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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "309f90ac-71d0-4b74-9942-3289cda32674",
+   "metadata": {},
+   "source": [
+    "# Text corpora\n",
+    "## 1. Gutenberg corpus\n",
+    "### Electronic text archive containing free electronic books"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "09f63e09-04e8-4d16-af92-962bdb16cee9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.corpus import gutenberg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6519f2b9-8061-472b-9e86-c08148f2b4f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['austen-emma.txt',\n",
+       " 'austen-persuasion.txt',\n",
+       " 'austen-sense.txt',\n",
+       " 'bible-kjv.txt',\n",
+       " 'blake-poems.txt',\n",
+       " 'bryant-stories.txt',\n",
+       " 'burgess-busterbrown.txt',\n",
+       " 'carroll-alice.txt',\n",
+       " 'chesterton-ball.txt',\n",
+       " 'chesterton-brown.txt',\n",
+       " 'chesterton-thursday.txt',\n",
+       " 'edgeworth-parents.txt',\n",
+       " 'melville-moby_dick.txt',\n",
+       " 'milton-paradise.txt',\n",
+       " 'shakespeare-caesar.txt',\n",
+       " 'shakespeare-hamlet.txt',\n",
+       " 'shakespeare-macbeth.txt',\n",
+       " 'whitman-leaves.txt']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gutenberg.fileids()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "96301294-03b2-4e1d-ab62-23a7a17ea4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract words\n",
+    "mac = gutenberg.words('shakespeare-macbeth.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "56ae568d-9470-498a-bc1e-0024609d1ece",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23140"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(mac)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e2804231-2f4c-4a8f-812a-a5b26b7907aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['[', 'The', 'Tragedie', 'of', 'Macbeth', 'by', ...]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mac"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9ad6418d-9a00-47ad-8f30-7a8b7b97424f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['[',\n",
+       "  'The',\n",
+       "  'Tragedie',\n",
+       "  'of',\n",
+       "  'Macbeth',\n",
+       "  'by',\n",
+       "  'William',\n",
+       "  'Shakespeare',\n",
+       "  '1603',\n",
+       "  ']'],\n",
+       " ['Actus', 'Primus', '.'],\n",
+       " ['Scoena', 'Prima', '.'],\n",
+       " ['Thunder', 'and', 'Lightning', '.'],\n",
+       " ['Enter', 'three', 'Witches', '.']]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# extract sentences\n",
+    "gutenberg.sents('shakespeare-macbeth.txt')[0:5] # First 5 sentences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9821d6f9-f9a4-4b9e-80f8-126a6b542b48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['1', '.'],\n",
+       " ['When', 'shall', 'we', 'three', 'meet', 'againe', '?'],\n",
+       " ['In', 'Thunder', ',', 'Lightning', ',', 'or', 'in', 'Raine', '?'],\n",
+       " ['2', '.'],\n",
+       " ['When',\n",
+       "  'the',\n",
+       "  'Hurley',\n",
+       "  '-',\n",
+       "  'burley',\n",
+       "  \"'\",\n",
+       "  's',\n",
+       "  'done',\n",
+       "  ',',\n",
+       "  'When',\n",
+       "  'the',\n",
+       "  'Battaile',\n",
+       "  \"'\",\n",
+       "  's',\n",
+       "  'lost',\n",
+       "  ',',\n",
+       "  'and',\n",
+       "  'wonne']]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gutenberg.sents('shakespeare-macbeth.txt')[5:10] # Next 5 sentences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5518584c-f43d-42fb-8a9b-8b9d82e8a65c",
+   "metadata": {},
+   "source": [
+    "# 2. Web & chat\n",
+    "### samples of less formal language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f778503b-9a4a-4336-b484-353183d1794b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.corpus import nps_chat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0aaa90ae-7528-4f82-80d0-55dadaef19b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat = nps_chat.posts('10-19-20s_706posts.xml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "09e19b03-a4e5-4009-84ca-13a4f22aaafb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['i', \"'ll\", 'thunder', 'clap', 'your', 'ass', '.'],\n",
+       " ['PART'],\n",
+       " ['and', 'i', 'dont', 'even', 'know', 'what', 'that', 'means', '.'],\n",
+       " ['that', 'sounds', 'painful'],\n",
+       " ['any', 'ladis', 'wanna', 'chat', '?', '29', 'm'],\n",
+       " ['26', '/', 'm'],\n",
+       " ['JOIN'],\n",
+       " ['my', 'cousin', 'drew', 'a', 'messed', 'up', 'pic', 'on', 'my', 'cast'],\n",
+       " ['PART'],\n",
+       " ['24', '/', 'm']]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat[20:30]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "407ce9b9-4ce2-4e4c-860a-b212ed392f22",
+   "metadata": {},
+   "source": [
+    "# 3. Brown corpus\n",
+    "### text from 500 sources, categorized by genre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f77a8f7b-ee78-4f7d-bb52-3fe4982f519d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.corpus import brown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1eca1401-baf1-4033-bc13-3b9ca3c54e26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['adventure',\n",
+       " 'belles_lettres',\n",
+       " 'editorial',\n",
+       " 'fiction',\n",
+       " 'government',\n",
+       " 'hobbies',\n",
+       " 'humor',\n",
+       " 'learned',\n",
+       " 'lore',\n",
+       " 'mystery',\n",
+       " 'news',\n",
+       " 'religion',\n",
+       " 'reviews',\n",
+       " 'romance',\n",
+       " 'science_fiction']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "brown.categories()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "26e8a772-f93d-4ba6-a9f9-7f9008991482",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "brown.words(categories='news')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b0619362-8905-4062-9bf3-5744f6ed1548",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  can could   may might  must  will \n",
+      "           news    93    86    66    38    50   389 \n",
+      "       religion    82    59    78    12    54    71 \n",
+      "        hobbies   268    58   131    22    83   264 \n",
+      "science_fiction    16    49     4    12     8    16 \n",
+      "        romance    74   193    11    51    45    43 \n",
+      "          humon     0     0     0     0     0     0 \n"
+     ]
+    }
+   ],
+   "source": [
+    "# The study of systemic differences between genres is known as \"stylistics\"\n",
+    "# e.g. a stylistic analysis of the use of modal verbs\n",
+    "import nltk\n",
+    "cfd = nltk.ConditionalFreqDist(\n",
+    "    (genre, word)\n",
+    "    for genre in brown.categories()\n",
+    "    for word in brown.words(categories=genre))\n",
+    "genres = ['news', 'religion', 'hobbies', 'science_fiction', 'romance', 'humon']\n",
+    "modals = ['can', 'could', 'may', 'might', 'must', 'will']\n",
+    "cfd.tabulate(conditions = genres, samples = modals)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7df941f2-d900-40df-97e0-fdcf1a3f2bb6",
+   "metadata": {},
+   "source": [
+    "# 4. Reuters Corpus\n",
+    "### 10,788 news docs, tagged with various topics/industries etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "cba18fb4-1232-4771-b7cf-99a69b179437",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package reuters to C:\\nltk_data...\n",
+      "[nltk_data]   Package reuters is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from nltk.corpus import reuters\n",
+    "nltk.download('reuters')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "91b9e46b-baaf-42f2-8124-bc2e75d2a824",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['acq',\n",
+       " 'alum',\n",
+       " 'barley',\n",
+       " 'bop',\n",
+       " 'carcass',\n",
+       " 'castor-oil',\n",
+       " 'cocoa',\n",
+       " 'coconut',\n",
+       " 'coconut-oil',\n",
+       " 'coffee',\n",
+       " 'copper',\n",
+       " 'copra-cake',\n",
+       " 'corn',\n",
+       " 'cotton',\n",
+       " 'cotton-oil',\n",
+       " 'cpi',\n",
+       " 'cpu',\n",
+       " 'crude',\n",
+       " 'dfl',\n",
+       " 'dlr',\n",
+       " 'dmk',\n",
+       " 'earn',\n",
+       " 'fuel',\n",
+       " 'gas',\n",
+       " 'gnp',\n",
+       " 'gold',\n",
+       " 'grain',\n",
+       " 'groundnut',\n",
+       " 'groundnut-oil',\n",
+       " 'heat',\n",
+       " 'hog',\n",
+       " 'housing',\n",
+       " 'income',\n",
+       " 'instal-debt',\n",
+       " 'interest',\n",
+       " 'ipi',\n",
+       " 'iron-steel',\n",
+       " 'jet',\n",
+       " 'jobs',\n",
+       " 'l-cattle',\n",
+       " 'lead',\n",
+       " 'lei',\n",
+       " 'lin-oil',\n",
+       " 'livestock',\n",
+       " 'lumber',\n",
+       " 'meal-feed',\n",
+       " 'money-fx',\n",
+       " 'money-supply',\n",
+       " 'naphtha',\n",
+       " 'nat-gas',\n",
+       " 'nickel',\n",
+       " 'nkr',\n",
+       " 'nzdlr',\n",
+       " 'oat',\n",
+       " 'oilseed',\n",
+       " 'orange',\n",
+       " 'palladium',\n",
+       " 'palm-oil',\n",
+       " 'palmkernel',\n",
+       " 'pet-chem',\n",
+       " 'platinum',\n",
+       " 'potato',\n",
+       " 'propane',\n",
+       " 'rand',\n",
+       " 'rape-oil',\n",
+       " 'rapeseed',\n",
+       " 'reserves',\n",
+       " 'retail',\n",
+       " 'rice',\n",
+       " 'rubber',\n",
+       " 'rye',\n",
+       " 'ship',\n",
+       " 'silver',\n",
+       " 'sorghum',\n",
+       " 'soy-meal',\n",
+       " 'soy-oil',\n",
+       " 'soybean',\n",
+       " 'strategic-metal',\n",
+       " 'sugar',\n",
+       " 'sun-meal',\n",
+       " 'sun-oil',\n",
+       " 'sunseed',\n",
+       " 'tea',\n",
+       " 'tin',\n",
+       " 'trade',\n",
+       " 'veg-oil',\n",
+       " 'wheat',\n",
+       " 'wpi',\n",
+       " 'yen',\n",
+       " 'zinc']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reuters.categories()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ce974f87-7bb6-4412-bde0-32783adaf4be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ASIAN', 'EXPORTERS', 'FEAR', 'DAMAGE', 'FROM', 'U', ...]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reuters.words()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec6fc6ad-906b-4278-b2fe-23d9982bd390",
+   "metadata": {},
+   "source": [
+    "# 5. Inaugural address corpus\n",
+    "### 55 texts, one for each US presidential address."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "981c9cf2-6ca6-4205-af3d-64f38fa03525",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['1789-Washington.txt',\n",
+       " '1793-Washington.txt',\n",
+       " '1797-Adams.txt',\n",
+       " '1801-Jefferson.txt',\n",
+       " '1805-Jefferson.txt',\n",
+       " '1809-Madison.txt',\n",
+       " '1813-Madison.txt',\n",
+       " '1817-Monroe.txt',\n",
+       " '1821-Monroe.txt',\n",
+       " '1825-Adams.txt',\n",
+       " '1829-Jackson.txt',\n",
+       " '1833-Jackson.txt',\n",
+       " '1837-VanBuren.txt',\n",
+       " '1841-Harrison.txt',\n",
+       " '1845-Polk.txt',\n",
+       " '1849-Taylor.txt',\n",
+       " '1853-Pierce.txt',\n",
+       " '1857-Buchanan.txt',\n",
+       " '1861-Lincoln.txt',\n",
+       " '1865-Lincoln.txt',\n",
+       " '1869-Grant.txt',\n",
+       " '1873-Grant.txt',\n",
+       " '1877-Hayes.txt',\n",
+       " '1881-Garfield.txt',\n",
+       " '1885-Cleveland.txt',\n",
+       " '1889-Harrison.txt',\n",
+       " '1893-Cleveland.txt',\n",
+       " '1897-McKinley.txt',\n",
+       " '1901-McKinley.txt',\n",
+       " '1905-Roosevelt.txt',\n",
+       " '1909-Taft.txt',\n",
+       " '1913-Wilson.txt',\n",
+       " '1917-Wilson.txt',\n",
+       " '1921-Harding.txt',\n",
+       " '1925-Coolidge.txt',\n",
+       " '1929-Hoover.txt',\n",
+       " '1933-Roosevelt.txt',\n",
+       " '1937-Roosevelt.txt',\n",
+       " '1941-Roosevelt.txt',\n",
+       " '1945-Roosevelt.txt',\n",
+       " '1949-Truman.txt',\n",
+       " '1953-Eisenhower.txt',\n",
+       " '1957-Eisenhower.txt',\n",
+       " '1961-Kennedy.txt',\n",
+       " '1965-Johnson.txt',\n",
+       " '1969-Nixon.txt',\n",
+       " '1973-Nixon.txt',\n",
+       " '1977-Carter.txt',\n",
+       " '1981-Reagan.txt',\n",
+       " '1985-Reagan.txt',\n",
+       " '1989-Bush.txt',\n",
+       " '1993-Clinton.txt',\n",
+       " '1997-Clinton.txt',\n",
+       " '2001-Bush.txt',\n",
+       " '2005-Bush.txt',\n",
+       " '2009-Obama.txt',\n",
+       " '2013-Obama.txt',\n",
+       " '2017-Trump.txt',\n",
+       " '2021-Biden.txt']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from nltk.corpus import inaugural\n",
+    "inaugural.fileids()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4c9099ac-5c7b-417b-8210-557f66b74a48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: xlabel='Samples', ylabel='Counts'>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfd = nltk.ConditionalFreqDist(\n",
+    "    (target, fileid[:4])\n",
+    "    for fileid in inaugural.fileids()\n",
+    "    for w in inaugural.words(fileid)\n",
+    "    for target in ['america', 'citizen'] if w.lower().startswith(target))\n",
+    "cfd.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e904063f-5fbe-41e9-b4d1-79868594ddab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ConditionalFreqDist with 2 conditions>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "565ff739-4271-4f42-8db5-7e89a4e66560",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['citizen', 'america']"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfd.conditions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7bd6e4d8-efa2-4440-98da-5532124b00db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<FreqDist with 50 samples and 502 outcomes>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cfd['america'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "8423f19a-d2a8-457c-9f96-a68d6229da25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('1841', 38),\n",
+       " ('1821', 15),\n",
+       " ('1817', 14),\n",
+       " ('1885', 13),\n",
+       " ('1889', 12),\n",
+       " ('1929', 12),\n",
+       " ('1845', 11),\n",
+       " ('2001', 11),\n",
+       " ('1805', 10),\n",
+       " ('1893', 10)]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfd['citizen'].most_common(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "1009b0ae-dad9-47e8-8e49-e4fd2a6d72f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('2021', 40),\n",
+       " ('2017', 35),\n",
+       " ('1993', 33),\n",
+       " ('1997', 31),\n",
+       " ('2005', 30),\n",
+       " ('1921', 24),\n",
+       " ('1973', 23),\n",
+       " ('1985', 21),\n",
+       " ('2001', 20),\n",
+       " ('2013', 19)]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfd['america'].most_common(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9ff0e423-8241-436d-923f-b3328dcc05f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "40"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfd['america']['2021']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "337a9910-fd59-4566-8e80-d37a7edf5e1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  0    1    2    3    4    5    6    7    8    9 \n",
+      "       English    0  185  525  883  997 1166 1283 1440 1558 1638 \n",
+      "German_Deutsch    0  171  263  614  717  894 1013 1110 1213 1275 \n"
+     ]
+    }
+   ],
+   "source": [
+    "from nltk.corpus import udhr\n",
+    "languages = ['Chickasaw', 'English', 'German_Deutsch',\n",
+    "             'Greenlandic_Inuktikut', 'Hungarian_Magyar', 'Ibibio_Efik']\n",
+    "cfd = nltk.ConditionalFreqDist(\n",
+    "    (lang, len(word))\n",
+    "    for lang in languages\n",
+    "    for word in udhr.words(lang + '-Latin1'))\n",
+    "cfd.tabulate(conditions=['English', 'German_Deutsch'],\n",
+    "             samples=range(10), cumulative=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58a755c9-5f7b-48b0-aaf7-67abe19bd7bc",
+   "metadata": {},
+   "source": [
+    "# Summary: 4 types of corpus\n",
+    "1. Isolated (e.g. Gutenberg)\n",
+    "2. Categorized (e.g. Brown)\n",
+    "3. Overlapping (e.g. Reuters)\n",
+    "4. Temporal (e.g. Inaugural)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}