{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<div class=\"alert alert-block alert-info\">\n", "<h1> Ekstrakcja informacji </h1>\n", "<h2> 0. <i>Jezyk</i> [ćwiczenia]</h2> \n", "<h3> Jakub Pokrywka (2022)</h3>\n", "</div>\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 278, "metadata": {}, "outputs": [], "source": [ "NR_INDEKSU = 375985" ] }, { "cell_type": "code", "execution_count": 65, "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": "code", "execution_count": 68, "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": 77, "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] Unzipping corpora/stopwords.zip.\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nltk.download('punkt')\n", "nltk.download('stopwords')" ] }, { "cell_type": "code", "execution_count": 78, "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": 78, "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": 79, "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": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nltk.tokenize.sent_tokenize(text)" ] }, { "cell_type": "code", "execution_count": 80, "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": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nltk.corpus.stopwords.words('german')" ] }, { "cell_type": "code", "execution_count": 84, "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": 57, "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "słowo=%{x}<br>liczba=%{y}<extra></extra>", "legendgroup": "", "marker": { "color": "#636efa", "pattern": { "shape": "" } }, "name": "", "offsetgroup": "", "orientation": "v", 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{\"responsive\": true} ).then(function(){\n", " \n", "var gd = document.getElementById('4c1ff49c-bfbe-4479-af48-39178a3ed8a7');\n", "var x = new MutationObserver(function (mutations, observer) {{\n", " var display = window.getComputedStyle(gd).display;\n", " if (!display || display === 'none') {{\n", " console.log([gd, 'removed!']);\n", " Plotly.purge(gd);\n", " observer.disconnect();\n", " }}\n", "}});\n", "\n", "// Listen for the removal of the full notebook cells\n", "var notebookContainer = gd.closest('#notebook-container');\n", "if (notebookContainer) {{\n", " x.observe(notebookContainer, {childList: true});\n", "}}\n", "\n", "// Listen for the clearing of the current output cell\n", "var outputEl = gd.closest('.output');\n", "if (outputEl) {{\n", " x.observe(outputEl, {childList: true});\n", "}}\n", "\n", " }) }; }); </script> </div>" ] }, "metadata": {}, "output_type": "display_data" } ], "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": 49, "metadata": {}, "outputs": [], "source": [ "?px.histogram" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ZADANIE 1 \n", "\n", "ZNAJDŹ PRZYKŁAD TEKSTÓW Z TEJ SAMEJ DOMENY 1_000_000 słów (20 punktów):\n", "- język angielski \n", "- język polski\n", "- język z rodziny romańskich\n", "\n", "Narzędzia:\n", "- nltk, plotly express\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\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) w taki sposób żeby był maksymalnie czytelny, wypróbuj skali logarytmicznej x, y, usuwanie słów poniżej limitu wystąpień itp. \n", "- dla próbki 10000 zdań sprawdź jak często langdetect https://pypi.org/project/langdetect/ się myli i jakie języki odgaduje \n", "- zilustruj prawo zipfa ( px.line z zaznaczonymi punktami)\n", "- napisz wnioski (20-50 zdań)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### START ZADANIA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### KONIEC ZADANIA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ZADANIE\n", "\n", "Weź teksty w języku polskim (mają składać sie po 5 osobnych dokumentów każdy:\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", "- zilustruj gunning_fog INDEX z https://pypi.org/project/textstat/ (oś y) i średnią długość zdania (oś x) na jednym wykresie narysuj dla wszystkich tekstów na jednym wykresie , domeny oznacz kolorami (px.scatter)\n", "- zilustruj prawo Heap'a dla wszystkich tekstów na jednym wykresie, domeny oznacz kolorami (px.scatter)\n", "- napisz wnioski (20-50 zdań)\n", "\n", "NAPISZ WNIOSKI\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### START ZADANIA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### KONIEC ZADANIA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## WYKONANIE ZADAŃ\n", "Zgodnie z instrukcją 01_Kodowanie_tekstu.ipynb" ] } ], "metadata": { "author": "Jakub Pokrywka", "email": "kubapok@wmi.amu.edu.pl", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "lang": "pl", "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" }, "subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]", "title": "Ekstrakcja informacji", "year": "2021" }, "nbformat": 4, "nbformat_minor": 4 }