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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka </h1>\n",
"<h2> 2. <i>Język</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"import plotly.express as px\n",
"import numpy as np\n",
"import pandas as pd\n",
"import nltk"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://github.com/sdadas/polish-nlp-resources"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"program : program\n",
"programs : program\n",
"programmer : programm\n",
"programming : program\n",
"programmers : programm\n"
]
}
],
"source": [
"ps = nltk.stem.PorterStemmer()\n",
"\n",
"for w in [\"program\", \"programs\", \"programmer\", \"programming\", \"programmers\"]:\n",
" print(w, \" : \", ps.stem(w))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /home/kuba/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n",
"[nltk_data] Downloading package stopwords to /home/kuba/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nltk.download('punkt')\n",
"nltk.download('stopwords')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Python',\n",
" 'is',\n",
" 'dynamically-typed',\n",
" 'and',\n",
" 'garbage-collected',\n",
" '.',\n",
" 'It',\n",
" 'supports',\n",
" 'multiple',\n",
" 'programming',\n",
" 'paradigms',\n",
" ',',\n",
" 'including',\n",
" 'structured',\n",
" '(',\n",
" 'particularly',\n",
" ',',\n",
" 'procedural',\n",
" ')',\n",
" ',',\n",
" 'object-oriented',\n",
" 'and',\n",
" 'functional',\n",
" 'programming',\n",
" '.',\n",
" 'It',\n",
" 'is',\n",
" 'often',\n",
" 'described',\n",
" 'as',\n",
" 'a',\n",
" '``',\n",
" 'batteries',\n",
" 'included',\n",
" \"''\",\n",
" 'language',\n",
" 'due',\n",
" 'to',\n",
" 'its',\n",
" 'comprehensive',\n",
" 'standard',\n",
" 'library',\n",
" '.']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text = \"\"\"Python is dynamically-typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented and functional programming. It is often described as a \"batteries included\" language due to its comprehensive standard library.\"\"\"\n",
"nltk.tokenize.word_tokenize(text)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Python is dynamically-typed and garbage-collected.',\n",
" 'It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented and functional programming.',\n",
" 'It is often described as a \"batteries included\" language due to its comprehensive standard library.']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nltk.tokenize.sent_tokenize(text)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['aber',\n",
" 'alle',\n",
" 'allem',\n",
" 'allen',\n",
" 'aller',\n",
" 'alles',\n",
" 'als',\n",
" 'also',\n",
" 'am',\n",
" 'an',\n",
" 'ander',\n",
" 'andere',\n",
" 'anderem',\n",
" 'anderen',\n",
" 'anderer',\n",
" 'anderes',\n",
" 'anderm',\n",
" 'andern',\n",
" 'anderr',\n",
" 'anders',\n",
" 'auch',\n",
" 'auf',\n",
" 'aus',\n",
" 'bei',\n",
" 'bin',\n",
" 'bis',\n",
" 'bist',\n",
" 'da',\n",
" 'damit',\n",
" 'dann',\n",
" 'der',\n",
" 'den',\n",
" 'des',\n",
" 'dem',\n",
" 'die',\n",
" 'das',\n",
" 'dass',\n",
" 'daß',\n",
" 'derselbe',\n",
" 'derselben',\n",
" 'denselben',\n",
" 'desselben',\n",
" 'demselben',\n",
" 'dieselbe',\n",
" 'dieselben',\n",
" 'dasselbe',\n",
" 'dazu',\n",
" 'dein',\n",
" 'deine',\n",
" 'deinem',\n",
" 'deinen',\n",
" 'deiner',\n",
" 'deines',\n",
" 'denn',\n",
" 'derer',\n",
" 'dessen',\n",
" 'dich',\n",
" 'dir',\n",
" 'du',\n",
" 'dies',\n",
" 'diese',\n",
" 'diesem',\n",
" 'diesen',\n",
" 'dieser',\n",
" 'dieses',\n",
" 'doch',\n",
" 'dort',\n",
" 'durch',\n",
" 'ein',\n",
" 'eine',\n",
" 'einem',\n",
" 'einen',\n",
" 'einer',\n",
" 'eines',\n",
" 'einig',\n",
" 'einige',\n",
" 'einigem',\n",
" 'einigen',\n",
" 'einiger',\n",
" 'einiges',\n",
" 'einmal',\n",
" 'er',\n",
" 'ihn',\n",
" 'ihm',\n",
" 'es',\n",
" 'etwas',\n",
" 'euer',\n",
" 'eure',\n",
" 'eurem',\n",
" 'euren',\n",
" 'eurer',\n",
" 'eures',\n",
" 'für',\n",
" 'gegen',\n",
" 'gewesen',\n",
" 'hab',\n",
" 'habe',\n",
" 'haben',\n",
" 'hat',\n",
" 'hatte',\n",
" 'hatten',\n",
" 'hier',\n",
" 'hin',\n",
" 'hinter',\n",
" 'ich',\n",
" 'mich',\n",
" 'mir',\n",
" 'ihr',\n",
" 'ihre',\n",
" 'ihrem',\n",
" 'ihren',\n",
" 'ihrer',\n",
" 'ihres',\n",
" 'euch',\n",
" 'im',\n",
" 'in',\n",
" 'indem',\n",
" 'ins',\n",
" 'ist',\n",
" 'jede',\n",
" 'jedem',\n",
" 'jeden',\n",
" 'jeder',\n",
" 'jedes',\n",
" 'jene',\n",
" 'jenem',\n",
" 'jenen',\n",
" 'jener',\n",
" 'jenes',\n",
" 'jetzt',\n",
" 'kann',\n",
" 'kein',\n",
" 'keine',\n",
" 'keinem',\n",
" 'keinen',\n",
" 'keiner',\n",
" 'keines',\n",
" 'können',\n",
" 'könnte',\n",
" 'machen',\n",
" 'man',\n",
" 'manche',\n",
" 'manchem',\n",
" 'manchen',\n",
" 'mancher',\n",
" 'manches',\n",
" 'mein',\n",
" 'meine',\n",
" 'meinem',\n",
" 'meinen',\n",
" 'meiner',\n",
" 'meines',\n",
" 'mit',\n",
" 'muss',\n",
" 'musste',\n",
" 'nach',\n",
" 'nicht',\n",
" 'nichts',\n",
" 'noch',\n",
" 'nun',\n",
" 'nur',\n",
" 'ob',\n",
" 'oder',\n",
" 'ohne',\n",
" 'sehr',\n",
" 'sein',\n",
" 'seine',\n",
" 'seinem',\n",
" 'seinen',\n",
" 'seiner',\n",
" 'seines',\n",
" 'selbst',\n",
" 'sich',\n",
" 'sie',\n",
" 'ihnen',\n",
" 'sind',\n",
" 'so',\n",
" 'solche',\n",
" 'solchem',\n",
" 'solchen',\n",
" 'solcher',\n",
" 'solches',\n",
" 'soll',\n",
" 'sollte',\n",
" 'sondern',\n",
" 'sonst',\n",
" 'über',\n",
" 'um',\n",
" 'und',\n",
" 'uns',\n",
" 'unsere',\n",
" 'unserem',\n",
" 'unseren',\n",
" 'unser',\n",
" 'unseres',\n",
" 'unter',\n",
" 'viel',\n",
" 'vom',\n",
" 'von',\n",
" 'vor',\n",
" 'während',\n",
" 'war',\n",
" 'waren',\n",
" 'warst',\n",
" 'was',\n",
" 'weg',\n",
" 'weil',\n",
" 'weiter',\n",
" 'welche',\n",
" 'welchem',\n",
" 'welchen',\n",
" 'welcher',\n",
" 'welches',\n",
" 'wenn',\n",
" 'werde',\n",
" 'werden',\n",
" 'wie',\n",
" 'wieder',\n",
" 'will',\n",
" 'wir',\n",
" 'wird',\n",
" 'wirst',\n",
" 'wo',\n",
" 'wollen',\n",
" 'wollte',\n",
" 'würde',\n",
" 'würden',\n",
" 'zu',\n",
" 'zum',\n",
" 'zur',\n",
" 'zwar',\n",
" 'zwischen']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nltk.corpus.stopwords.words('german')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('Python', 'is'), ('is', 'dynamically-typed'), ('dynamically-typed', 'and'), ('and', 'garbage-collected'), ('garbage-collected', '.'), ('.', 'It'), ('It', 'supports'), ('supports', 'multiple'), ('multiple', 'programming'), ('programming', 'paradigms'), ('paradigms', ','), (',', 'including'), ('including', 'structured'), ('structured', '('), ('(', 'particularly'), ('particularly', ','), (',', 'procedural'), ('procedural', ')'), (')', ','), (',', 'object-oriented'), ('object-oriented', 'and'), ('and', 'functional'), ('functional', 'programming'), ('programming', '.'), ('.', 'It'), ('It', 'is'), ('is', 'often'), ('often', 'described'), ('described', 'as'), ('as', 'a'), ('a', '``'), ('``', 'batteries'), ('batteries', 'included'), ('included', \"''\"), (\"''\", 'language'), ('language', 'due'), ('due', 'to'), ('to', 'its'), ('its', 'comprehensive'), ('comprehensive', 'standard'), ('standard', 'library'), ('library', '.')]\n"
]
}
],
"source": [
"nltk_tokens = nltk.word_tokenize(text)\n",
"print(list(nltk.bigrams(nltk_tokens)))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" define('plotly', function(require, exports, module) {\n",
" /**\n",
"* plotly.js v2.4.2\n",
"* Copyright 2012-2021, Plotly, Inc.\n",
"* All rights reserved.\n",
"* Licensed under the MIT license\n",
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"!function(t){if(\"object\"==typeof exports&&\"undefined\"!=typeof module)module.exports=t();else if(\"function\"==typeof define&&define.amd)define([],t);else{(\"undefined\"!=typeof window?window:\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:this).Plotly=t()}}((function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l=\"function\"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error(\"Cannot find module '\"+o+\"'\");throw c.code=\"MODULE_NOT_FOUND\",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,(function(t){return i(e[o][1][t]||t)}),u,u.exports,t,e,r,n)}return r[o].exports}for(var a=\"function\"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}({1:[function(t,e,r){\"use strict\";var n=t(\"../src/lib\"),i={\"X,X div\":'direction:ltr;font-family:\"Open Sans\",verdana,arial,sans-serif;margin:0;padding:0;',\"X input,X button\":'font-family:\"Open Sans\",verdana,arial,sans-serif;',\"X input:focus,X button:focus\":\"outline:none;\",\"X a\":\"text-decoration:none;\",\"X a:hover\":\"text-decoration:none;\",\"X .crisp\":\"shape-rendering:crispEdges;\",\"X .user-select-none\":\"-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;user-select:none;\",\"X svg\":\"overflow:hidden;\",\"X svg a\":\"fill:#447adb;\",\"X svg a:hover\":\"fill:#3c6dc5;\",\"X .main-svg\":\"position:absolute;top:0;left:0;pointer-events:none;\",\"X .main-svg .draglayer\":\"pointer-events:all;\",\"X .cursor-default\":\"cursor:default;\",\"X .cursor-pointer\":\"cursor:pointer;\",\"X .cursor-crosshair\":\"cursor:crosshair;\",\"X .cursor-move\":\"cursor:move;\",\"X .cursor-col-resize\":\"cursor:col-resize;\",\"X .cursor-row-resize\":\"cursor:row-resize;\",\"X .cursor-ns-resize\":\"cursor:ns-resize;\",\"X .cursor-ew-resize\":\"cursor:ew-resize;\",\"X .cursor-sw-resize\":\"cursor:sw-resize;\",\"X .cursor-s-resize\":\"cursor:s-resize;\",\"X .cursor-se-resize\":\"cursor:se-resize;\",\"X .cursor-w-resize\":\"cursor:w-resize;\",\"X .cursor-e-resize\":\"cursor:e-resize;\",\"X .cursor-nw-resize\":\"cursor:nw-resize;\",\"X .cursor-n-resize\":\"cursor:n-resize;\",\"X .cursor-ne-resize\":\"cursor:ne-resize;\",\"X .cursor-grab\":\"cursor:-webkit-grab;cursor:grab;\",\"X .modebar\":\"position:absolute;top:2px;right:2px;\",\"X .ease-bg\":\"-webkit-transition:background-color .3s ease 0s;-moz-transition:background-color .3s ease 0s;-ms-transition:background-color .3s ease 0s;-o-transition:background-color .3s ease 0s;transition:background-color .3s ease 0s;\",\"X .modebar--hover>:not(.watermark)\":\"opacity:0;-webkit-transition:opacity .3s ease 0s;-moz-transition:opacity .3s ease 0s;-ms-transition:opacity .3s ease 0s;-o-transition:opacity .3s ease 0s;transition:opacity .3s ease 0s;\",\"X:hover .modebar--hover .modebar-group\":\"opacity:1;\",\"X .modebar-group\":\"float:left;display:inline-block;box-sizing:border-box;padding-left:8px;position:relative;vertical-align:middle;white-space:nowrap;\",\"X .modebar-btn\":\"position:relative;font-size:16px;padding:3px 4px;height:22px;cursor:pointer;line-height:normal;box-sizing:border-box;\",\"X .modebar-btn svg\":\"position:relative;top:2px;\",\"X .modebar.vertical\":\"display:flex;flex-direction:column;flex-wrap:wrap;align-content:flex-end;max-height:100%;\",\"X .modebar.vertical svg\":\"top:-1px;\",\"X .modebar.vertical .modebar-group\":\"display:block;float:none;padding-left:0px;padding-bottom:8px;\",\"X .modebar.vertical .modebar-group .modebar-btn\":\"display:block;text-align:center;\",\"X [data-title]:before,X [data-title]:after\":\"position:absolute;-webkit-transform:translate3d(0, 0, 0);-moz-transform:translate3d(0, 0, 0);-ms-transform:translate3d(0, 0, 0);-o-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0);display:none;opacity:0;z-index:1001;pointer-events:none;top:110%;right:50%;\",\"X [data-title]:hover:before,X [data-title]:hover:after\":\"display:block;opacity:1;\",\"X [data-title]:before\":'content:\"\";position:absolute;background:transparent;border:6px solid transparent;z-index:10
"/*!\n",
" * The buffer module from node.js, for the browser.\n",
" *\n",
" * @author Feross Aboukhadijeh <feross@feross.org> <http://feross.org>\n",
" * @license MIT\n",
" */function i(t,e){if(t===e)return 0;for(var r=t.length,n=e.length,i=0,a=Math.min(r,n);i<a;++i)if(t[i]!==e[i]){r=t[i],n=e[i];break}return r<n?-1:n<r?1:0}function a(t){return r.Buffer&&\"function\"==typeof r.Buffer.isBuffer?r.Buffer.isBuffer(t):!(null==t||!t._isBuffer)}var o=t(\"util/\"),s=Object.prototype.hasOwnProperty,l=Array.prototype.slice,c=\"foo\"===function(){}.name;function u(t){return Object.prototype.toString.call(t)}function f(t){return!a(t)&&(\"function\"==typeof r.ArrayBuffer&&(\"function\"==typeof ArrayBuffer.isView?ArrayBuffer.isView(t):!!t&&(t instanceof DataView||!!(t.buffer&&t.buffer instanceof ArrayBuffer))))}var h=e.exports=y,p=/\\s*function\\s+([^\\(\\s]*)\\s*/;function d(t){if(o.isFunction(t)){if(c)return t.name;var e=t.toString().match(p);return e&&e[1]}}function g(t,e){return\"string\"==typeof t?t.length<e?t:t.slice(0,e):t}function m(t){if(c||!o.isFunction(t))return o.inspect(t);var e=d(t);return\"[Function\"+(e?\": \"+e:\"\")+\"]\"}function v(t,e,r,n,i){throw new h.AssertionError({message:r,actual:t,expected:e,operator:n,stackStartFunction:i})}function y(t,e){t||v(t,!0,e,\"==\",h.ok)}function x(t,e,r,n){if(t===e)return!0;if(a(t)&&a(e))return 0===i(t,e);if(o.isDate(t)&&o.isDate(e))return t.getTime()===e.getTime();if(o.isRegExp(t)&&o.isRegExp(e))return t.source===e.source&&t.global===e.global&&t.multiline===e.multiline&&t.lastIndex===e.lastIndex&&t.ignoreCase===e.ignoreCase;if(null!==t&&\"object\"==typeof t||null!==e&&\"object\"==typeof e){if(f(t)&&f(e)&&u(t)===u(e)&&!(t instanceof Float32Array||t instanceof Float64Array))return 0===i(new Uint8Array(t.buffer),new Uint8Array(e.buffer));if(a(t)!==a(e))return!1;var s=(n=n||{actual:[],expected:[]}).actual.indexOf(t);return-1!==s&&s===n.expected.indexOf(e)||(n.actual.push(t),n.expected.push(e),function(t,e,r,n){if(null==t||null==e)return!1;if(o.isPrimitive(t)||o.isPrimitive(e))return t===e;if(r&&Object.getPrototypeOf(t)!==Object.getPrototypeOf(e))return!1;var i=b(t),a=b(e);if(i&&!a||!i&&a)return!1;if(i)return t=l.call(t),e=l.call(e),x(t,e,r);var s,c,u=T(t),f=T(e);if(u.length!==f.length)return!1;for(u.sort(),f.sort(),c=u.length-1;c>=0;c--)if(u[c]!==f[c])return!1;for(c=u.length-1;c>=0;c--)if(s=u[c],!x(t[s],e[s],r,n))return!1;return!0}(t,e,r,n))}return r?t===e:t==e}function b(t){return\"[object Arguments]\"==Object.prototype.toString.call(t)}function _(t,e){if(!t||!e)return!1;if(\"[object RegExp]\"==Object.prototype.toString.call(e))return e.test(t);try{if(t instanceof e)return!0}catch(t){}return!Error.isPrototypeOf(e)&&!0===e.call({},t)}function w(t,e,r,n){var i;if(\"function\"!=typeof e)throw new TypeError('\"block\" argument must be a function');\"string\"==typeof r&&(n=r,r=null),i=function(t){var e;try{t()}catch(t){e=t}return e}(e),n=(r&&r.name?\" (\"+r.name+\").\":\".\")+(n?\" \"+n:\".\"),t&&!i&&v(i,r,\"Missing expected exception\"+n);var a=\"string\"==typeof n,s=!t&&i&&!r;if((!t&&o.isError(i)&&a&&_(i,r)||s)&&v(i,r,\"Got unwanted exception\"+n),t&&i&&r&&!_(i,r)||!t&&i)throw i}h.AssertionError=function(t){this.name=\"AssertionError\",this.actual=t.actual,this.expected=t.expected,this.operator=t.operator,t.message?(this.message=t.message,this.generatedMessage=!1):(this.message=function(t){return g(m(t.actual),128)+\" \"+t.operator+\" \"+g(m(t.expected),128)}(this),this.generatedMessage=!0);var e=t.stackStartFunction||v;if(Error.captureStackTrace)Error.captureStackTrace(this,e);else{var r=new Error;if(r.stack){var n=r.stack,i=d(e),a=n.indexOf(\"\\n\"+i);if(a>=0){var o=n.indexOf(\"\\n\",a+1);n=n.substring(o+1)}this.stack=n}}},o.inherits(h.AssertionError,Error),h.fail=v,h.ok=y,h.equal=function(t,e,r){t!=e&&v(t,e,r,\"==\",h.equal)},h.notEqual=function(t,e,r){t==e&&v(t,e,r,\"!=\",h.notEqual)},h.deepEqual=function(t,e,r){x(t,e,!1)||v(t,e,r,\"deepEqual\",h.deepEqual)},h.deepStrictEqual=function(t,e,r){x(t,e,!0)||v(t,e,r,\"deepStrictEqual\",h.deepStrictEqual)},h.notDeepEqual=function(t,e,r){x(t,e,!1)&&v(t,e,r,\"notDeepEqual\",h.notDeepEqual)},h.notDeepStrictEqual=function t(e,r,n){x(e,r,!0)&&v(e,r,n,\"notDeepStrictEqual\",t)},h.strictEqual=function(t,e
"/*!\n",
" * The buffer module from node.js, for the browser.\n",
" *\n",
" * @author Feross Aboukhadijeh <https://feross.org>\n",
" * @license MIT\n",
" */\n",
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"*/\n",
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"object-assign\n",
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"/*!\n",
" * pad-left <https://github.com/jonschlinkert/pad-left>\n",
" *\n",
" * Copyright (c) 2014-2015, Jon Schlinkert.\n",
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],
"source": [
"df = pd.DataFrame([[random.choice(['ang','polski','hiszp']), np.random.geometric(0.2)] for i in range(5000) ], columns=['jezyk', 'dlugosc'])\n",
"fig = px.histogram(df, x=\"dlugosc\",facet_row='jezyk',nbins=50, hover_data=df.columns)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"?px.histogram"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ZADANIE 1 \n",
"\n",
"(40 punktów)\n",
"\n",
"ZNAJDŹ PRZYKŁAD TEKSTÓW Z TEJ SAMEJ DOMENY 1_000_000 słów albo nawet tłumaczenie :\n",
"- język angielski \n",
"- język polski\n",
"- język z rodziny romańskich\n",
"\n",
"Proponowane narzędzia:\n",
"- nltk\n",
"- plotly express\n",
"- biblioteka collections\n",
"- spacy (niekoniecznie)\n",
"\n",
"\n",
"Dla każdego z języków:\n",
"- policz ilosć unikalnych lowercase słów (ze stemmingiem i bez)\n",
"- policz ilosć znaków\n",
"- policz ilosć unikalnych znaków\n",
"- policz ilosć zdań zdań\n",
"- policz ilosć unikalnych zdań\n",
"- podaj min, max, średnią oraz medianę ilości znaków w słowie \n",
"- podaj min, max, średnią oraz medianę ilości słów w zdaniu, znajdz najkrotsze i najdluzsze zdania\n",
"- wygeneruj word cloud (normalnie i po usunięciu stopwordów)\n",
"- wypisz 20 najbardziej popularnych słów (normalnie i po usunięciu stopwordów) (lowercase)\n",
"- wypisz 20 najbardziej popularnych bigramów (normalnie i po usunięciu stopwordów)\n",
"- narysuj wykres częstotliwości słów (histogram lub linie) w taki sposób żeby był czytelny, wypróbuj skali logarytmicznej dla osi x (ale na razie nie dla y), usuwanie słów poniżej limitu wystąpień itp.\n",
"- punkt jak wyżej, tylko dla bigramów\n",
"- punkt jak wyżej, tylko dla znaków\n",
"- narysuj wykres barplot dla części mowy (PART OF SPEECH TAGS, tylko pierwszy stopień zagłębienia)\n",
"- dla próbki 10000 zdań sprawdź jak często langdetect https://pypi.org/project/langdetect/ się myli i w jaki sposób.\n",
"- zilustruj prawo zipfa ( px.line z zaznaczonymi punktami)\n",
"- napisz wnioski (10-50 zdań)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### START ZADANIA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### KONIEC ZADANIA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://github.com/sdadas/polish-nlp-resources\n",
"\n",
"## Indeks czytelności Foga\n",
"\n",
"\n",
"Indeks czytelności Foga (Fog index) ilustruje stopień trudności tekstu. Nim wyższa liczba, tym trudniejszy jest tekst w odbiorze. Ze względu na charakterystyki różnych języków nie powinno porównywać sie tekstów pisanych w różnych językach. Index służy do porównywania różnych tekstów w tym samym języku.\n",
"\n",
"$$FOG = 0.4\\left(\\frac{liczba\\ słów}{liczba\\ zdań} + 100\\left(\\frac{liczba\\ słów\\ skomplikowanych}{liczba\\ słów}\\right) \\right)$$\n",
"\n",
"Słowa skomplikowane mogą pochodzić ze specjalnej listy, jeżeli nie ma takiej listy, to można przyjąć że są to słowa składające sie z więcej niz 3 sylab (dla języka polskiego)\n",
"\n",
"Indeks czytelności Foga jest skutecznym narzędziem przy pisaniu tekstów. Jeżeli indeks jest zbyt wysoki należy uprościć tekst. Można to zrobić przez redukcje średniej długości zdania $\\frac{liczba słów}{liczba zdań}$ oraz uproszczenie skompikowanych lub długich słów.\n",
"\n",
"## Prawo Heapsa\n",
"\n",
"Prawo heapsa to empiryczne prawo lingwistyczne. Stanowi, że liczba odmiennych słów rośnie wykładniczo (z wykładnikiem <1) względem długości dokumentu.\n",
"\n",
"Ilosć odmiennych słów $V_R$ względem całkowitej ilości słów w tekście $n$ można opisać wzorem:\n",
"\n",
"$$V_R(n) = Kn^{\\beta},$$\n",
"\n",
"gdzie $K$ i $\\beta$ to parametry wyznaczone empirycznie.\n",
"\n",
"Podobnie do Indeksu czytelności Foga nie powinno się porównywać różnych tekstów w różnych językach. Jednak porównanie tego samego tekstu przetłumaczonego na różne języki daje ich wgląd w ich charakterystykę.\n",
"\n",
"## ZADANIE 1\n",
"\n",
"(50 punktów)\n",
"\n",
"Znajdź teksty w języku polskim (mają składać sie po 5 osobnych dokumentów każdy, długości powinny być różne):\n",
"- tekst prawny\n",
"- tekst naukowy\n",
"- tekst z polskiego z powieści (np. wolne lektury)\n",
"- tekst z polskiego internetu (reddit, wykop, komentarze)\n",
"- transkrypcja tekstu mówionego\n",
"\n",
"\n",
"\n",
"ZADANIA:\n",
"- zilustruj gunning fog index (oś y) i średnią długość zdania (oś x) na jednym wykresie dla wszystkich tekstów, domeny oznacz kolorami (px.scatter), dla języka polskiego traktuj wyrazy długie jako te powyżej 3 sylab, możesz użyć https://pyphen.org/ do liczenia sylab\n",
"- zilustruj prawo Heaps'a dla wszystkich tekstów na jednym wykresie, domeny oznacz kolorami (px.scatter)\n",
"- napisz wnioski (10-50 zdań)\n",
"\n",
"\n",
"#### START ZADANIA\n",
"\n",
"#### KONIEC ZADANIA\n",
"\n",
"## WYKONANIE ZADAŃ\n",
"Zgodnie z instrukcją 01_Kodowanie_tekstu.ipynb"
]
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"metadata": {},
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],
"metadata": {
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
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"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",
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