From 71ca3b66ed4328dcefeefcfd415f5d7f1f41b52a Mon Sep 17 00:00:00 2001 From: Bartekfiolek Date: Sat, 13 Apr 2024 08:20:53 +0200 Subject: [PATCH] upload --- lab/.gitignore | 1 + lab/data/corpus_corrected.zip | Bin 0 -> 113948 bytes lab/data/hunspell_pl.zip | Bin 0 -> 946311 bytes lab/img/tagtransfer.png | Bin 0 -> 24772 bytes lab/lab_01.ipynb | 478 ++++++++++++++++++++++++++++++++++ lab/lab_02.ipynb | 384 +++++++++++++++++++++++++++ lab/lab_03.ipynb | 433 ++++++++++++++++++++++++++++++ lab/lab_04-05.ipynb | 289 ++++++++++++++++++++ lab/lab_06-07.ipynb | 216 +++++++++++++++ lab/lab_08.ipynb | 217 +++++++++++++++ lab/lab_09-10.ipynb | 264 +++++++++++++++++++ lab/lab_11.ipynb | 214 +++++++++++++++ lab/lab_12.ipynb | 181 +++++++++++++ lab/lab_13-14.ipynb | 227 ++++++++++++++++ lab/lab_15.ipynb | 181 +++++++++++++ 15 files changed, 3085 insertions(+) create mode 100644 lab/.gitignore create mode 100644 lab/data/corpus_corrected.zip create mode 100644 lab/data/hunspell_pl.zip create mode 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b/lab/lab_01.ipynb @@ -0,0 +1,478 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "packed-oracle", + "metadata": {}, + "source": [ + "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", + "

\n", + "\n", + "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "aggregate-listing", + "metadata": {}, + "source": [ + "Podstawowy scenariusz - tłumacz otrzymuje **dokument** będący ciągiem **segmentów** w **języku źródłowym** . Zadaniem tłumacza jest przetłumaczenie wszystkich segmentów na **język docelowy**. Oznacza to, że został wykonany już szereg operacji technicznych, a w tym:\n", + "- przesłanie pliku do tłumaczenia\n", + "- ekstrakcja tekstu z dokumentu\n", + "- podział tekstu na segmenty.\n" + ] + }, + { + "cell_type": "markdown", + "id": "allied-gasoline", + "metadata": {}, + "source": [ + "Tłumacz tłumaczy jeden segment na raz. Widzi kilka poprzednich oraz kilka następnych segmentów, jednak jego uwaga powinna skupiać się na aktywnym, aktualnie tłumaczonym segmencie. Nie oznacza to jednak, że tłumacz jest pozbawiony pomocy. Ma możliwość korzystania z różnorakich technik **komputerowego wspomagania tłumaczenia**." + ] + }, + { + "cell_type": "markdown", + "id": "golden-turkish", + "metadata": {}, + "source": [ + "# Pamięć tłumaczeń" + ] + }, + { + "cell_type": "markdown", + "id": "retired-burke", + "metadata": {}, + "source": [ + "Najbardziej podstawową techniką wspomagania tłumaczenia jest **pamięć tłumaczeń**. Pamięć tłumaczeń stanowi zbiór wcześniej przetłumaczonych zdań:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "narrow-romantic", + "metadata": {}, + "outputs": [], + "source": [ + "translation_memory = [('Wciśnij przycisk Enter', 'Press the ENTER button'), \n", + " ('Sprawdź ustawienia sieciowe', 'Check the network settings')]" + ] + }, + { + "cell_type": "markdown", + "id": "tender-drunk", + "metadata": {}, + "source": [ + "Kiedy tłumacz pracuje nad kolejnym zdaniem, program automatycznie przeszukuje pamięć tłumaczeń. Jeśli okaże się, że zdanie zostało już przetłumaczone, automatycznie pobierane jest jego tłumaczenie z pamięci:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "indonesian-electron", + "metadata": {}, + "outputs": [], + "source": [ + "def tm_lookup(sentence):\n", + " return [entry[1] for entry in translation_memory if entry[0] == sentence]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "compact-trinidad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Press the ENTER button']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tm_lookup('Wciśnij przycisk Enter')" + ] + }, + { + "cell_type": "markdown", + "id": "legislative-portsmouth", + "metadata": {}, + "source": [ + "W takim przypadku tłumaczenie z pamięci jest najczęściej automatycznie wstawiane jako tłumaczenie segmentu źródłowego i tłumacz nie musi się już nim martwić (co więcej, nie dostaje za takie tłumaczenie zapłaty...)" + ] + }, + { + "cell_type": "markdown", + "id": "prepared-entry", + "metadata": {}, + "source": [ + "Tłumacz pracuje dalej i każde wykonane przez niego tłumaczenie trafia do pamięci tłumaczeń (oczywiście dzieje się to automatycznie):" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "exposed-daniel", + "metadata": {}, + "outputs": [], + "source": [ + "translation_memory.append(('Drukarka jest wyłączona', 'The printer is switched off'))\n", + "translation_memory.append(('Wymagane ponowne uruchomienie komputera', 'System restart required'))\n", + "translation_memory.append(('Wciśnij przycisk Enter', 'Press the ENTER key'))" + ] + }, + { + "cell_type": "markdown", + "id": "alert-cancellation", + "metadata": {}, + "source": [ + "Z całą pewnością tłumacz stara się przełożyć jakąś komputerową instrukcję. Po wielu godzinach pracy trafia na znane sobie zdanie \"Wciśnij przycisk Enter\". Następuje przeszukanie pamięci tłumaczeń:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "serial-velvet", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Press the ENTER button', 'Press the ENTER key']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tm_lookup('Wciśnij przycisk Enter')" + ] + }, + { + "cell_type": "markdown", + "id": "meaning-breathing", + "metadata": {}, + "source": [ + "Jak widać, mamy w pamięci dwa tłumaczenia tego zdania. To tłumacz musi zadecydować, które tłumaczenie wybrać. O metodach wspomagania decyzji tłumacza w takich przypadkach będziemy jeszcze mówić." + ] + }, + { + "cell_type": "markdown", + "id": "miniature-interim", + "metadata": {}, + "source": [ + "Przeanalizujmy teraz następujący przypadek: do tłumaczenia trafia zdanie: \"Wciśnij przycisk ENTER\":" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "every-gibson", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tm_lookup('Wciśnij przycisk ENTER')" + ] + }, + { + "cell_type": "markdown", + "id": "restricted-oregon", + "metadata": {}, + "source": [ + "Tutaj tłumacz nie ma szczęścia - restrykcyjny mechanizm wyszukiwania nie podaje mu podpowiedzi. Możemy jednak sprawić, żeby przeszukiwanie pamięci tłumaczeń nie brało pod uwagę wielkości liter." + ] + }, + { + "cell_type": "markdown", + "id": "inclusive-bargain", + "metadata": {}, + "source": [ + "### Ćwiczenie 1: zmodyfikuj funkcję tm_lookup w taki sposób, aby nie brała pod uwagę wielkości liter. Przetestuj zmodyfikowaną funkcję." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "protected-rings", + "metadata": {}, + "outputs": [], + "source": [ + "def tm_lookup(sentence):\n", + " return ''" + ] + }, + { + "cell_type": "markdown", + "id": "dress-plymouth", + "metadata": {}, + "source": [ + "Nasz tłumacz powinien być teraz zadowolony. Jednak w dalszej części dokumentu pojawiło się inne podobne zdanie, dla którego nie było tłumaczenia:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "severe-alloy", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "''" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tm_lookup('Wciśnij przycisk [ENTER]')" + ] + }, + { + "cell_type": "markdown", + "id": "choice-committee", + "metadata": {}, + "source": [ + "### Ćwiczenie 2: zmodyfikuj funkcję tm_lookup w taki sposób, aby nie brała pod uwagę znaków interpunkcyjnych. Rada - zdefiniuj funkcję sentence_similar." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "structural-diesel", + "metadata": {}, + "outputs": [], + "source": [ + "def tm_lookup(sentence):\n", + " return ''" + ] + }, + { + "cell_type": "markdown", + "id": "physical-distribution", + "metadata": {}, + "source": [ + "Praca tłumacza oczywiście trwa nadal. Spróbujmy teraz zaradzić następującej sytuacji - tłumacz otrzymuje do tłumaczenia następujące zdanie:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "brief-senegal", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "''" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tm_lookup('Wymagane ponowne uruchomienie maszyny')" + ] + }, + { + "cell_type": "markdown", + "id": "guided-tutorial", + "metadata": {}, + "source": [ + "Znów nie otrzymuje od nas podpowiedzi i znów nic w tym dziwnego - w pamięci tłumaczeń nie znajduje się takie zdanie. Jest jednak zdanie podobne, różniące się jednym słowem." + ] + }, + { + "cell_type": "markdown", + "id": "laughing-preview", + "metadata": {}, + "source": [ + "### Ćwiczenie 3: zmodyfikuj funkcję tm_lookup w taki sposób, aby zwracała podpowiedzi także wtedy, gdy zdania różnią się tylko jednym słowem." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "mathematical-customs", + "metadata": {}, + "outputs": [], + "source": [ + "def tm_lookup(sentence):\n", + " return ''" + ] + }, + { + "cell_type": "markdown", + "id": "meaningful-virus", + "metadata": {}, + "source": [ + "# Słownik kontekstowy / glosariusz" + ] + }, + { + "cell_type": "markdown", + "id": "early-variety", + "metadata": {}, + "source": [ + "Inną podstawową pomocą dla naszego tłumacza jest słownik, zwany także glosariuszem. Stanowi on zbiór terminów w języku źródłowym wraz z ich tłumaczeniami na język docelowy:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "humanitarian-wrong", + "metadata": {}, + "outputs": [], + "source": [ + "glossary = [('komputer', 'computer'), ('przycisk', 'button'), ('drukarka', 'printer')]" + ] + }, + { + "cell_type": "markdown", + "id": "minimal-moral", + "metadata": {}, + "source": [ + "Podczas tłumaczenia, glosariusz jest przeszukiwany w celu znalezienia aktualnie tłumaczonych słów." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "located-perception", + "metadata": {}, + "outputs": [], + "source": [ + "def glossary_lookup(sentence):\n", + " sentence_words = sentence.split()\n", + " return [entry for entry in glossary if entry[0] in sentence_words]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "advised-casting", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('przycisk', 'button'), ('drukarka', 'printer')]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "glossary_lookup('Każda drukarka posiada przycisk wznowienia drukowania')" + ] + }, + { + "cell_type": "markdown", + "id": "blessed-gentleman", + "metadata": {}, + "source": [ + "### Ćwiczenie 4: (tym razem teoretyczne) Jaka jest złożoność obliczeniowa czasowa przedstawionego powyżej algorytmu względem liczby haseł w słowniku (n) oraz liczby słów w zdaniu do tłumaczenia (m)?" + ] + }, + { + "cell_type": "markdown", + "id": "defensive-fifteen", + "metadata": {}, + "source": [ + "Odpowiedź:" + ] + }, + { + "cell_type": "markdown", + "id": "objective-matthew", + "metadata": {}, + "source": [ + "### Ćwiczenie 5: zmodyfikuj funkcję glossary_lookup w taki sposób, aby nie brała pod uwagę wielkości liter." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "original-tunisia", + "metadata": {}, + "outputs": [], + "source": [ + "def glossary_lookup(sentence):\n", + " return ''" + ] + }, + { + "cell_type": "markdown", + "id": "injured-hormone", + "metadata": {}, + "source": [ + "### Ćwiczenie 6: zmodyfikuj funkcję glossary_lookup w taki sposób, aby jej złożoność obliczeniowa była mniejsza." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "adolescent-semiconductor", + "metadata": {}, + "outputs": [], + "source": [ + "def glossary_lookup(sentence):\n", + " return ''" + ] + } + ], + "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": "3.8.10" + }, + "subtitle": "1. Podstawowe techniki wspomagania tłumaczenia", + "title": "Komputerowe wspomaganie tłumaczenia", + "year": "2021" + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lab/lab_02.ipynb b/lab/lab_02.ipynb new file mode 100644 index 0000000..10c2003 --- /dev/null +++ b/lab/lab_02.ipynb @@ -0,0 +1,384 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "continued-dinner", + "metadata": {}, + "source": [ + "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", + "
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Komputerowe wspomaganie tłumaczenia

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2. Zaawansowane użycie pamięci tłumaczeń [laboratoria]

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Rafał Jaworski (2021)

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\n", + "\n", + "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "aggregate-listing", + "metadata": {}, + "source": [ + "Wiemy już, do czego służy pamięć tłumaczeń. Spróbujmy przeprowadzić mały research, którego celem będzie odkrycie, w jaki sposób do pamięci tłumaczeń podchodzą najwięksi producenci oprogramowania typu CAT.\n" + ] + }, + { + "cell_type": "markdown", + "id": "golden-turkish", + "metadata": {}, + "source": [ + "### Ćwiczenie 1: Wykonaj analizę funkcjonalności pamięci tłumaczeń w programach SDL Trados Studio 2021 oraz Kilgray memoQ. Dla obu programów wypisz funkcje, które są związane z TM oraz zaznacz, które funkcje są wspólne dla obu programów oraz których funkcji Tradosa brakuje w memoQ oraz odwrotnie." + ] + }, + { + "cell_type": "markdown", + "id": "retired-burke", + "metadata": {}, + "source": [ + "Odpowiedź:" + ] + }, + { + "cell_type": "markdown", + "id": "existing-approval", + "metadata": {}, + "source": [ + "Jedną z funkcji dostępnych we wszystkich większych programach do wspomagania tłumaczenia jest znajdowanie bardzo pewnych dopasowań w pamięci tłumaczeń. Są one zwane **ICE** (In-Context Exact match) lub 101% match. Są to takie dopasowania z pamięci tłumaczeń, dla których nie tylko zdanie źródłowe z TM jest identyczne z tłumaczonym, ale także poprzednie zdanie źródłowe z TM zgadza się z poprzednim zdaniem tłumaczonym oraz następne z TM z następnym tłumaczonym." + ] + }, + { + "cell_type": "markdown", + "id": "decimal-electricity", + "metadata": {}, + "source": [ + " Rozważmy przykładową pamięć tłumaczeń z poprzednich zajęć (można do niej dorzucić kilka przykładów):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "confident-prison", + "metadata": {}, + "outputs": [], + "source": [ + "translation_memory = [\n", + " ('Wciśnij przycisk Enter', 'Press the ENTER button'), \n", + " ('Sprawdź ustawienia sieciowe', 'Check the network settings'),\n", + " ('Drukarka jest wyłączona', 'The printer is switched off'),\n", + " ('Wymagane ponowne uruchomienie komputera', 'System restart required')\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "id": "informal-breakdown", + "metadata": {}, + "source": [ + "### Ćwiczenie 2: Zaimplementuj funkcję ice_lookup, przyjmującą trzy parametry: aktualnie tłumaczone zdanie, poprzednio tłumaczone zdanie, następne zdanie do tłumaczenia. Funkcja powinna zwracać dopasowania typu ICE. Nie pozwól, aby doszło do błędów podczas sprawdzania pierwszego i ostatniego przykładu w pamięci (ze względu na brak odpowiednio poprzedzającego oraz następującego przykładu)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "continental-submission", + "metadata": {}, + "outputs": [], + "source": [ + "def ice_lookup(sentence, prev_sentence, next_sentence):\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "figured-server", + "metadata": {}, + "source": [ + "Inną powszechnie stosowaną techniką przeszukiwania pamięci tłumaczeń jest tzw. **fuzzy matching**. Technika ta polega na wyszukiwaniu zdań z pamięci, które są tylko podobne do zdania tłumaczonego. Na poprzednich zajęciach wykonywaliśmy funkcję tm_lookup, która pozwalała na różnicę jednego słowa." + ] + }, + { + "cell_type": "markdown", + "id": "beautiful-fancy", + "metadata": {}, + "source": [ + "Zazwyczaj jednak funkcje fuzzy match posiadają znacznie szersze możliwości. Ich działanie opiera się na zdefiniowaniu funkcji $d$ dystansu pomiędzy zdaniami $x$ i $y$. Matematycznie, funkcja dystansu posiada następujące właściwości:\n", + "1. $\\forall_{x,y} d(x,y)\\geqslant 0$\n", + "2. $\\forall_{x,y} d(x,y)=0 \\Leftrightarrow x=y$\n", + "3. $\\forall_{x,y} d(x,y)=d(y,x)$\n", + "4. $\\forall_{x,y,z} d(x,y) + d(y,z)\\geqslant d(x,z)$" + ] + }, + { + "cell_type": "markdown", + "id": "square-usage", + "metadata": {}, + "source": [ + "Rozważmy następującą funkcję:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fourth-pillow", + "metadata": {}, + "outputs": [], + "source": [ + "def sentence_distance(x,y):\n", + " return abs(len(y) - len(x))" + ] + }, + { + "cell_type": "markdown", + "id": "mediterranean-cosmetic", + "metadata": {}, + "source": [ + "### Ćwiczenie 3: Czy to jest poprawna funkcja dystansu? Które warunki spełnia?" + ] + }, + { + "cell_type": "markdown", + "id": "graduate-theorem", + "metadata": {}, + "source": [ + "Odpowiedź:" + ] + }, + { + "cell_type": "markdown", + "id": "native-amber", + "metadata": {}, + "source": [ + "A teraz spójrzmy na taką funkcję:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "continued-christopher", + "metadata": {}, + "outputs": [], + "source": [ + "def sentence_distance(x,y):\n", + " if (x == y):\n", + " return 0\n", + " else:\n", + " return 3" + ] + }, + { + "cell_type": "markdown", + "id": "every-surveillance", + "metadata": {}, + "source": [ + "### Ćwiczenie 4: Czy to jest poprawna funkcja dystansu? Które warunki spełnia?" + ] + }, + { + "cell_type": "markdown", + "id": "metallic-leave", + "metadata": {}, + "source": [ + "Odpowiedź:" + ] + }, + { + "cell_type": "markdown", + "id": "executed-baptist", + "metadata": {}, + "source": [ + "Wprowadźmy jednak inną funkcję dystansu - dystans Levenshteina. Dystans Levenshteina pomiędzy dwoma łańcuchami znaków definiuje się jako minimalną liczbę operacji edycyjnych, które są potrzebne do przekształcenia jednego łańcucha znaków w drugi. Wyróżniamy trzy operacje edycyjne:\n", + "* dodanie znaku\n", + "* usunięcie znaku\n", + "* zamiana znaku na inny" + ] + }, + { + "cell_type": "markdown", + "id": "square-brown", + "metadata": {}, + "source": [ + "### Ćwiczenie 5: Czy dystans Levenshteina jest poprawną funkcją dystansu? Uzasadnij krótko swoją odpowiedź sprawdzając każdy z warunków." + ] + }, + { + "cell_type": "markdown", + "id": "bibliographic-stopping", + "metadata": {}, + "source": [ + "Odpowiedź:" + ] + }, + { + "cell_type": "markdown", + "id": "attended-channels", + "metadata": {}, + "source": [ + "W Pythonie dostępna jest biblioteka zawierająca implementację dystansu Levenshteina. Zainstaluj ją w swoim systemie przy użyciu polecenia:\n", + "\n", + "`pip3 install python-Levenshtein`\n", + "\n", + "I wypróbuj:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "secondary-wrist", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from Levenshtein import distance as levenshtein_distance\n", + "\n", + "levenshtein_distance(\"kotek\", \"kotki\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "concrete-satellite", + "metadata": {}, + "source": [ + "Funkcja ta daje nam możliwość zdefiniowania podobieństwa pomiędzy zdaniami:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "associate-tuner", + "metadata": {}, + "outputs": [], + "source": [ + "def levenshtein_similarity(x,y):\n", + " return 1 - levenshtein_distance(x,y) / max(len(x), len(y))" + ] + }, + { + "cell_type": "markdown", + "id": "built-michael", + "metadata": {}, + "source": [ + "Przetestujmy ją!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "focal-pathology", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9166666666666666" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "levenshtein_similarity('Program jest uruchomiony', 'Program jest uruchamiany')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "roman-ceiling", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9428571428571428" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "levenshtein_similarity('Spróbuj wyłączyć i włączyć komputer', 'Spróbuj włączyć i wyłączyć komputer')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "invisible-cambodia", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.631578947368421" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "levenshtein_similarity('Spróbuj wyłączyć i włączyć komputer', 'Nie próbuj wyłączać i włączać drukarki')" + ] + }, + { + "cell_type": "markdown", + "id": "administrative-phoenix", + "metadata": {}, + "source": [ + "### Ćwiczenie 6: Napisz funkcję fuzzy_lookup, która wyszuka w pamięci tłumaczeń wszystkie zdania, których podobieństwo Levenshteina do zdania wyszukiwanego jest większe lub równe od ustalonego progu." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "genetic-cradle", + "metadata": {}, + "outputs": [], + "source": [ + "def fuzzy_lookup(sentence, threshold):\n", + " return []" + ] + } + ], + "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": "3.8.10" + }, + "subtitle": "2. Zaawansowane użycie pamięci tłumaczeń", + "title": "Komputerowe wspomaganie tłumaczenia", + "year": "2021" + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lab/lab_03.ipynb b/lab/lab_03.ipynb new file mode 100644 index 0000000..5707f0d --- /dev/null +++ b/lab/lab_03.ipynb @@ -0,0 +1,433 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "coastal-lincoln", + "metadata": {}, + "source": [ + "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", + "
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Komputerowe wspomaganie tłumaczenia

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3. Terminologia [laboratoria]

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Rafał Jaworski (2021)

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\n", + "\n", + "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "aggregate-listing", + "metadata": {}, + "source": [ + "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." + ] + }, + { + "cell_type": "markdown", + "id": "israeli-excuse", + "metadata": {}, + "source": [ + "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", + "- Teksty o tematyce ogólnej są tłumaczone dość rzadko (nikt nie tłumaczy pocztówek z pozdrowieniami z wakacji...)\n", + "- Te same słowa mogą mieć zarówno znaczenie ogólne, jak i bardzo specjalistyczne (np. \"dziedziczenie\" w kontekście prawnym lub informatycznym)\n", + "- Klient używa nazw lub słów wymyślonych przez siebie, np. na potrzeby marketingowe." + ] + }, + { + "cell_type": "markdown", + "id": "reflected-enforcement", + "metadata": {}, + "source": [ + "Nietrywialnymi zadaniami stają się: odnalezienie terminu specjalistycznego w tekście źródłowym oraz podanie prawidłowego tłumaczenia tego terminu na język docelowy" + ] + }, + { + "cell_type": "markdown", + "id": "statutory-florist", + "metadata": {}, + "source": [ + "Brzmi prosto? Spróbujmy wykonać ręcznie tę drugą operację." + ] + }, + { + "cell_type": "markdown", + "id": "danish-anchor", + "metadata": {}, + "source": [ + "### Ćwiczenie 1: Podaj tłumaczenie terminu \"prowadnice szaf metalowych\" na język angielski. Opisz, z jakich narzędzi skorzystałaś/eś." + ] + }, + { + "cell_type": "markdown", + "id": "diverse-sunglasses", + "metadata": {}, + "source": [ + "Odpowiedź:" + ] + }, + { + "cell_type": "markdown", + "id": "limited-waterproof", + "metadata": {}, + "source": [ + "W dalszych ćwiczeniach skupimy się jednak na odszukaniu terminu specjalistycznego w tekście. W tym celu będą potrzebne dwie operacje:\n", + "1. Przygotowanie słownika specjalistycznego.\n", + "2. Detekcja terminologii przy użyciu przygotowanego słownika specjalistycznego." + ] + }, + { + "cell_type": "markdown", + "id": "literary-blues", + "metadata": {}, + "source": [ + "Zajmijmy się najpierw krokiem nr 2 (gdyż jest prostszy). Rozważmy następujący tekst:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "loving-prince", + "metadata": {}, + "outputs": [], + "source": [ + "text = \" For all Java programmers:\"\n", + "text += \" This section explains how to compile and run a Swing application from the command line.\"\n", + "text += \" For information on compiling and running a Swing application using NetBeans IDE,\"\n", + "text += \" see Running Tutorial Examples in NetBeans IDE. The compilation instructions work for all Swing programs\"\n", + "text += \" — applets, as well as applications. Here are the steps you need to follow:\"\n", + "text += \" Install the latest release of the Java SE platform, if you haven't already done so.\"\n", + "text += \" Create a program that uses Swing components. Compile the program. Run the program.\"" + ] + }, + { + "cell_type": "markdown", + "id": "extreme-cycling", + "metadata": {}, + "source": [ + "Załóżmy, że posiadamy następujący słownik:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bound-auction", + "metadata": {}, + "outputs": [], + "source": [ + "dictionary = ['program', 'application', 'applet' 'compile']" + ] + }, + { + "cell_type": "markdown", + "id": "other-trinidad", + "metadata": {}, + "source": [ + "### Ćwiczenie 2: Napisz program, który wypisze pozycje wszystkich wystąpień poszczególnych terminów specjalistycznych. Dla każdego terminu należy wypisać listę par (pozycja_startowa, pozycja końcowa)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cognitive-cedar", + "metadata": {}, + "outputs": [], + "source": [ + "def terminology_lookup():\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "interior-things", + "metadata": {}, + "source": [ + "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." + ] + }, + { + "cell_type": "markdown", + "id": "aggressive-plane", + "metadata": {}, + "source": [ + "Żeby poradzić sobie z tymi problemami, musimy wykorzystać techniki przetwarzania języka naturalnego. Wypróbujmy pakiet spaCy:\n", + "\n", + "`pip3 install spacy`\n", + "\n", + "oraz\n", + "\n", + "`python3 -m spacy download en_core_web_sm`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "tribal-attention", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "for\n", + "all\n", + "Java\n", + "programmer\n", + ":\n", + "this\n", + "section\n", + "explain\n", + "how\n", + "to\n", + "compile\n", + "and\n", + "run\n", + "a\n", + "swing\n", + "application\n", + "from\n", + "the\n", + "command\n", + "line\n", + ".\n", + "for\n", + "information\n", + "on\n", + "compile\n", + "and\n", + "run\n", + "a\n", + "swing\n", + "application\n", + "use\n", + "NetBeans\n", + "IDE\n", + ",\n", + "see\n", + "Running\n", + "Tutorial\n", + "Examples\n", + "in\n", + "NetBeans\n", + "IDE\n", + ".\n", + "the\n", + "compilation\n", + "instruction\n", + "work\n", + "for\n", + "all\n", + "swing\n", + "program\n", + "—\n", + "applet\n", + ",\n", + "as\n", + "well\n", + "as\n", + "application\n", + ".\n", + "here\n", + "be\n", + "the\n", + "step\n", + "-PRON-\n", + "need\n", + "to\n", + "follow\n", + ":\n", + "install\n", + "the\n", + "late\n", + "release\n", + "of\n", + "the\n", + "Java\n", + "SE\n", + "platform\n", + ",\n", + "if\n", + "-PRON-\n", + "have\n", + "not\n", + "already\n", + "do\n", + "so\n", + ".\n", + "create\n", + "a\n", + "program\n", + "that\n", + "use\n", + "Swing\n", + "component\n", + ".\n", + "compile\n", + "the\n", + "program\n", + ".\n", + "run\n", + "the\n", + "program\n", + ".\n" + ] + } + ], + "source": [ + "import spacy\n", + "nlp = spacy.load(\"en_core_web_sm\")\n", + "\n", + "doc = nlp(text)\n", + "\n", + "for token in doc:\n", + " print(token.lemma_)" + ] + }, + { + "cell_type": "markdown", + "id": "regional-craft", + "metadata": {}, + "source": [ + "Sukces! Nastąpił podział tekstu na słowa (tokenizacja) oraz sprowadzenie do formy podstawowej każdego słowa (lematyzacja)." + ] + }, + { + "cell_type": "markdown", + "id": "toxic-subsection", + "metadata": {}, + "source": [ + "### 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." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "surgical-demonstration", + "metadata": {}, + "outputs": [], + "source": [ + "def terminology_lookup():\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "straight-letter", + "metadata": {}, + "source": [ + "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." + ] + }, + { + "cell_type": "markdown", + "id": "nearby-frontier", + "metadata": {}, + "source": [ + "Przyjmijmy następujące podejście - terminami specjalistycznymi będą najcześćiej występujące rzeczowniki w tekście. Wykonajmy krok pierwszy:" + ] + }, + { + "cell_type": "markdown", + "id": "harmful-lightning", + "metadata": {}, + "source": [ + "### Ćwiczenie 4: Wypisz wszystkie rzeczowniki z tekstu. Wykorzystaj możliwości spaCy." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "superb-butterfly", + "metadata": {}, + "outputs": [], + "source": [ + "def get_nouns(text):\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "musical-creator", + "metadata": {}, + "source": [ + "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:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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": 8, + "id": "eight-redhead", + "metadata": {}, + "outputs": [], + "source": [ + "def extract_terms(text):\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "loaded-smell", + "metadata": {}, + "source": [ + "### Ćwiczenie 6: Rozszerz powyższy program o ekstrację czasowników i przymiotników." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "monetary-mambo", + "metadata": {}, + "outputs": [], + "source": [ + "def extract_terms(text):\n", + " return []" + ] + } + ], + "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": "3.8.10" + }, + "subtitle": "3. Terminologia", + "title": "Komputerowe wspomaganie tłumaczenia", + "year": "2021" + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lab/lab_04-05.ipynb b/lab/lab_04-05.ipynb new file mode 100644 index 0000000..ef47f3c --- /dev/null +++ b/lab/lab_04-05.ipynb @@ -0,0 +1,289 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "going-morocco", + "metadata": {}, + "source": [ + "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", + "
\n", + "

Komputerowe wspomaganie tłumaczenia

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4,5. Klasyfikacja tematyczna (terminologii ciąg dalszy) [laboratoria]

\n", + "

Rafał Jaworski (2021)

\n", + "
\n", + "\n", + "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "expanded-entrance", + "metadata": {}, + "source": [ + "# Komputerowe wspomaganie tłumaczenia" + ] + }, + { + "cell_type": "markdown", + "id": "atlantic-greenhouse", + "metadata": {}, + "source": [ + "# Zajęcia 4 i 5 - klasyfikacja tematyczna (terminologii ciąg dalszy)" + ] + }, + { + "cell_type": "markdown", + "id": "colored-nothing", + "metadata": {}, + "source": [ + "Na poprzednich zajęciach opracowaliśmy nasz własny ekstraktor terminologii. Mówiliśmy również, jak ważna jest ekstrakcja terminów specjalistycznych. Dziś zajmiemy się zagadnieniem, w jaki sposób wyciągnąć z tekstu terminy, które naprawdę są specjalistyczne." + ] + }, + { + "cell_type": "markdown", + "id": "economic-pontiac", + "metadata": {}, + "source": [ + "Dlaczego nasze dotychczasowe rozwiązanie mogło nie spełniać tego warunku? Wykonajmy następujące ćwiczenie:" + ] + }, + { + "cell_type": "markdown", + "id": "sealed-november", + "metadata": {}, + "source": [ + "### Ćwiczenie 1: Zgromadź korpus w języku angielskim składający się z co najmniej 100 dokumentów, z których każdy zawiera co najmniej 100 zdań. Wykorzystaj stronę https://opus.nlpl.eu/. Dobrze, aby dokumenty pochodziły z różnych dziedzin (np. prawo Unii Europejskiej, manuale programistyczne, medycyna). Ściągnięty korpus zapisz na swoim dysku lokalnym, nie załączaj go do niniejszego notatnika." + ] + }, + { + "cell_type": "markdown", + "id": "closed-steel", + "metadata": {}, + "source": [ + "Taki korpus pozwoli nam zaobserwować, co się stanie, jeśli do ekstrakcji terminologii będziemy stosowali wyłącznie kryterium częstościowe. Aby wykonać odpowiedni eksperyment musimy uruchomić ekstraktor z poprzednich zajęć." + ] + }, + { + "cell_type": "markdown", + "id": "environmental-thread", + "metadata": {}, + "source": [ + "### Ćwiczenie 2: Uruchom ekstraktor terminologii (wykrywacz rzeczowników) z poprzednich zajęć na każdym dokumencie z osobna. Jako wynik ekstraktora w każdym przypadku wypisz 5 najczęściej występujących rzeczowników. Wyniki działania komendy umieść w notatniku." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "honest-assessment", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "desirable-appeal", + "metadata": {}, + "source": [ + "Czy wyniki uzyskane w ten sposób to zawsze terminy specjalistyczne? Niestety może zdarzyć się, że w wynikach pojawią się rzeczowniki, które są po prostu częste w języku, a niekoniecznie charakterystyczne dla przetwarzanych przez nas tekstów. Aby wyniki ekstrakcji były lepsze, konieczne jest zastosowanie bardziej wyrafinowanych metod." + ] + }, + { + "cell_type": "markdown", + "id": "accomplished-interview", + "metadata": {}, + "source": [ + "Jedną z tych metod jest znana z dyscypliny Information Retrieval technika zwana TF-IDF. Jej nazwa wywodzi się od **T**erm **F**requency **I**nverted **D**ocument **F**requency. Według tej metody, dla każdego odnalezionego przez nas termu powinniśmy obliczyć czynnik TF-IDF, a następnie wyniki posortować malejąco po wartości tego czynnika." + ] + }, + { + "cell_type": "markdown", + "id": "green-september", + "metadata": {}, + "source": [ + "Jak obliczyć czynnik TF-IDF? Czym jest TF, a czym jest IDF?" + ] + }, + { + "cell_type": "markdown", + "id": "several-gardening", + "metadata": {}, + "source": [ + "Zacznijmy od TF, bo ten czynnik już znamy. Jest to nic innego jak częstość wystąpienia terminu w tekście, który przetwarzamy. Idea TF-IDF skupia się na drugim czynniku - IDF. Słowo *inverted* oznacza, że czynnik ten będzie odwrócony, czyli trafi do mianownika. W związku z tym TF-IDF to w istocie:\n", + "$\\frac{TF}{DF}$" + ] + }, + { + "cell_type": "markdown", + "id": "collect-brooklyn", + "metadata": {}, + "source": [ + "Czym zatem jest document frequency? Jest to liczba dokumentów, w których wystąpił dany termin. Dokumenty w tym przypadku są rozumiane jako jednostki, na które podzielony jest korpus, nad którym pracujemy (dokładnie taki, jak korpus z ćwiczenia pierwszego)." + ] + }, + { + "cell_type": "markdown", + "id": "urban-spray", + "metadata": {}, + "source": [ + "Zastanówmy się nad sensem tego czynnika. Pamiętajmy, że naszym zadaniem jest ekstracja terminów z tylko jednego dokumentu na raz. Mamy jednak do dyspozycji wiele innych dokumentów, zawierających wiele innych słów i termów. Wartość TF-IDF jest tym większa, im częściej termin występuje w dokumencie, na którym dokonujemy ekstrakcji. Czynnik ten jednak zmniejsza się, jeśli słowo występuje w wielu różnych dokumentach. Zatem, popularne słowa będą miały wysoki czynnik DF i niski TF-IDF. Natomiast najwyższą wartość TF-IDF będą miały terminy, które są częste w przetwarzanym przez nas dokumencie, ale nie występują nigdzie indziej." + ] + }, + { + "cell_type": "markdown", + "id": "worse-throat", + "metadata": {}, + "source": [ + "### Ćwiczenie 3: Zaimplementuj czynnik TF-IDF i dokonaj ekstrakcji terminologii za jego pomocą, używając korpusu z ćwiczenia nr 1. Czy wyniki różnią się od tych uzyskanych tylko za pomocą TF?" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "published-speaking", + "metadata": {}, + "outputs": [], + "source": [ + "def tfidf_extract():\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "handmade-square", + "metadata": {}, + "source": [ + "Teraz potrafimy już w lepszy sposób wyciągać terminy z dokumentów. Spróbujmy jeszcze czegoś widowiskowego - wygenerujmy tzw. chmurę słów z tekstu przy użyciu biblioteki WordCloud dla artykułu z BBC News (https://www.bbc.com/news/world-europe-56530714):" + ] + }, + { + "cell_type": "markdown", + "id": "solar-particular", + "metadata": {}, + "source": [ + "`sudo pip install wordcloud`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "monetary-wages", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from wordcloud import WordCloud\n", + "text = \"\"\"\"This is where it happened,\" says Felipe Luis Codesal, opening the gate to a three-hectare field on his farm in Zamora, north-west Spain.\n", + "\n", + "One night last November, a pack of wolves got through the fence surrounding the field and attacked Mr Codesal's sheep, many of which were pregnant. When he arrived the next morning, he found 11 animals had been killed. Over the following days, he says, another 36 sheep died from injuries sustained in that attack and miscarriages it triggered.\n", + "\n", + "Mr Codesal fears that such attacks will become even more commonplace if a proposed change to laws protecting the Iberian wolf comes into force.\n", + "\n", + "The leftist coalition government plans to prevent the Iberian wolf from being hunted anywhere by categorising it as an endangered species. The reform is yet to be implemented and could see changes.\n", + "\n", + "Iberian wolves from the Iberian Wolf Centre in Robledo de Sanabria on February 21, 2020 in Zamora, Spain\n", + "image captionSpain has Europe's biggest wolf population: These Iberian wolves are kept at Zamora's Iberian wolf centre\n", + "\"It's like in a nightclub when there's a fire,\" says Mr Codesal of the wolf attack. \"There's a stampede and people get trodden on and hurt. This is the same.\"\n", + "\n", + "He was not entitled to any compensation and estimates that the financial losses he suffered from this incident totalled around €12-14,000\n", + "\n", + "\"It's not even about the money,\" he says. \"It's emotional, because the animals are part of my family.\"\n", + "\n", + "A 'historic' change?\n", + "The region of Castilla y León is the habitat for most of Spain's wolves. Figures gathered by the local government showed that they killed 3,774 sheep and cows in the region in 2019.\n", + "\n", + "Felipe Luis Codesal's farm is just north of the Duero river, which marks a natural border between north-west Spain and the rest of the country. Until now, it has been legal to hunt wolves north of the Duero, under a strict quota system, because that is where they are most prevalent.\n", + "\n", + "South of the river they have been protected.\n", + "\n", + "Conservationist groups have welcomed the government plan. When it was unveiled in February, the Ecologistas en Acción organisation hailed it as a \"historic day\".\n", + "\n", + "But Mr Codesal, who is a member of the UPA association of smallholder farmers, warns the reform will ruin livestock owners by allowing the wolf population to spiral out of control and roam uncontrolled. The UPA is unconvinced by measures included in the plan to subsidise the installation of fences and the use of guard dogs in livestock farming areas.\n", + "\n", + "Biggest wolf numbers in Europe\n", + "The Iberian wolf was close to being wiped out in the middle of the 20th Century. But it enjoyed a resurgence on the back of new hunting regulations introduced in the 1970s and the migration of Spaniards away from rural areas also encouraged its spread down from the north-western corner of the country.\n", + "\n", + "In recent years, wolves have moved into areas such as the Guadarrama mountains north of Madrid and near the city of Ávila, to the west of the capital.\n", + "\n", + "There are now some 2,500 Iberian wolves: around 2,000 are in Spain - the largest wolf population in western Europe - and the rest in Portugal.\n", + "\"\"\"\n", + "\n", + "wordcloud = WordCloud(background_color=\"white\", max_words=5000, contour_width=3, contour_color='steelblue')\n", + "wordcloud.generate(text)\n", + "wordcloud.to_image()" + ] + }, + { + "cell_type": "markdown", + "id": "third-eagle", + "metadata": {}, + "source": [ + "### Ćwiczenie 4: Wykonaj chmurę słów dla całego korpusu z ćwiczenia nr 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "electrical-disposition", + "metadata": {}, + "outputs": [], + "source": [ + "def my_word_cloud():\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "objective-possession", + "metadata": {}, + "source": [ + "Zastanówmy się nad jeszcze jednym zagadnieniem - jak pogrupować te terminy ze względu na dziedzinę? Zagadnienie to nosi nazwę klasyfikacji tematycznej. A dzięki pewnemu XIX-wiecznemu niemieckiemu matematykowi możliwe jest przeprowadzenie tego procesu automatycznie. Matematyk ten nosił nazwisko Peter Gustav Lejeune Dirichlet, a metoda klasyfikacji nazywa się LDA (**L**atent **D**irichlet **A**llocation)." + ] + }, + { + "cell_type": "markdown", + "id": "intellectual-gothic", + "metadata": {}, + "source": [ + "### Ćwiczenie 5: Wykonaj tutorial dostępny pod https://towardsdatascience.com/end-to-end-topic-modeling-in-python-latent-dirichlet-allocation-lda-35ce4ed6b3e0. Wklej do notatnika wyniki." + ] + } + ], + "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": "3.8.10" + }, + "subtitle": "4,5. Klasyfikacja tematyczna (terminologii ciąg dalszy)", + "title": "Komputerowe wspomaganie tłumaczenia", + "year": "2021" + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lab/lab_06-07.ipynb b/lab/lab_06-07.ipynb new file mode 100644 index 0000000..3b0a71a --- /dev/null +++ b/lab/lab_06-07.ipynb @@ -0,0 +1,216 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "described-terrain", + "metadata": {}, + "source": [ + "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", + "
\n", + "

Komputerowe wspomaganie tłumaczenia

\n", + "

6,7. Preprocessing i postprocessing [laboratoria]

\n", + "

Rafał Jaworski (2021)

\n", + "
\n", + "\n", + "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "colored-nothing", + "metadata": {}, + "source": [ + "Na dzisiejszych zajęciach zajmiemy się niezwykle przydatnymi narzędziami wspomagającymi pracę tłumacza. W odróżnieniu od dotychczas poznanych, nie są one oparte na pamięci tłumaczeń, ani na słownikach. Chodzi o techniki preprocessingu i postprocessingu." + ] + }, + { + "cell_type": "markdown", + "id": "atomic-rubber", + "metadata": {}, + "source": [ + "Proces tłumaczenia przeprowadzony w pełni profesjonalnie składa się z wielu faz, które angażują nie tylko tłumaczy, ale także kierowników projektu, analityków, czy korektorów. Każda z tych osób do swojej pracy może wykorzystywać system informatyczny, do którego na początku całego procesu trafiają pliki do tłumaczenia. Oznacza to, że zanim tekst źródłowy trafi do tłumacza, system ma jeszcze szansę coś w nim zmienić. A kiedy tłumacz wykona już swoją pracę, można uruchomić kolejny mechanizm, który zmodyfikuje tłumaczenie przed oddaniem go do zamawiającego. Jak się domyślamy, modyfikacje tekstu przed przekazaniem go do tłumacza nazywamy **preprocessingiem**, natomiast te dokonywane po wykonaniu tłumaczenia (ale przed zwróceniem go do klienta) nazywamy **postprocessingiem**. Terminy te, będące mało zgrabnymi kalkami z języka angielskiego, mają wersje prawdziwie polskie: przetwarzanie wstępne i końcowe. Wersje te są jednak stosowane na tyle rzadko, że mogą jedynie wprowadzić zamieszanie (co w gruncie rzeczy jest dość smutne)." + ] + }, + { + "cell_type": "markdown", + "id": "mature-republic", + "metadata": {}, + "source": [ + "Typowe operacje w fazie preprocessingu obejmują:\n", + "* identyfikację tagów xmlowych (które często są później wyświetlane w interfejsie CAT-a jako jeden niepodzielny znak)\n", + "* identyfikację segmentów, których nie należy tłumaczyć (na przykład składających się z samych liczb)\n", + "\n", + "* identyfikację dat i jednostek miary w tekście źródłowym\n", + "\n", + "We wszystkich tych operacjach niezwykle przydatne okazują się wyrażenia regularne." + ] + }, + { + "cell_type": "markdown", + "id": "southern-applicant", + "metadata": {}, + "source": [ + "### Ćwiczenie 1: Używając wyrażeń regularnych napisz funkcję do znajdowania wszystkich tagów XML w tekście. Funkcja powinna zwracać pozycje, na których znalazła tagi." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "documented-hacker", + "metadata": {}, + "outputs": [], + "source": [ + "def find_tags(text):\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "determined-utilization", + "metadata": {}, + "source": [ + "### Ćwiczenie 2: Używając wyrażeń regularnych napisz funkcję do identyfikacji segmentów, których nie należy tłumaczyć. Zastosuj wymyślone przez siebie kryteria. Funkcja is_translatable powinna zwracać True, jeśli segment powinien być przetłumaczony przez tłumacza (zwykłe zdanie). False powinno być zwrócone, kiedy segment jest nieprzetłumaczalny i powinien zostać skopiowany (np. 4.2.1.)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "unauthorized-study", + "metadata": {}, + "outputs": [], + "source": [ + "def is_translatable(text):\n", + " return True" + ] + }, + { + "cell_type": "markdown", + "id": "plastic-crown", + "metadata": {}, + "source": [ + "### Ćwiczenie 3: Używając wyrażeń regularnych napisz funkcję do identyfikacji i interpretacji 5 wybranych przez siebie formatów daty. Funkcja powinna zwracać pozycje, na których odnalazła daty oraz dzień, miesiąc i rok, które ta data reprezentuje." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "beautiful-mathematics", + "metadata": {}, + "outputs": [], + "source": [ + "def find_dates(text):\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "hourly-incentive", + "metadata": {}, + "source": [ + "Po preprocessingu i tłumaczeniu czas na postprocessing. Ponieważ wykonywany jest on na przetłumaczonym tekście, jego głównym zadaniem jest eliminacja błędów popełnionych przez tłumacza w fazie tłumaczenia. Podczas postprocessingu najczęściej wykonuje się:\n", + "* korektę pisowni dla języka docelowego\n", + "* usuwanie błędów typograficznych z tekstu (np. wielokrotne spacje, brak spacji po przecinku)\n", + "Stanowi to bardzo ważne wsparcie dla edytorów i korektorów, czyli osób sprawdzających pracę tłumacza.\n", + "\n", + "Jednak nowoczesne CAT-y potrafią coś jeszcze. Są w stanie w sprytny sposób wykorzystać kombinację pre- i postprocessingu do wyręczenia tłumacza w żmudnych i technicznych czynnościach. Wykonajmy następujące ćwiczenie:" + ] + }, + { + "cell_type": "markdown", + "id": "dental-combination", + "metadata": {}, + "source": [ + "### Ćwiczenie 4: Wykorzystując funkcję find_dates napisz funkcję do obsługi dat w tłumaczeniu. Wejściem jest segment źródłowy oraz docelowy, które zawierają daty, przy czym daty te mogą być w różnych formatach. Dodatkowym parametrem wejściowym jest nazwa oczekiwanego formatu daty w tłumaczeniu (np. \"Europe\", \"US\", \"digit-dot\". Funkcja najpierw sprawdza, czy liczba dat w tłumaczeniu zgadza się z liczbą dat w segmencie źródłowym oraz czy odpowiadające sobie daty wskazują na ten sam dzień. Jeśli nie, wypisywane jest stosowne ostrzeżenie. Oczekiwanym wyjściem jest segment docelowy, w którym wszystkie daty są w żądanym formacie. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "finished-essex", + "metadata": {}, + "outputs": [], + "source": [ + "def correct_dates(source_segment, target_segment, date_format):\n", + " return ''" + ] + }, + { + "cell_type": "markdown", + "id": "vertical-divide", + "metadata": {}, + "source": [ + "Co jeszcze można zrobić? Zajmijmy się tagami XML. Z punktu widzenia tłumacza najlepiej byłoby, gdyby mógł przetłumaczyć segment źródłowy zawierający tagi XML na język docelowy zupełnie ignorując te tagi. Ponieważ jednak tagi muszą jakoś znaleźć się w segmencie docelowym, przydałaby się jakaś \"magiczna różdżka\", która przeniosłaby wszystkie tagi ze źródła do tłumaczenia na mniej więcej dobre miejsca. Spełnijmy marzenie tłumacza!" + ] + }, + { + "cell_type": "markdown", + "id": "trained-trouble", + "metadata": {}, + "source": [ + "Rozważmy następujący algorytm: na wejściu mamy segment źródłowy zawierający tagi oraz segment docelowy bez tagów. Dokonujemy tokenizacji segmentu źródłowego tak, aby tagi były osobnymi tokenami. Następnie przeprowadźmy tokenizację segmentu docelowego. Gdy to jest gotowe, możemy zabrać się za przenoszenie (kopiowanie) tagów z segmentu źródłowego do docelowego." + ] + }, + { + "cell_type": "markdown", + "id": "damaged-simpson", + "metadata": {}, + "source": [ + "![Transfer tagów](img/tagtransfer.png)" + ] + }, + { + "cell_type": "markdown", + "id": "numerical-southeast", + "metadata": {}, + "source": [ + "Gdzie w segmencie docelowym powinien trafić tag? Przede wszystkim pomiędzy tokeny - nie chcemy rozbijać słów tagami. Pytanie tylko, pomiędzy które tokeny? Jeśli sytuacja jest taka, jak powyżej, kiedy segment źródłowy i docelowy mają taką samą liczbę słów nie będących tagami, przenosimy tagi na odpowiadające pozycje w tłumaczeniu. Natomiast jeśli długość tłumaczenia jest inna niż źródła, należy obliczać te pozycje w sposób proporcjonalny - jeśli np. mamy tag w źródle na pozycji 3, a tłumaczenie jest dwa razy dłuższe niż źródło, tag powinien być przeniesiony do tłumaczenia na pozycję 6. W przypadku niecałkowitych wartości proporcji stosujemy zaokrąglenia." + ] + }, + { + "cell_type": "markdown", + "id": "separated-socket", + "metadata": {}, + "source": [ + "### Ćwiczenie 5: Zaimplementuj opisany algorytm transferu tagów." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "romance-judge", + "metadata": {}, + "outputs": [], + "source": [ + "def transfer_tags(source_segment, target_segment):\n", + " return ''" + ] + } + ], + "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": "3.8.10" + }, + "subtitle": "6,7. Preprocessing i postprocessing", + "title": "Komputerowe wspomaganie tłumaczenia", + "year": "2021" + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lab/lab_08.ipynb b/lab/lab_08.ipynb new file mode 100644 index 0000000..569c392 --- /dev/null +++ b/lab/lab_08.ipynb @@ -0,0 +1,217 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "improved-register", + "metadata": {}, + "source": [ + "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", + "
\n", + "

Komputerowe wspomaganie tłumaczenia

\n", + "

8. Wykorzystanie tłumaczenia automatycznego we wspomaganiu tłumaczenia [laboratoria]

\n", + "

Rafał Jaworski (2021)

\n", + "
\n", + "\n", + "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "hungarian-davis", + "metadata": {}, + "source": [ + "W dzisiejszych czasach, niezwykle ważną techniką wspomagania tłumaczenia jest użycie tłumaczenia maszynowego. Tekst źródłowy do tłumaczenia jest najpierw tłumaczony całkowicie autommatycznie, a następnie tłumacz ludzki dokonuje korekty wyniku. Technologia tłumaczenia maszynowego jest już na tyle dojrzała, że oferuje bardzo wysoką jakość wyników. Coraz częstsze stają się scenariusze, w których ludzka korekta to niemal całkowicie machinalne (sic!) zatwierdzanie wyników tłumaczenia maszynowego. Na dzisiejszych zajęciach poznamy techniki ewaluacji tłumaczenia maszynowego oraz sprawdzania jego przydatności w procesie wspomagania tłumaczenia ludzkiego." + ] + }, + { + "cell_type": "markdown", + "id": "posted-commons", + "metadata": {}, + "source": [ + "Jakość tłumaczenia maszynowego możemy oceniać na dwóch niezależnych płaszczyznach: dokładność i płynność. Płynność jest subiektywnie odbieranym odczuciem, że czytany tekst jest napisany językiem naturalnym i zrozumiałym. Systemy tłumaczenia maszynowego oparte na uczeniu głębokim z wykorzystaniem sieci neuronowych osiągają duży stopień płynności tłumaczenia. Niestety jednak ich dokładność nie zawsze jest równie wysoka." + ] + }, + { + "cell_type": "markdown", + "id": "referenced-implement", + "metadata": {}, + "source": [ + "Dokładność tłumaczenia maszynowego jest parametrem, który łatwiej zmierzyć. Wartość takich pomiarów daje obraz tego, jaka jest faktyczna jakość tłumaczenia maszynowego i jaka jest jego potencjalna przydatność we wspomaganiu tłumaczenia." + ] + }, + { + "cell_type": "markdown", + "id": "disturbed-january", + "metadata": {}, + "source": [ + "Najczęściej stosowaną techniką oceny tłumaczenia maszynowego jest ocena BLEU. Do obliczenia tej oceny potrzebny jest wynik tłumaczenia maszynowego oraz referencyjne tłumaczenie ludzkie wysokiej jakości." + ] + }, + { + "cell_type": "markdown", + "id": "dental-combination", + "metadata": {}, + "source": [ + "### Ćwiczenie 1: Zaimplementuj program do obliczania oceny BLEU dla korpusu w folderze data. Użyj implementacji BLEU z pakietu nltk. Dodatkowe wymaganie techniczne - napisz program tak, aby nie musiał rozpakwowywać pliku zip z korpusem na dysku." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "moving-clothing", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_bleu():\n", + " return 0" + ] + }, + { + "cell_type": "markdown", + "id": "jewish-ethics", + "metadata": {}, + "source": [ + "### Ćwiczenie 2: Oblicz wartość bleu na różnych fragmentach przykładowego korpusu (np. na pierwszych 100 zdaniach, zdaniach 500-600). Czy w jakimś fragmencie korpusu jakość tłumaczenia znacząco odbiega od średniej?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "lasting-rolling", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_bleu():\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "listed-bikini", + "metadata": {}, + "source": [ + "Inną metodą oceny jakości tłumaczenia maszynowego jest parametr WER - Word Error Rate. Definiuje się on w następujący sposób:\n", + "\n", + "$WER = \\frac{S+D+I}{N}=\\frac{S+D+I}{S+D+C}$\n", + "\n", + "gdzie:\n", + " * S - liczba substytucji (słów)\n", + " * D - liczba usunięć\n", + " * I - liczba wstawień\n", + " * C - liczba poprawnych śłów\n", + " * N - liczba słów w tłumaczeniu referencyjnym (N=S+D+C)" + ] + }, + { + "cell_type": "markdown", + "id": "conscious-cookbook", + "metadata": {}, + "source": [ + "Miara ta jest zwykle używana w do oceny systemów automatycznego rozpoznawania mowy, jednak w kontekście wspomagania tłumaczenia może być rozumiana jako wielkość nakładu pracy tłumacza nad poprawieniem tłumaczenia maszynowego." + ] + }, + { + "cell_type": "markdown", + "id": "split-palace", + "metadata": {}, + "source": [ + "### Ćwiczenie 3: Oblicz wartość WER dla przykładowego korpusu. Skorzystaj z gotowej implementacji WER." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "occupied-swing", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_wer():\n", + " return 0" + ] + }, + { + "cell_type": "markdown", + "id": "stretch-wound", + "metadata": {}, + "source": [ + "Poza wymienionymi powyżej, stosować można jeszcze inne miary oparte na porównywaniu tłumaczenia maszynowego z ludzkim. Przypomnijmy sobie jedną, którą stosowaliśmy wcześniej." + ] + }, + { + "cell_type": "markdown", + "id": "abstract-wilderness", + "metadata": {}, + "source": [ + "### Ćwiczenie 4: Oblicz średnią wartość dystansu Levenshteina pomiędzy zdaniami przetłumaczonymi automatycznie oraz przez człowieka. Użyj implementacji z ćwiczeń nr 2." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "immediate-element", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_levenshtein():\n", + " return 0" + ] + }, + { + "cell_type": "markdown", + "id": "filled-burton", + "metadata": {}, + "source": [ + "A teraz sprawdźmy coś jeszcze. W danych przykładowego korpusu znajduje się także angielski tekst źródłowy. Teoretycznie, dobre tłumaczenie niemieckie powinno zawierać jak najwięcej słów z angielskiego źródła. Wykonajmy najstępujący eksperyment:" + ] + }, + { + "cell_type": "markdown", + "id": "grateful-recruitment", + "metadata": {}, + "source": [ + "### Ćwiczenie 5: Dla każdej trójki zdań z korpusu przykładowego wykonaj następujące kroki:\n", + " * Przetłumacz każde angielskie słowo na niemiecki przy użyciu modułu PyDictionary.\n", + " * Sprawdź, które z niemieckich tłumaczeń zawiera więcej spośród tych przetłumaczonych słów - automatyczne, czy ludzkie.\n", + "Następnie wypisz statystyki zbiorcze. Które tłumaczenie zawiera więcej słownikowych tłumaczeń słów ze źródła?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "descending-easter", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_translations():\n", + " return []" + ] + } + ], + "metadata": { + "author": "Rafał Jaworski", + "email": "rjawor@amu.edu.pl", + "lang": "pl", + "subtitle": "8. Wykorzystanie tłumaczenia automatycznego we wspomaganiu tłumaczenia", + "title": "Komputerowe wspomaganie tłumaczenia", + "year": "2021", + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lab/lab_09-10.ipynb b/lab/lab_09-10.ipynb new file mode 100644 index 0000000..683cdfa --- /dev/null +++ b/lab/lab_09-10.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "competitive-desire", + "metadata": {}, + "source": [ + "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", + "
\n", + "

Komputerowe wspomaganie tłumaczenia

\n", + "

9,10. Web scraping [laboratoria]

\n", + "

Rafał Jaworski (2021)

\n", + "
\n", + "\n", + "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "hungarian-davis", + "metadata": {}, + "source": [ + "Jak dobrze wiemy, w procesie wspomagania tłumaczenia oraz w zagadnieniach przetwarzania języka naturalnego ogromną rolę pełnią zasoby lingwistyczne. Należą do nich korpusy równoległe (pamięci tłumaczeń), korpusy jednojęzyczne oraz słowniki. Bywa, że zasoby te nie są dostępne dla języka, nad którym chcemy pracować." + ] + }, + { + "cell_type": "markdown", + "id": "featured-moisture", + "metadata": {}, + "source": [ + "W tej sytuacji jest jeszcze dla nas ratunek - możemy skorzystać z zasobów dostępnych publicznie w Internecie. Na dzisiejszych zajęciach przećwiczymy techniki pobierania tekstu ze stron internetowych." + ] + }, + { + "cell_type": "markdown", + "id": "underlying-isaac", + "metadata": {}, + "source": [ + "Poniższy kod służy do ściągnięcia zawartości strony (w formacie HTML do zmiennej) oraz do wyszukania na tej stronie konkretnych elementów. Przed jego uruchomieniem należy zainstalować moduł BeautifulSoup:\n", + "`pip3 install beautifulsoup4`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "revolutionary-trust", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nastolatek ukradł znajomemu 4500 złotych. Wcześniej pił z nim alkohol\n", + "Czekają nas kolejne podwyżki rachunków. Tym razem za ogrzewanie i ciepłą wodę\n", + "Nie żyje Piotr Ś. Czyściciel kamienic miał 47 lat\n", + "Maciej Skorża nie zmienił zdania o systemie na mecz z Rakowem. Kolejorz ma szybką okazję do rehabilitacji\n", + "Kto zabił Kazimierę Kurkowiak? Poznańskie Archiwum X wraca do sprawy sprzed 30 lat\n", + "Mieszkańcy osiedla Kwiatowego zyskają nowy chodnik\n", + "Poznańskie ZOO ponownie się otwiera i apeluje o kupowanie biletów online\n", + "1700 zł mandatu dla motocyklisty: nie ma prawa jazdy, jechał za szybko\n", + "Plac Wolności ma tętnić życiem. Jest koncepcja zagospodarowania\n", + "Dzikie wysypisko w Wielkopolskim Parku Narodowym, a w nim paczka z telefonem odbiorcy\n", + "Dobre wieści z Łazarza! \"Zielona Perła\" sprzedana!\n", + "Sokoły wędrowne w gnieździe na kominie poznańskiej elektrociepłowni! Są 4 młode\n", + "720 nowych zakażeń w Wielkopolsce\n", + "Uderzył kobietę w sklepie: \"sprawca będzie rozliczony\"\n", + "Zespół Szkół Geodezyjno- Drogowych. Przyszłość rysuje się w kolorowych barwach!\n", + "Tajemniczy wypadek i pożar pod Kwilczem. Auto spłonęło, w środku nikogo nie było\n", + "Nad Jeziorem Maltańskim powstanie duży hotel? \"Ma uzupełniać infrastrukturę sportową\"\n", + "Śmiertelny wypadek na trasie S8: samochód potrącił rowerzystę\n", + "Specjaliści o poszukiwaniu Natalii Lick: \"niestety trop psa prowadził na Wartostradę\"\n", + "Korki przy skrzyżowaniu Grochowska / Grunwaldzka: ruszyły prace!\n", + "Restauracja w Kaliszu przyjmuje klientów: sanepid i policja \"odwiedzili\" lokal\n", + "Ile kosztuje wywóz odpadów?\n", + "Dachowanie auta na trasie Konin - Turek\n", + "Kierowca BMW pod wpływem narkotyków, pasażer w ich posiadaniu. Obaj zostali zatrzymani\n", + "Leszno: mężczyzna uderzył klientkę sklepu. Poszło o maseczkę?\n", + "Od poniedziałku zapłacimy za parkowanie na kolejnych ulicach\n", + "Włamał się do obiektu handlowego. Grozi mu nawet 15 lat więzienia\n", + "Rondo Śródka: kolizja z udziałem dwóch pojazdów\n", + "Europoseł PSL: oświadczenie Episkopatu ma wpływ na proces szczepień. \"Bardzo dużo ludzi zrezygnowało\"\n", + "Bezcenna wygrana Enea Energetyka. Poznanianki zagrają w fazie play-off\n", + "No to w drogę! Po odmienionych trasach w Wielkopolsce\n" + ] + } + ], + "source": [ + "import requests\n", + "from bs4 import BeautifulSoup\n", + "\n", + "url='https://epoznan.pl'\n", + "\n", + "page = requests.get(url)\n", + "soup = BeautifulSoup(page.content, 'html.parser')\n", + "\n", + "headers = soup.find_all('h3', {'class':'postItem__title'})\n", + "\n", + "print('\\n'.join([header.get_text() for header in headers]))" + ] + }, + { + "cell_type": "markdown", + "id": "dental-combination", + "metadata": {}, + "source": [ + "### Ćwiczenie 1: Napisz funkcję do pobierania nazw towarów z serwisu Ceneo.pl. Typ towaru, np. telewizor, pralka, laptop jest parametrem funkcji. Wystarczy pobierać dane z pierwszej strony wyników wyszukiwania." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "moving-clothing", + "metadata": {}, + "outputs": [], + "source": [ + "def get_names(article_type):\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "mechanical-produce", + "metadata": {}, + "source": [ + "W ten sposób pobieramy dane z jednej strony. Nic jednak nie stoi nam na przeszkodzie, aby zasymulować przełączanie stron." + ] + }, + { + "cell_type": "markdown", + "id": "legitimate-corrections", + "metadata": {}, + "source": [ + "### Ćwiczenie 2: Zaobserwuj, jak zmienia się url strony podczas przechodzenia do kolejnych stron wyników wyszukiwania na Ceneo.pl. Wykorzystaj tę informację i uruchom funkcję get_names() na więcej niż jednej stronie wyników." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "german-dispute", + "metadata": {}, + "outputs": [], + "source": [ + "def scrape_names():\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "discrete-durham", + "metadata": {}, + "source": [ + "Technika pobierania treści z Internetu jest szczególnie efektywnym sposobem na pozyskiwanie dużych ilości tekstu. Poniższy fragment kodu służy do ściągnięcia całości tekstu ze strony." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "premium-button", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Yahoo Make Yahoo Your HomepageDiscover something new every day from News, Sports, Finance, Entertainment and more! HOME MAIL NEWS FINANCE SPORTS ENTERTAINMENT LIFE SHOPPING YAHOO PLUS MORE... Download the Yahoo Home app Yahoo Home Search query Sign in Mail Sign in to view your mail Mail Mail COVID-19 COVID-19 News News Finance Finance Sports Sports Entertainment Entertainment Life Life Shopping Shopping Yahoo Plus Yahoo Plus More... More... Follow live:Closing arguments begin for Derek Chauvin's murder trial in the death of George Floyd 5 people in hospital after shooting in Louisiana One victim was shot in the head, and another suffered multiple gunshot wounds, according to local news outlet.Multiple police units dispatched to scene »2 dead in crash of Tesla with 'no one' drivingMall shooter, 16, faces 1st-degree murder charge'80s pop star rips 'Simpsons' for 'hateful' parodyConspiracy theorist Alex Jones faces a reckoningPig's head left at former home of Chauvin trial witness U.S.HuffPostFirst-Ever Wild Wolf Collar Camera Shows What They Really Do All Day LongThis canine's favorite meal might surprise you. Thanks for your feedback! CelebrityThe TelegraphRobert De Niro unable to turn down acting roles because of his ‘estranged wife's expensive lifestyle’Hollywood legend Robert De Niro is unable to turn down acting roles because he must pay for his estranged wife's expensive tastes, the actor's lawyer has claimed. Caroline Krauss told a Manhattan court that he is struggling financially because of the pandemic, a massive tax bill and the demands of Grace Hightower, who filed for divorce in 2018 after 21 years of marriage. The court has been asked to settle how much De Niro should pay Ms Hightower, 66, until the terms of the prenuptial agreement the couple negotiated in 2004 takes effect. “Mr De Niro is 77 years old, and while he loves his craft, he should not be forced to work at this prodigious pace because he has to,” Ms Krauss told the court. “When does that stop? When does he get the opportunity to not take every project that comes along and not work six-day weeks, 12-hour days so he can keep pace with Ms Hightower’s thirst for Stella McCartney?” Thanks for your feedback! U.S.Associated PressCouple: Man has tossed used cups in their yard for 3 yearsAn upstate New York couple may have finally solved the mystery of who's been tossing used coffee cups in their front yard for nearly three years. Edward and Cheryl Patton told The Buffalo News they tried mounting a camera in a tree in front of their home in Lake View to catch the phantom litterer. After Edward Patton called police, they waited and pulled over a vehicle driven by 76-year-old Larry Pope, who Cheryl Patton said had once worked with her and had had disagreements with her over union issues. Thanks for your feedback! U.S.INSIDERA leading conspiracy theorist who thought COVID-19 was a hoax died from the virus after hosting illegal house partiesA high-profile conspiracy theorist from Norway, who shared false information about the pandemic online, has died from COVID-19, officials say. Thanks for your feedback! PoliticsThe WeekOne America News Network producer says 'majority' of employees didn't believe reports on voter fraud claimsMarty Golingan, a producer at One America News Network, a right-wing cable news channel often noted for its affinity for former President Donald Trump, told The New York Times he was worried his work may have helped inspire the Jan. 6 Capitol riot. At one point during the incident, Golingan said he caught sight of someone in the mob holding a flag with OAN's logo. \"I was like, OK, that's not good. That's what happens when people listen to us,\" he told the Times, referring to OAN's coverage of the 2020 presidential election, which often gave credence to Trump's unfounded claims of widespread voter fraud and Democratic conspiracies. Golingan said that many of his colleagues, including himself, disagreed with the coverage. \"The majority of people did not believe the voter fraud claims being run on the air,\" he told the Times. Indeed, the Times interviewed 18 current and former OAN employees, 16 of whom said the channel has \"broadcast reports that they considered misleading, inaccurate, or untrue.\" But Allysia Britton, a former producer and one of more than a dozen employees to leave OAN in the wake of the riot, explained that while \"many people have raised concerns ... when people speak up about anything, you will get in trouble.\" Read more at The New York Times. More stories from theweek.comThe new HBO show you won't be able to stop watchingDonald Trump's most dangerous political legacyTrump's NSA general counsel Michael Ellis resigns, never having taken office Thanks for your feedback! BusinessMoneyWiseFourth stimulus check update: Biden faces mounting pressure for new paymentAdvocates and lawmakers say the crisis isn't over, and neither is the need for relief. Thanks for your feedback! CelebrityThe TelegraphLand Rover driver at Prince Philip's funeral spent week ensuring he could drive at correct speedHuffPostPrince Philip's Funeral, In PhotosUSA TODAY EntertainmentWhy did Prince Philip's Land Rover carry his casket? The story behind the strange hearse Thanks for your feedback! Trending Now1. Gianna Hammer2. Derek Chauvin3. Black Rob4. 2021 Acm Awards5. Baby Shower Invitations6. Amanda Broderick7. Mortgage Refinance Calculator8. Interest Rates Today9. Tesla Crash10. Mars Helicopter Yahoo! Mail WeatherWeatherGreater PolandView your LocationsRemove from favorite locationsDetect my locationEnter City or ZipcodeManage LocationsToday66°45°TueRain today with a high of 59 °F (15.0 °C) and a low of 41 °F (5.0 °C). There is a 50% chance of precipitation.59°41°WedPartly cloudy today with a high of 57 °F (13.9 °C) and a low of 41 °F (5.0 °C).57°41°ThuScattered showers today with a high of 48 °F (8.9 °C) and a low of 37 °F (2.8 °C). There is a 35% chance of precipitation.48°37°See More » ScoreboardChange Sports to display different scoresNBA NFL MLB NHL NCAAB NCAAF Trending YesterdayTodayTomorrowPortland Charlotte 101109FinalSacramento Dallas 121107FinalMinnesota LA Clippers 105124FinalMore scores » HoroscopeChange your horoscope signAriesTaurusGeminiCancerLeoVirgoLibraScorpioSagittariusCapricornAquariusPiscesApril 19 -Aries - You're feeling the heat, and you may find that your friends like it as much as you do! Your great energy is perfect for almost any activity, so light up the night and have a great time! See more » Yahoo! Mail Yahoo! Sports Terms (Updated)Privacy (Updated)AdvertiseAbout Our AdsCareersHelpFeedback Close this content, you can also use the Escape key at anytime \n" + ] + } + ], + "source": [ + "import re\n", + "\n", + "url = \"https://www.yahoo.com\"\n", + "\n", + "page = requests.get(url)\n", + "soup = BeautifulSoup(page.content, 'html.parser')\n", + "\n", + "# usunięcie elementów script i style\n", + "for script in soup([\"script\", \"style\"]):\n", + " script.extract() # usuń element\n", + "\n", + "# pobierz tekst\n", + "text = soup.get_text()\n", + "\n", + "# usuń wielokrotne białe znaki\n", + "text = re.sub(r\"\\s+\", \" \", text)\n", + "\n", + "print(text)" + ] + }, + { + "cell_type": "markdown", + "id": "assigned-necessity", + "metadata": {}, + "source": [ + "### Ćwiczenie 3: Napisz program do pobrania tekstu ze strony Wydziału Matematyki i Informatyki. Pobierz cały tekst ze strony głównej a następnie wyszukaj na tej stronie wszystkich linków wewnętrznych i pobierz tekst ze stron wskazywanych przez te linki. Nie zagłębiaj się już dalej.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "regulation-sheriff", + "metadata": {}, + "outputs": [], + "source": [ + "def scrape_wmi():\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "paperback-hello", + "metadata": {}, + "source": [ + "Omówione wyżej techniki działają również bardzo dobrze dla zasobów słownikowych." + ] + }, + { + "cell_type": "markdown", + "id": "after-activity", + "metadata": {}, + "source": [ + "### Ćwiczenie 4: Pobierz jak najwięcej słów w języku albańskim z serwisu glosbe.com." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "surgical-ozone", + "metadata": {}, + "outputs": [], + "source": [ + "def scrape_shqip():\n", + " return []" + ] + } + ], + "metadata": { + "author": "Rafał Jaworski", + "email": "rjawor@amu.edu.pl", + "lang": "pl", + "subtitle": "9,10. Web scraping", + "title": "Komputerowe wspomaganie tłumaczenia", + "year": "2021", + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lab/lab_11.ipynb b/lab/lab_11.ipynb new file mode 100644 index 0000000..0a8ce14 --- /dev/null +++ b/lab/lab_11.ipynb @@ -0,0 +1,214 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "expanded-entrance", + "metadata": {}, + "source": [ + "# Komputerowe wspomaganie tłumaczenia" + ] + }, + { + "cell_type": "markdown", + "id": "atlantic-greenhouse", + "metadata": {}, + "source": [ + "# Zajęcia 11 - urównoleglanie" + ] + }, + { + "cell_type": "markdown", + "id": "hungarian-davis", + "metadata": {}, + "source": [ + "Na poprzednich zajęciach poznaliśmy techniki pozyskiwania tekstu z Internetu. Jeśli uda nam się w ten sposób pozyskać tekst w jednym języku oraz jego tłumaczenie na inny język, jesteśmy tylko o krok od uzyskania najbardziej przydatnego zasobu z punktu widzenia wspomagania tłumaczenia - pamięci tłumaczeń. Krokiem tym jest automatyczne urównoleglanie tekstu." + ] + }, + { + "cell_type": "markdown", + "id": "bronze-removal", + "metadata": {}, + "source": [ + "Automatyczne urównoleglanie tekstu składa się z dwóch kroków:\n", + "1. Podziału tekstu źródłowego oraz docelowego na zdania.\n", + "2. Dopasowaniu zdań źródłowych do docelowych." + ] + }, + { + "cell_type": "markdown", + "id": "junior-works", + "metadata": {}, + "source": [ + "Zdania, o których mowa w punkcie 1., powinniśmy rozumieć jako segmenty, tj. niekoniecznie kompletne zdania w sensie gramatycznym. Standardowym sposobem podziału tekstu na segmenty jest dzielenie po znaku nowej linii lub zaraz po kropce, o ile jest ona częścią sekwencji: \".[spacja][Wielka litera]\"" + ] + }, + { + "cell_type": "markdown", + "id": "legitimate-corrections", + "metadata": {}, + "source": [ + "### Ćwiczenie 1: Zaimplementuj podstawowy algorytm segmentacji tekstu. Użyj odpowiedniego wyrażenia regularnego, łapiącego wielkie litery w dowolnym języku, np. \"Ż\" (użyj klasy unikodowej). Zwróć listę segmentów." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "german-dispute", + "metadata": {}, + "outputs": [], + "source": [ + "def sentence_split(text):\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "continued-assessment", + "metadata": {}, + "source": [ + "### Ćwiczenie 2: Uruchom powyższy algorytm na treści wybranej przez siebie strony internetowej (do ściągnięcia treści strony wykorzystaj kod z laboratoriów nr 7). Zidentyfikuj co najmniej dwa wyjątki od ogólnej reguły podziału na segmenty i ulepsz algorytm." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "guilty-morocco", + "metadata": {}, + "outputs": [], + "source": [ + "def sentence_split_enhanced(text):\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "experimental-recipient", + "metadata": {}, + "source": [ + "Po podziale tekstu na segmenty po stronie źródłowej oraz docelowej, możemy przystąpić do kroku drugiego - dopasowania segmentów. Głównym wyzwaniem tego kroku jest fakt, iż po stronie źródłowej może być inna liczba segmentów, niż po stronie docelowej. Takie rozbieżności są bardzo częste, a wynikają między innymi z:\n", + "* tłumaczenia jednego zdania źródłowego przy użyciu więcej niż jednego zdania\n", + "* tłumaczenia więcej niż jednego zdania źródłowego przy użyciu jednego zdania\n", + "* pominięcia zdania podczas tłumaczenia\n", + "* rozbieżności pomiędzy wersjami tekstu źródłowego i docelowego (np. tekst źródłowy mógł być modyfikowany po przetłumaczeniu i tłumaczenie nie zostało zaktualizowane)\n", + "* przetłumaczenia tekstu źródłowego tylko częściowo" + ] + }, + { + "cell_type": "markdown", + "id": "australian-hundred", + "metadata": {}, + "source": [ + "Problemy te rozwiązwyane są na różne sposoby. Najpopularniejszym programem do przeprowadzania urównoleglania jest [Hunalign](https://github.com/danielvarga/hunalign). Wejściem do programu są dwa pliki, zawierające po jednym segmencie w linii. Wyjściem - plik urównoleglony w wewnętrznym formacie hunaligna." + ] + }, + { + "cell_type": "markdown", + "id": "russian-chambers", + "metadata": {}, + "source": [ + "### Ćwiczenie 3: Odnajdź dowolną stronę, która jest dostępna w wielu językach. Pobierz z tej strony tekst oryginalny (tylko ze strony głównej) oraz przetłumaczony na dowolny inny język. Przy użyciu Pythona przygotuj pliki dla Hunaligna i uruchom go." + ] + }, + { + "cell_type": "markdown", + "id": "controlled-pacific", + "metadata": {}, + "source": [ + "Wyjściem z Hunaligna jest plik w specjalnym formacie Hunaligna. Problem jednak w tym, że niestety nie można go w prosty sposób zaimportować do jakiegokolwiek narzędzia typu CAT. Potrzebna jest konwersja do któregoś z bardziej popularnych formatów, np. XLIFF." + ] + }, + { + "cell_type": "markdown", + "id": "divided-chain", + "metadata": {}, + "source": [ + "XLIFF jest formatem do przechowywania pamięci tłumaczeń, który opiera się na XML-u. Przykładowy plik XLIFF wygląda następująco:" + ] + }, + { + "cell_type": "raw", + "id": "appropriate-timber", + "metadata": {}, + "source": [ + "\n", + "\n", + " \n", + "
\n", + " \n", + " sample\n", + " KWT\n", + " KWT\n", + " 123\n", + " sample XLIFF file\n", + " \n", + "
\n", + " \n", + " \n", + " Hello world!\n", + " Hola mundo!\n", + " \n", + " \n", + " File\n", + " Archivo\n", + " \n", + " \n", + " New\n", + " Nuevo\n", + " \n", + " \n", + " Exit\n", + " Salir\n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "falling-greenhouse", + "metadata": {}, + "source": [ + "### Ćwiczenie 4: Napisz konwerter formatu hunaligna na XLIFF." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "remarkable-pillow", + "metadata": {}, + "outputs": [], + "source": [ + "def convert2xliff(hunalign_file_name):\n", + " return 0" + ] + } + ], + "metadata": { + "author": "Rafał Jaworski", + "email": "rjawor@amu.edu.pl", + "lang": "pl", + "subtitle": "11. Urównoleglanie", + "title": "Komputerowe wspomaganie tłumaczenia", + "year": "2021", + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lab/lab_12.ipynb b/lab/lab_12.ipynb new file mode 100644 index 0000000..c4dc223 --- /dev/null +++ b/lab/lab_12.ipynb @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "virtual-accreditation", + "metadata": {}, + "source": [ + "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", + "
\n", + "

Komputerowe wspomaganie tłumaczenia

\n", + "

12. Key logging [laboratoria]

\n", + "

Rafał Jaworski (2021)

\n", + "
\n", + "\n", + "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "featured-afghanistan", + "metadata": {}, + "source": [ + "Badania nad komputerowym wspomaganiem tłumaczenia często prowadzone są przy użyciu metodologii testowania interfejsów użytkownika - UI/UX testing. Program typu CAT traktuje się wówczas jak każdy inny program komputerowy i przeprowadza testy wydajności i użyteczności." + ] + }, + { + "cell_type": "markdown", + "id": "severe-protein", + "metadata": {}, + "source": [ + "Testy takie prowadzone są zawsze na użytkownikach końcowych, w tym przypadku - na tłumaczach. Podstawowym celem testów jest próba zaobserwowania faktycznego sposobu pracy tłumacza - które funkcje programu są przez niego wykorzystywane najczęściej, jakich innych narzędzi poza CAT-em używa on do swojej pracy, które funkcje programu działają zgodnie, a które niezgodnie z intuicją użytkownika oraz wiele innych czynników. Aby wszystkie te analizy były możliwe, konieczne jest zgromadzenie jak największej ilości danych dotyczących przebiegu testu." + ] + }, + { + "cell_type": "markdown", + "id": "constant-underground", + "metadata": {}, + "source": [ + "Testy są przede wszystkim nagrywane. Nagrywany jest zarówno ekran komputera (screen capture), jak i sam użytkownik pracujący przy komputerze. To jednak nie wszystko - często stosuje się specjalne techniki eye-trackingu, które są w stanie określić, w który punk ekranu użytkownik aktualnie patrzy. Dane pozyskane w ten sposób używane są do analizy czasu znalezienia przez użytkownika potrzebnej mu funkcji oraz zidentyfikowania miejsc, gdzie tej funkcji poszukiwał. Można również wyznaczyć obszary ekranu, które często skupiają uwagę użytkownika. " + ] + }, + { + "cell_type": "markdown", + "id": "analyzed-lodging", + "metadata": {}, + "source": [ + "Dodatkowo stosuje się jeszcze jedną technikę, która jest szczególnie przydatna z punktu widzenia analizy procesu tłumaczenia. Wykonuje się pełny key logging, tj. zapisuje się każde uderzenie użytkownika w dowolny klawisz na klawiaturze wraz z precyzyjnym czasem tego uderzenia. Dane pozyskane w ten sposób pozwalają na przeprowadzenie szeregu interesujących analiz." + ] + }, + { + "cell_type": "markdown", + "id": "incredible-stress", + "metadata": {}, + "source": [ + "Zapoznajmy się najpierw z programem typu key logger:" + ] + }, + { + "cell_type": "markdown", + "id": "arctic-horror", + "metadata": {}, + "source": [ + "`sudo pip3 install keyboard`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "broken-workstation", + "metadata": {}, + "outputs": [], + "source": [ + "import keyboard\n", + "\n", + "\n", + "def report_key(event):\n", + " print(event)\n", + "\n", + "keyboard.on_release(callback=report_key)\n", + "keyboard.wait()" + ] + }, + { + "cell_type": "markdown", + "id": "polish-census", + "metadata": {}, + "source": [ + "UWAGA! Aby uruchomić powyższy kod na Linuxie konieczne są uprawnienia administratora (pytanie poza konkursem - dlaczego?)" + ] + }, + { + "cell_type": "markdown", + "id": "incoming-hands", + "metadata": {}, + "source": [ + "### Ćwiczenie 1: Wykorzystując powyższy kod napisz keylogger, który zapisuje wszystkie uderzenia w klawisze do pliku. Format pliku jest dowolny, każdy wpis musi zawierać precyzyjną godzinę uderzenia oraz uderzony klawisz. Uruchom program i przepisz paragraf dowolnie wybranego tekstu." + ] + }, + { + "cell_type": "markdown", + "id": "valuable-bearing", + "metadata": {}, + "source": [ + "Celem powyższego ćwiczenia jest pozyskanie danych testowych. Dalsze analizy będziemy prowadzili już bez key loggera, starając się korzystać jedynie z danych zapisanych w pliku. Oczywiście, jeśli zajdzie taka konieczność, można w każdej chwili wygenerować sobie nowy plik." + ] + }, + { + "cell_type": "markdown", + "id": "boxed-maple", + "metadata": {}, + "source": [ + "### Ćwiczenie 2: Napisz program, który wyliczy średnią prędkość pisania. Wykryj, kiedy użytkownik zaczął pisać. Nie bierz pod uwagę przerw dłuższych niż 5 sekund. Podaj prędkość pisania w znakach na minutę oraz słowach na minutę." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "possible-holder", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_typing_speed():\n", + " return 0" + ] + }, + { + "cell_type": "markdown", + "id": "ceramic-birth", + "metadata": {}, + "source": [ + "Wróćmy teraz do procesu tłumaczenia. Analiza uderzeń klawiszy wykonanych podczas tłumaczenia pozwala wykryć dłuższe pauzy. Pauzy te najczęściej wskazują miejsca, w których tłumacz musi się głębiej zastanowić nad tłumaczeniem danego słowa lub frazy. Przerwę tę wykorzystuje na przykład na sprawdzenie tłumaczenia lub definicji w słowniku, przeglądanie wyników z pamięci tłumaczeń lub korzystanie z innych pomocy (eye-tracking mógłby w tym przypadku rozstrzygnąć, czym w istocie zajmuje się w tym momencie tłuamcz). Jest też możliwe, że tłumacz poświęca pauzę na tzw. cognitive pause-and-unload - rodzaj zamyślenia, pozwalający oczyścić myśli. Z punktu widzenia projektowania systemu wspomagającego tłumaczenie niezwykle istotna jest informacja, nad czym tłumacz musi się dłużej zastanowić. Minimalizacja liczby i czasu trwania takich przerw jest szansą na usprawnienie procesu tłumaczenia." + ] + }, + { + "cell_type": "markdown", + "id": "great-cable", + "metadata": {}, + "source": [ + "### Ćwiczenie 3: Napisz program do wykrywania przerw w pisaniu. Raportuj długość oraz miejsce wystąpienia przerwy podając 20-znakowy kontekst z każdej strony. Wykryj każdą przerwę dłuższą niż 3 sekundy, posortuj wyniki malejąco po długości przerwy." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "close-riverside", + "metadata": {}, + "outputs": [], + "source": [ + "def find_pauses():\n", + " return []" + ] + } + ], + "metadata": { + "author": "Rafał Jaworski", + "email": "rjawor@amu.edu.pl", + "lang": "pl", + "subtitle": "12. Key logging", + "title": "Komputerowe wspomaganie tłumaczenia", + "year": "2021", + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lab/lab_13-14.ipynb b/lab/lab_13-14.ipynb new file mode 100644 index 0000000..740d7d9 --- /dev/null +++ b/lab/lab_13-14.ipynb @@ -0,0 +1,227 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ordered-wrestling", + "metadata": {}, + "source": [ + "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", + "
\n", + "

Komputerowe wspomaganie tłumaczenia

\n", + "

13,14. Korekta pisowni [laboratoria]

\n", + "

Rafał Jaworski (2021)

\n", + "
\n", + "\n", + "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "featured-afghanistan", + "metadata": {}, + "source": [ + "Współczesne programy typu CAT nie mogą obyć się bez korektora pisowni. Na bieżąco kontrolują one pisownię wyrazów po stronie docelowej, czyli tam, gdzie tłumacz wpisuje tłumaczenie. Jest to niezwykle istotne w sytuacji, gdy język docelowy nie jest dla tłumacza językiem ojczystym. Co więcej, badania wykazują, iż korekta pisowni wydatnie zmniejsza liczbę błędów w każdych scenariuszach." + ] + }, + { + "cell_type": "markdown", + "id": "seventh-genre", + "metadata": {}, + "source": [ + "Co poprawia korekta pisowni? Słowa. Tylko lub aż słowa. Program dokonujący korekty pisowni przegląda tekst słowo po słowie i sprawdza, czy należy ono do słownika. Jeśli nie, sygnalizowany jest błąd oraz, jeśli to możliwe, podawane sugestie poprawy. Co istotne, korektor pisowni nie zajmuje się szeregiem błędów, które mieszczą się w dziedzinie korekty gramatycznej, w tym:\n", + "* interpunkcją\n", + "* powtórzeniami wyrazów\n", + "* stylistyką." + ] + }, + { + "cell_type": "markdown", + "id": "sticky-society", + "metadata": {}, + "source": [ + "Aby zaimplementować korektor pisowni bez wątpienia potrzebny jest słownik. Skorzystajmy ze słownika, który znajdziemy w folderze data, pochodzącego z narzędzia Hunspell. Jest on spakowany - użyjmy techniki czytania z archiwum zip bez rozpakowywania. Poniższy kod wypisze fragment ze środka słownika." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "familiar-terrace", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "kalecząc\n", + "kaledonidy\n", + "kaledoński\n", + "kalefaktor\n", + "kalejdofon\n", + "kalejdoskop\n", + "kalejdoskopowość\n", + "kalejdoskopowy\n", + "kaleka\n", + "kaleki\n", + "kalema\n", + "kalendarium\n", + "kalendarz\n", + "kalendarzowy\n", + "kalendarzyk\n", + "kalendy\n", + "kalenica\n", + "kalenicowy\n", + "kalepin\n", + "kalesonki\n", + "kalesony\n" + ] + } + ], + "source": [ + "from zipfile import ZipFile\n", + "\n", + "with ZipFile('data/hunspell_pl.zip') as zipped_dictionary:\n", + " with zipped_dictionary.open('hunspell_pl.txt') as dictionary_file:\n", + " count = 0\n", + " for line_bytes in dictionary_file:\n", + " count += 1\n", + " if count >= 100000 and count <= 100020:\n", + " line = line_bytes.decode('utf-8')\n", + " print(line.rstrip())" + ] + }, + { + "cell_type": "markdown", + "id": "dominant-insurance", + "metadata": {}, + "source": [ + "Miejmy na uwadze, że powyższy słownik zawiera tylko formy podstawowe słowa, np. zawiera słowo \"kalendarz\", ale nie zawiera \"kalendarze\", \"kalendarza\", \"kalendarzy\" itp. " + ] + }, + { + "cell_type": "markdown", + "id": "single-brighton", + "metadata": {}, + "source": [ + "Algorytm korekty pisowni na podstawie słownika powinien działać według następujących kroków:\n", + "1. Wczytanie słownika do zbioru (set)\n", + "2. Podział tekstu do korekty na słowa (podział po spacji)\n", + "3. Dla każdego słowa wypisać, czy jest ono poprawne (znajduje się w słowniku) czy nie." + ] + }, + { + "cell_type": "markdown", + "id": "needed-watson", + "metadata": {}, + "source": [ + "### Ćwiczenie 1: Zaimplementuj podstawowy algorytm korekty pisowni według powyższych wytycznych." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "economic-southeast", + "metadata": {}, + "outputs": [], + "source": [ + "def correct_text(text):\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "endless-slide", + "metadata": {}, + "source": [ + "To jednak oczywiście nie wszystko. Do tej pory mamy funkcjonalność sygnalizowania słów błędnych, ale każdy dobry korektor pisowni potrafi podać sugestie poprawek. W tym celu musimy stawić czoła następującemu problemowi - wygenerowanie listy słów podobnych do danego słowa błędnego, które znajdują się w słowniku." + ] + }, + { + "cell_type": "markdown", + "id": "adult-freight", + "metadata": {}, + "source": [ + "W pierwszej kolejności musimy zdefiniować podobieństwo między wyrazami. Posłuży do tego dobrze nam znana odległość Levenshteina - wyrazy podobne to takie, dla których dystans Levenshteina jest niewielki (np. równy 1 lub 2). Teraz brakuje tylko algorytmu wyszukiwania wyrazów w danym słowniku, które znajdują się niedaleko (w sensie Levenshteina) danego błędnego słowa." + ] + }, + { + "cell_type": "markdown", + "id": "everyday-things", + "metadata": {}, + "source": [ + "Rozważmy następujący algorytm: dla danego słownika $D$ i błędnego słowa $w \\notin D$:\n", + "1. Wygeneruj zbiór $L_1(w)$ wszystkich słów, których odległość Levenshteina od $w$ wynosi 1.\n", + "2. Wyznacz zbiór $S_1(w)=L_1(w) \\cap D$\n", + "3. Wyznacz zbiór $L_2(w)=\\bigcup_{v \\in L_1(w)} L_1(v)$\n", + "4. Wyznacz zbiór $S_2(w)=L_2(w) \\cap D$\n", + "5. Zwróć jako listę sugestii: $S_1 \\cup S_2$" + ] + }, + { + "cell_type": "markdown", + "id": "industrial-convert", + "metadata": {}, + "source": [ + "### Ćwiczenie 2: Napisz funkcję do generowania zbioru $L_1(w)$ - wszystkich słów znajdujących się w odległości Levenshteina 1 od danego słowa w." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "built-sally", + "metadata": {}, + "outputs": [], + "source": [ + "def L1(w):\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "wireless-uncle", + "metadata": {}, + "source": [ + "### Ćwiczenie 3: Napisz funkcję do generowania sugestii poprawek dla danego słowa według opisanego wcześniej algorytmu." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "coordinated-cooperation", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_suggestions(w):\n", + " return []" + ] + } + ], + "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": "3.8.10" + }, + "subtitle": "13,14. Korekta pisowni", + "title": "Komputerowe wspomaganie tłumaczenia", + "year": "2021" + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lab/lab_15.ipynb b/lab/lab_15.ipynb new file mode 100644 index 0000000..cb0382e --- /dev/null +++ b/lab/lab_15.ipynb @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "marine-termination", + "metadata": {}, + "source": [ + "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", + "
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Komputerowe wspomaganie tłumaczenia

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15. Korekta gramatyczna [laboratoria]

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Rafał Jaworski (2021)

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\n", + "\n", + "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "featured-afghanistan", + "metadata": {}, + "source": [ + "Ostatnią z omawianych przez nas technik stosowaną podczas wspomagania tłumaczenia jest korekta gramatyczna. Automatyczna korekta gramatyczna tekstu to ambitne zadanie odnalezienia możliwych błędów niezwiązanych bezpośrednio z pisownią. Są to między innymi:\n", + "* błędy gramatyczne\n", + "* źle użyte słowa\n", + "* złe połączenia wyrazowe\n", + "* błędy interpunkcyjne\n", + "* kolokwializmy\n", + "* redundancja (np. \"tylko i wyłącznie\")" + ] + }, + { + "cell_type": "markdown", + "id": "peaceful-kingston", + "metadata": {}, + "source": [ + "Warto zwrócić uwagę, iż systemy do korekcji gramatycznej można traktować jako klasyfikatory binarne. Przyjmijmy, że odpowiedź pozytywna korektora to wykrycie błędu w tekście, natomiast negatywna - brak błędu. Wówczas rozróżniamy dwa typy pomyłek: false positive oraz false negative. False positive to tzw. \"fałszywy alarm\" - zbyt duża ich ilość spowoduje wydłużenie czasu pracy użytkownika przez konieczność analizowania zgłoszeń, które w istocie błędami nie są. Co jednak jeszcze gorsze, zbyt duża ilość false positives powoduje spadek zaufania użytkownika do systemu oraz drastyczny spadek satysfakcji z używania systemu. Te drugie błędy - false negatives - to z kolei faktyczne błędy w tekście, które nie zostały wyłapane przez system korekty. Stare polskie porzekadło głosi, że \"czego oko nie widzi, tego sercu nie żal\". Niestety jednak problem pojawia się, kiedy dostrzeże to jakieś inne oko... Wysoka liczba false negatives wprawdzie skraca czas korekty (sic!), ale odbywa się to kosztem jakości całego procesu. Idealnie zatem byłoby zminimalizować zarówno liczbę false positives, jak i false negatives. Jak jednak łatwo się domyślić, nie jest to zawsze możliwe. Korektor gramatyczny, który jest bardzo restrykcyjny i raportuje wiele błędów, będzie miał tendencję do popełniania false positives. Natomiast korektor bardziej pobłażliwy niechybnie popełni wiele false negatives. Co zatem jest ważniejsze? Praktyka wskazuje, że oba parametry mają podobną wagę, ale jednak odrobinę ważniejsze jest powstrzymanie się od false positives." + ] + }, + { + "cell_type": "markdown", + "id": "soviet-highland", + "metadata": {}, + "source": [ + "Do najbardziej popularnych narzędzi wspomagających korektę gramatyczną tekstu należą Grammarly oraz LanguageTool. Na dzisiejszych zajęciach zajmiemy się tym drugim. LanguageTool został pierwotnie napisany jako praca dyplomowa Daniela Nabera, a następnie intensywnie rozwijany wspólnie z Marcinem Miłkowskim. Aż do dziś projekt jest ciągle rozwijany, zwiększana jest liczba obsługiwanych języków oraz dokładność działania." + ] + }, + { + "cell_type": "markdown", + "id": "arbitrary-reconstruction", + "metadata": {}, + "source": [ + "LanguageTool jest systemem opartym na regułach. W dobie wszechobecnej sztucznej inteligencji opartej na uczeniu maszynowym rozwiązanie to wydaje się nieco przestarzałe. Jednak to właśnie reguły stanowią o sile LanguageToola - pozwalają one na zwiększenie precyzji korektora poprzez minimalizację false positives. Warto wspomnieć, iż liczne reguły LanguageToola dotyczą również korekty pisowni. Czyni to z LanguageToola kompletne narzędzie do korekty tekstu. Polecam przejrzenie zestawu reguł LanguageToola dla języka angielskiego: https://community.languagetool.org/rule/list?lang=en" + ] + }, + { + "cell_type": "markdown", + "id": "piano-satellite", + "metadata": {}, + "source": [ + "Czas uruchomić to narzędzie. Skorzystajmy z Pythona." + ] + }, + { + "cell_type": "raw", + "id": "academic-crest", + "metadata": {}, + "source": [ + "pip3 install language_tool_python" + ] + }, + { + "cell_type": "markdown", + "id": "italian-cheese", + "metadata": {}, + "source": [ + "Następnie możemy użyć LanguageToola w programie Pythonowym: (przykład zaczerpnięty z oficjalnego tutoriala: https://pypi.org/project/language-tool-python/)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "relative-anaheim", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Match({'ruleId': 'EN_A_VS_AN', 'message': 'Use “an” instead of ‘a’ if the following word starts with a vowel sound, e.g. ‘an article’, ‘an hour’.', 'replacements': ['an'], 'offsetInContext': 16, 'context': 'A sentence with a error in the Hitchhiker’s Guide tot he ...', 'offset': 16, 'errorLength': 1, 'category': 'MISC', 'ruleIssueType': 'misspelling', 'sentence': 'A sentence with a error in the Hitchhiker’s Guide tot he Galaxy'}),\n", + " Match({'ruleId': 'TOT_HE', 'message': 'Did you mean “to the”?', 'replacements': ['to the'], 'offsetInContext': 43, 'context': '... with a error in the Hitchhiker’s Guide tot he Galaxy', 'offset': 50, 'errorLength': 6, 'category': 'TYPOS', 'ruleIssueType': 'misspelling', 'sentence': 'A sentence with a error in the Hitchhiker’s Guide tot he Galaxy'})]\n" + ] + } + ], + "source": [ + "import language_tool_python\n", + "import pprint\n", + "tool = language_tool_python.LanguageTool('en-US') \n", + "\n", + "text = 'A sentence with a error in the Hitchhiker’s Guide tot he Galaxy'\n", + "\n", + "pp = pprint.PrettyPrinter(depth=2)\n", + "errors = tool.check(text)\n", + "pp.pprint(errors)" + ] + }, + { + "cell_type": "markdown", + "id": "gorgeous-million", + "metadata": {}, + "source": [ + "Przeanalizujmy format zwracanego wyniku. Otrzymujemy listę obiektów Match - zawiadomień o potencjalnym błędzie. Razem z każdym błędem otrzymujemy m.in. identyfikator użytej reguły, opis błędu, rekomendancję poprawy, kontekst." + ] + }, + { + "cell_type": "markdown", + "id": "reasonable-cornwall", + "metadata": {}, + "source": [ + "### Ćwiczenie 1: Użyj LanguageTool do znalezienia jak największej liczby prawdziwych błędów na swoim ulubionym portalu internetowym. Skorzystaj z poznanych wcześniej technik web scrapingu. Uwaga - LanguageTool najprawdopodobniej oznaczy nazwy własne jako literówki - ten typ błędu nie powinien być brany pod uwagę." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "sound-teaching", + "metadata": {}, + "outputs": [], + "source": [ + "def find_errors(website_url):\n", + " return []" + ] + }, + { + "cell_type": "markdown", + "id": "coupled-extra", + "metadata": {}, + "source": [ + "### Ćwiczenie 2: Napisz skrypt, który poszuka błędów w komentarzach klasy Javowej (zwykłych // oraz w javadocach). Uruchom ten skrypt na źródłach wybranego opensourcowego projektu w Javie." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "settled-armor", + "metadata": {}, + "outputs": [], + "source": [ + "def correct_java_grammar(java_file_path):\n", + " return []" + ] + } + ], + "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": "3.8.10" + }, + "subtitle": "15. Korekta gramatyczna", + "title": "Komputerowe wspomaganie tłumaczenia", + "year": "2021" + }, + "nbformat": 4, + "nbformat_minor": 5 +}