antonyms()[0].synset().definition()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Semantic similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('baleen_whale.n.01')]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = wn.synset('right_whale.n.01')\n",
    "orca = wn.synset('orca.n.01')\n",
    "minke = wn.synset('minke_whale.n.01')\n",
    "tortoise = wn.synset('tortoise.n.01')\n",
    "novel = wn.synset('novel.n.01')\n",
    "right.lowest_common_hypernyms(minke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('whale.n.02')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.lowest_common_hypernyms(orca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('vertebrate.n.01')]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.lowest_common_hypernyms(tortoise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Synset('entity.n.01')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.lowest_common_hypernyms(novel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can calculate the similarity by looking at the shortest path\n",
    "right.path_similarity(minke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16666666666666666"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.path_similarity(orca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2512917986064953"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leacock-Chodorow Similarity takes into consideration the distance and \n",
    "# the depth of the taxonomy\n",
    "right.lch_similarity(minke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.845826690498331"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.lch_similarity(orca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Have a look at WordNet howto for more similarity metrics <a href=\"http://www.nltk.org/howto/wordnet.html\" target=\"_blank\">http://www.nltk.org/howto/wordnet.html</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Exercises\n",
    "\n",
    "- Ask the user to enter two nouns. Determine whether the two nouns are synonyms and display the meaning they share.\n",
    "- Part two of the above: if the two nouns are not synonyms find which senses of the two nouns are the closest (choose whatever measure of closeness you want).\n",
    "- Take a text from Brown corpus. Find out for each word in the text its synsets. Find out which of the synsets are most used in the text."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Read more\n",
    "\n",
    "- More examples how to access information in WordNet <a href=\"http://www.nltk.org/howto/wordnet.html\" target=\"_blank\">http://www.nltk.org/howto/wordnet.html</a>\n",
    "- A tutorial how to use NLTK and WordNet: <a href=\"https://pythonprogramming.net/wordnet-nltk-tutorial/\" target=\"_blank\">https://pythonprogramming.net/wordnet-nltk-tutorial/</a> (includes a video)\n",
    "- Section 5 of http://www.nltk.org/book/ch02.html\n",
    "- Online interface to WordNet: <a href=\"http://wordnetweb.princeton.edu/perl/webwn\" target=\"_blank\">http://wordnetweb.princeton.edu/perl/webwn</a>\n",
    "- For those of you who want to learn more about WordNet:\n",
    "<a href=\"http://wordnetcode.princeton.edu/5papers.pdf\" target=\"_blank\">http://wordnetcode.princeton.edu/5papers.pdf</a>"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
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   "source": [
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-sa/4.0/\"><img alt=\"Creative Commons Licence\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-sa/4.0/88x31.png\" /></a><br /><span xmlns:dct=\"http://purl.org/dc/terms/\" property=\"dct:title\">Python programming for linguists</span> by <a xmlns:cc=\"http://creativecommons.org/ns#\" href=\"http://dinel.org.uk\" property=\"cc:attributionName\" rel=\"cc:attributionURL\">Constantin Orasan</a> is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-sa/4.0/\">Creative Commons Attribution-ShareAlike 4.0 International License</a>.<br />Based on a work at <a xmlns:dct=\"http://purl.org/dc/terms/\" href=\"https://github.com/dinel/PythonForLinguists\" rel=\"dct:source\">https://github.com/dinel/PythonForLinguists</a>."
   ]
  }
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