forked from bfijalkowski/KWT-2024
664 lines
18 KiB
Plaintext
664 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "coastal-lincoln",
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"metadata": {},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Komputerowe wspomaganie tłumaczenia </h1>\n",
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"<h2> 3. <i>Terminologia</i> [laboratoria]</h2> \n",
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"<h3>Rafał Jaworski (2021)</h3>\n",
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"</div>\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "markdown",
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"id": "aggregate-listing",
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"metadata": {},
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"source": [
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"Na dzisiejszych zajęciach zajmiemy się bliżej słownikami używanymi do wspomagania tłumaczenia. Oczywiście na rynku dostępnych jest bardzo wiele słowników w formacie elektronicznym. Wiele z nich jest gotowych do użycia w SDL Trados, memoQ i innych narzędziach CAT. Zawierają one setki tysięcy lub miliony haseł i oferują natychmiastową pomoc tłumaczowi."
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]
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},
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{
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"cell_type": "markdown",
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"id": "israeli-excuse",
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"metadata": {},
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"source": [
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"Problem jednak w tym, iż często nie zawierają odpowiedniej terminologii specjalistycznej - używanej przez klienta zamawiającego tłumaczenie. Terminy specjalistyczne są bardzo częste w tekstach tłumaczonych ze względu na następujące zjawiska:\n",
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"- Teksty o tematyce ogólnej są tłumaczone dość rzadko (nikt nie tłumaczy pocztówek z pozdrowieniami z wakacji...)\n",
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"- Te same słowa mogą mieć zarówno znaczenie ogólne, jak i bardzo specjalistyczne (np. \"dziedziczenie\" w kontekście prawnym lub informatycznym)\n",
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"- Klient używa nazw lub słów wymyślonych przez siebie, np. na potrzeby marketingowe."
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]
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},
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{
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"cell_type": "markdown",
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"id": "reflected-enforcement",
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"metadata": {},
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"source": [
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"Nietrywialnymi zadaniami stają się: odnalezienie terminu specjalistycznego w tekście źródłowym oraz podanie prawidłowego tłumaczenia tego terminu na język docelowy"
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]
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},
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{
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"cell_type": "markdown",
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"id": "statutory-florist",
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"metadata": {},
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"source": [
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"Brzmi prosto? Spróbujmy wykonać ręcznie tę drugą operację."
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]
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},
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{
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"cell_type": "markdown",
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"id": "danish-anchor",
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"metadata": {},
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"source": [
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"### Ćwiczenie 1: Podaj tłumaczenie terminu \"prowadnice szaf metalowych\" na język angielski. Opisz, z jakich narzędzi skorzystałaś/eś."
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]
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},
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{
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"cell_type": "markdown",
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"id": "diverse-sunglasses",
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"metadata": {},
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"source": [
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"Odpowiedź: metal cabinet guides lub metal cabinet slides. Skorzystalem z dwoch slownikow oraz duzego modelu jezykowego."
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]
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},
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{
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"cell_type": "markdown",
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"id": "limited-waterproof",
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"metadata": {},
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"source": [
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"W dalszych ćwiczeniach skupimy się jednak na odszukaniu terminu specjalistycznego w tekście. W tym celu będą potrzebne dwie operacje:\n",
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"1. Przygotowanie słownika specjalistycznego.\n",
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"2. Detekcja terminologii przy użyciu przygotowanego słownika specjalistycznego."
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]
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},
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{
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"cell_type": "markdown",
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"id": "literary-blues",
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"metadata": {},
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"source": [
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"Zajmijmy się najpierw krokiem nr 2 (gdyż jest prostszy). Rozważmy następujący tekst:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 102,
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"id": "loving-prince",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \" For all Java programmers:\"\n",
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"text += \" This section explains how to compile and run a Swing application from the command line.\"\n",
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"text += \" For information on compiling and running a Swing application using NetBeans IDE,\"\n",
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"text += \" see Running Tutorial Examples in NetBeans IDE. The compilation instructions work for all Swing programs\"\n",
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"text += \" — applets, as well as applications. Here are the steps you need to follow:\"\n",
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"text += \" Install the latest release of the Java SE platform, if you haven't already done so.\"\n",
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"text += \" Create a program that uses Swing components. Compile the program. Run the program.\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "extreme-cycling",
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"metadata": {},
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"source": [
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"Załóżmy, że posiadamy następujący słownik:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 103,
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"id": "bound-auction",
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"metadata": {},
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"outputs": [],
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"source": [
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"dictionary = ['program', 'application', 'applet', 'compile']"
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]
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},
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{
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"cell_type": "markdown",
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"id": "other-trinidad",
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"metadata": {},
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"source": [
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"### Ćwiczenie 2: Napisz program, który wypisze pozycje wszystkich wystąpień poszczególnych terminów specjalistycznych. Dla każdego terminu należy wypisać listę par (pozycja_startowa, pozycja końcowa)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 104,
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"id": "cognitive-cedar",
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"metadata": {},
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"outputs": [],
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"source": [
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"import re\n",
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"\n",
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"def terminology_lookup(txt, labels):\n",
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" results = []\n",
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"\n",
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" for label in labels:\n",
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" results.append((\n",
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" label,\n",
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" [(m.start(), m.end() - 1) for m in re.finditer(label, txt)]\n",
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" ))\n",
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"\n",
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" return results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 105,
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"id": "7cc3ad1f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('program', [(14, 20), (291, 297), (468, 474), (516, 522), (533, 539)]),\n",
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" ('application', [(80, 90), (164, 174), (322, 332)]),\n",
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" ('applet', [(302, 307)]),\n",
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" ('compile', [(56, 62)])]"
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]
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},
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"execution_count": 105,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"terminology_lookup(text, dictionary)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "interior-things",
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"metadata": {},
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"source": [
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"Zwykłe wyszukiwanie w tekście ma pewne wady. Na przykład, gdy szukaliśmy słowa \"program\", złapaliśmy przypadkiem słowo \"programmer\". Złapaliśmy także słowo \"programs\", co jest poprawne, ale niepoprawnie podaliśmy jego pozycję w tekście."
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]
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},
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{
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"cell_type": "markdown",
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"id": "aggressive-plane",
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"metadata": {},
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"source": [
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"Żeby poradzić sobie z tymi problemami, musimy wykorzystać techniki przetwarzania języka naturalnego. Wypróbujmy pakiet spaCy:\n",
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"\n",
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"`pip3 install spacy`\n",
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"\n",
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"oraz\n",
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"\n",
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"`python3 -m spacy download en_core_web_sm`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 106,
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"id": "tribal-attention",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" 0\n",
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"For for 1\n",
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"all all 5\n",
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"Java Java 9\n",
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"programmers programmer 14\n",
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": : 25\n",
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"This this 27\n",
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"section section 32\n",
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"explains explain 40\n",
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"how how 49\n",
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"to to 53\n",
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"compile compile 56\n",
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"and and 64\n",
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"run run 68\n",
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"a a 72\n",
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"Swing swing 74\n",
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"application application 80\n",
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"from from 92\n",
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"the the 97\n",
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"command command 101\n",
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"line line 109\n",
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". . 113\n",
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"For for 115\n",
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"information information 119\n",
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"on on 131\n",
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"compiling compile 134\n",
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"and and 144\n",
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"running run 148\n",
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"a a 156\n",
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"Swing swing 158\n",
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"application application 164\n",
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"using use 176\n",
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"NetBeans NetBeans 182\n",
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"IDE IDE 191\n",
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", , 194\n",
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"see see 196\n",
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"Running run 200\n",
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"Tutorial Tutorial 208\n",
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"Examples Examples 217\n",
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"in in 226\n",
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"NetBeans NetBeans 229\n",
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"IDE IDE 238\n",
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". . 241\n",
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"The the 243\n",
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"compilation compilation 247\n",
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"instructions instruction 259\n",
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"work work 272\n",
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"for for 277\n",
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"all all 281\n",
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"Swing Swing 285\n",
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"programs program 291\n",
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"— — 300\n",
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"applets applet 302\n",
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", , 309\n",
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"as as 311\n",
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"well well 314\n",
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"as as 319\n",
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"applications application 322\n",
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". . 334\n",
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"Here here 336\n",
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"are be 341\n",
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"the the 345\n",
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"steps step 349\n",
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"you you 355\n",
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"need need 359\n",
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"to to 364\n",
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"follow follow 367\n",
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": : 373\n",
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"Install install 375\n",
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"the the 383\n",
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"latest late 387\n",
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"release release 394\n",
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"of of 402\n",
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"the the 405\n",
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"Java Java 409\n",
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"SE SE 414\n",
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"platform platform 417\n",
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", , 425\n",
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"if if 427\n",
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"you you 430\n",
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"have have 434\n",
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"n't not 438\n",
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"already already 442\n",
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"done do 450\n",
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"so so 455\n",
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". . 457\n",
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"Create create 459\n",
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"a a 466\n",
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"program program 468\n",
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"that that 476\n",
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"uses use 481\n",
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"Swing swing 486\n",
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"components component 492\n",
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". . 502\n",
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"Compile compile 504\n",
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"the the 512\n",
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"program program 516\n",
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". . 523\n",
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"Run run 525\n",
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"the the 529\n",
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"program program 533\n",
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". . 540\n"
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]
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}
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],
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"source": [
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"import spacy\n",
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"nlp = spacy.load(\"en_core_web_sm\")\n",
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"\n",
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"doc = nlp(text)\n",
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"\n",
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"for token in doc:\n",
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" print(token, token.lemma_, token.idx)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "regional-craft",
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"metadata": {},
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"source": [
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"Sukces! Nastąpił podział tekstu na słowa (tokenizacja) oraz sprowadzenie do formy podstawowej każdego słowa (lematyzacja)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "toxic-subsection",
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"metadata": {},
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"source": [
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"### Ćwiczenie 3: Zmodyfikuj program z ćwiczenia 2 tak, aby zwracał również odmienione słowa. Na przykład, dla słowa \"program\" powinien znaleźć również \"programs\", ustawiając pozycje w tekście odpowiednio dla słowa \"programs\". Wykorzystaj właściwość idx tokenu."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 107,
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"id": "surgical-demonstration",
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"metadata": {},
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"outputs": [],
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"source": [
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"import spacy\n",
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"nlp = spacy.load(\"en_core_web_sm\")\n",
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"\n",
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"\n",
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"def terminology_lookup(txt, labels):\n",
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" result = {};\n",
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" doc = nlp(txt)\n",
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"\n",
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" for token in doc:\n",
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" if token.lemma_ in labels: \n",
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" if token.lemma_ not in result:\n",
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" result[token.lemma_] = []\n",
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" result[token.lemma_].append((token.idx, token.idx + len(token)))\n",
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"\n",
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" return result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 108,
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"id": "4772c1b1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'compile': [(56, 63), (134, 143), (504, 511)],\n",
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" 'application': [(80, 91), (164, 175), (322, 334)],\n",
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" 'program': [(291, 299), (468, 475), (516, 523), (533, 540)],\n",
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" 'applet': [(302, 309)]}"
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]
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},
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"execution_count": 108,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"terminology_lookup(text, dictionary)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "straight-letter",
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"metadata": {},
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"source": [
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"Teraz czas zająć się problemem przygotowania słownika specjalistycznego. W tym celu napiszemy nasz własny ekstraktor terminologii. Wejściem do ekstraktora będzie tekst zawierający specjalistyczną terminologię. Wyjściem - lista terminów."
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]
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},
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{
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"cell_type": "markdown",
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"id": "nearby-frontier",
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"metadata": {},
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"source": [
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"Przyjmijmy następujące podejście - terminami specjalistycznymi będą najcześćiej występujące rzeczowniki w tekście. Wykonajmy krok pierwszy:"
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]
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},
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{
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"cell_type": "markdown",
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"id": "harmful-lightning",
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"metadata": {},
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"source": [
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"### Ćwiczenie 4: Wypisz wszystkie rzeczowniki z tekstu. Wykorzystaj możliwości spaCy."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 109,
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"id": "superb-butterfly",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_nouns(text):\n",
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" doc = nlp(text)\n",
|
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" return [token.lemma_ for token in doc if token.pos_ == 'NOUN']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 110,
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"id": "3c916a3e",
|
|
"metadata": {},
|
|
"outputs": [
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{
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|
"data": {
|
|
"text/plain": [
|
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"['programmer',\n",
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" 'section',\n",
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" 'swing',\n",
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" 'application',\n",
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" 'command',\n",
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" 'line',\n",
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" 'information',\n",
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" 'swing',\n",
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" 'application',\n",
|
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" 'compilation',\n",
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" 'instruction',\n",
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" 'program',\n",
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" 'applet',\n",
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" 'application',\n",
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" 'step',\n",
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" 'release',\n",
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" 'platform',\n",
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" 'program',\n",
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" 'swing',\n",
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" 'component',\n",
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" 'program',\n",
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" 'program']"
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]
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},
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"execution_count": 110,
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
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],
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"source": [
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"get_nouns(text)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "musical-creator",
|
|
"metadata": {},
|
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"source": [
|
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"Teraz czas na podliczenie wystąpień poszczególnych rzeczowników. Uwaga - różne formy tego samego słowa zliczamy razem jako wystąpienia tego słowa (np. \"program\" i \"programs\"). Najwygodniejszą metodą podliczania jest zastosowanie tzw. tally (po polsku \"zestawienie\"). Jest to słownik, którego kluczem jest słowo w formie podstawowej, a wartością liczba wystąpień tego słowa, wliczając słowa odmienione. Przykład gotowego tally:"
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]
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},
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{
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"cell_type": "code",
|
|
"execution_count": 111,
|
|
"id": "acting-tolerance",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tally = {\"program\" : 4, \"component\" : 1}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "vanilla-estimate",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Ćwiczenie 5: Napisz program do ekstrakcji terminologii z tekstu według powyższych wytycznych."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 112,
|
|
"id": "eight-redhead",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def count_words(words):\n",
|
|
" word_count = {}\n",
|
|
" for word in words:\n",
|
|
" if word in word_count:\n",
|
|
" word_count[word] += 1\n",
|
|
" else:\n",
|
|
" word_count[word] = 1\n",
|
|
" return word_count\n",
|
|
"\n",
|
|
"def extract_terms(text):\n",
|
|
" return count_words(get_nouns(text))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 113,
|
|
"id": "374550d8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'programmer': 1,\n",
|
|
" 'section': 1,\n",
|
|
" 'swing': 3,\n",
|
|
" 'application': 3,\n",
|
|
" 'command': 1,\n",
|
|
" 'line': 1,\n",
|
|
" 'information': 1,\n",
|
|
" 'compilation': 1,\n",
|
|
" 'instruction': 1,\n",
|
|
" 'program': 4,\n",
|
|
" 'applet': 1,\n",
|
|
" 'step': 1,\n",
|
|
" 'release': 1,\n",
|
|
" 'platform': 1,\n",
|
|
" 'component': 1}"
|
|
]
|
|
},
|
|
"execution_count": 113,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"extract_terms(text)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "loaded-smell",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Ćwiczenie 6: Rozszerz powyższy program o ekstrację czasowników i przymiotników."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 114,
|
|
"id": "monetary-mambo",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def get_verbs(text):\n",
|
|
" doc = nlp(text)\n",
|
|
" return [token.lemma_ for token in doc if token.pos_ == 'VERB']\n",
|
|
"\n",
|
|
"def get_adjectives(text):\n",
|
|
" doc = nlp(text)\n",
|
|
" return [token.lemma_ for token in doc if token.pos_ == 'ADJ']\n",
|
|
"\n",
|
|
"def extract_terms(text):\n",
|
|
" return {\n",
|
|
" \"nouns\": get_nouns(text),\n",
|
|
" \"verbs\": get_verbs(text),\n",
|
|
" \"adjectives\": get_adjectives(text)\n",
|
|
" }"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 115,
|
|
"id": "95494ac9",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'nouns': ['programmer',\n",
|
|
" 'section',\n",
|
|
" 'swing',\n",
|
|
" 'application',\n",
|
|
" 'command',\n",
|
|
" 'line',\n",
|
|
" 'information',\n",
|
|
" 'swing',\n",
|
|
" 'application',\n",
|
|
" 'compilation',\n",
|
|
" 'instruction',\n",
|
|
" 'program',\n",
|
|
" 'applet',\n",
|
|
" 'application',\n",
|
|
" 'step',\n",
|
|
" 'release',\n",
|
|
" 'platform',\n",
|
|
" 'program',\n",
|
|
" 'swing',\n",
|
|
" 'component',\n",
|
|
" 'program',\n",
|
|
" 'program'],\n",
|
|
" 'verbs': ['explain',\n",
|
|
" 'compile',\n",
|
|
" 'run',\n",
|
|
" 'compile',\n",
|
|
" 'run',\n",
|
|
" 'use',\n",
|
|
" 'see',\n",
|
|
" 'run',\n",
|
|
" 'work',\n",
|
|
" 'need',\n",
|
|
" 'follow',\n",
|
|
" 'install',\n",
|
|
" 'do',\n",
|
|
" 'create',\n",
|
|
" 'use',\n",
|
|
" 'compile',\n",
|
|
" 'run'],\n",
|
|
" 'adjectives': ['late']}"
|
|
]
|
|
},
|
|
"execution_count": 115,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"extract_terms(text)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"author": "Rafał Jaworski",
|
|
"email": "rjawor@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": "3.7.9"
|
|
},
|
|
"subtitle": "3. Terminologia",
|
|
"title": "Komputerowe wspomaganie tłumaczenia",
|
|
"year": "2021"
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|