KWT-2024/lab/lab_03.ipynb

{
 "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",
    "<div class=\"alert alert-block alert-info\">\n",
    "<h1> Komputerowe wspomaganie tłumaczenia </h1>\n",
    "<h2> 3. <i>Terminologia</i> [laboratoria]</h2> \n",
    "<h3>Rafał Jaworski (2021)</h3>\n",
    "</div>\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": [
    "```python\n",
    "import collections\n",
    "lista1 = [3,4,5,4,4,7,8,7]\n",
    "lista2 = [3,4,5,4,4,7,8,7]\n",
    "print((collections.Counter(lista) + collections.Counter(lista2)).most_common(5))\n",
    "```\n",
    "\n",
    "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:\n",
    "- **DeepL:** metal cabinet slides / metal cabinet guides\n",
    "- **Model GPT-3.5:** metal cabinet slides / metal wardrobe rails.\n",
    "- **Model GPT-4:** guides for metal cabinets / metal cabinet guides\n",
    "- **Google-translate**: metal cabinet guides\n",
    "- **www.tlumaczangielskopolski.pl:** metal cabinet guides\n"
   ]
  },
  {
   "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": 70,
   "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": 71,
   "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": 76,
   "id": "cognitive-cedar",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'applet': [(302, 308)],\n",
      " 'application': [(80, 91), (153, 84), (300, 158)],\n",
      " 'compile': [(56, 63), (497, 448)],\n",
      " 'program': [(14, 21), (284, 277), (454, 177), (495, 48), (505, 17)]}\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "from pprint import pprint\n",
    "\n",
    "def terminology_lookup():\n",
    "    answer = {pattern:[] for pattern in dictionary}\n",
    "    low_text = text.lower()\n",
    "    for pattern in dictionary:\n",
    "        offset = 0\n",
    "        start = 0\n",
    "        end = 0\n",
    "        while True:\n",
    "            match = (re.search(pattern,low_text[offset:]))\n",
    "            if not match:\n",
    "                break\n",
    "            else:\n",
    "                start += match.start()\n",
    "                end = +match.end()\n",
    "                offset += end\n",
    "\n",
    "                answer[pattern].append((start,end))\n",
    "    pprint(answer)\n",
    "    #return answer\n",
    "\n",
    "terminology_lookup()"
   ]
  },
  {
   "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": 1,
   "id": "02e1c16f-be37-4a64-a514-8875b393ccb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: spacy in /usr/local/lib/python3.9/dist-packages (3.4.1)\n",
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      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from spacy) (3.1.2)\n",
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      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from spacy) (21.3)\n",
      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from spacy) (3.3.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/lib/python3/dist-packages (from packaging>=20.0->spacy) (2.4.7)\n",
      "Requirement already satisfied: smart-open<6.0.0,>=5.2.1 in /usr/local/lib/python3.9/dist-packages (from pathy>=0.3.5->spacy) (5.2.1)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<1.10.0,>=1.7.4->spacy) (4.3.0)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.1.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (1.26.12)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2022.9.14)\n",
      "Requirement already satisfied: blis<0.10.0,>=0.7.8 in /usr/local/lib/python3.9/dist-packages (from thinc<8.2.0,>=8.1.0->spacy) (0.9.1)\n",
      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.9/dist-packages (from thinc<8.2.0,>=8.1.0->spacy) (0.0.1)\n",
      "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.9/dist-packages (from typer<0.5.0,>=0.3.0->spacy) (8.1.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->spacy) (2.1.1)\n"
     ]
    }
   ],
   "source": [
    "pip3 install spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6d7e9f5-4d6f-49c5-8dea-9957bc6da318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "\u001b[33mDEPRECATION: https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.1/en_core_web_sm-3.4.1-py3-none-any.whl#egg=en_core_web_sm==3.4.1 contains an egg fragment with a non-PEP 508 name pip 25.0 will enforce this behaviour change. A possible replacement is to use the req @ url syntax, and remove the egg fragment. Discussion can be found at https://github.com/pypa/pip/issues/11617\u001b[0m\u001b[33m\n",
      "\u001b[0mCollecting en-core-web-sm==3.4.1\n",
      "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.1/en_core_web_sm-3.4.1-py3-none-any.whl (12.8 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m45.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m0:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: spacy<3.5.0,>=3.4.0 in /usr/local/lib/python3.9/dist-packages (from en-core-web-sm==3.4.1) (3.4.1)\n",
      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.9 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (3.0.10)\n",
      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (1.0.3)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (1.0.8)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.0.6)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (3.0.7)\n",
      "Requirement already satisfied: thinc<8.2.0,>=8.1.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (8.1.1)\n",
      "Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (0.10.1)\n",
      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.4.4)\n",
      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.0.8)\n",
      "Requirement already satisfied: typer<0.5.0,>=0.3.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (0.4.2)\n",
      "Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (0.6.2)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (4.64.1)\n",
      "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (1.21.6)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.28.1)\n",
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      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (3.1.2)\n",
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      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (3.3.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/lib/python3/dist-packages (from packaging>=20.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.4.7)\n",
      "Requirement already satisfied: smart-open<6.0.0,>=5.2.1 in /usr/local/lib/python3.9/dist-packages (from pathy>=0.3.5->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (5.2.1)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from pydantic!=1.8,!=1.8.1,<1.10.0,>=1.7.4->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (4.3.0)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.1.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (1.26.12)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2022.9.14)\n",
      "Requirement already satisfied: blis<0.10.0,>=0.7.8 in /usr/local/lib/python3.9/dist-packages (from thinc<8.2.0,>=8.1.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (0.9.1)\n",
      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.9/dist-packages (from thinc<8.2.0,>=8.1.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (0.0.1)\n",
      "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.9/dist-packages (from typer<0.5.0,>=0.3.0->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (8.1.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->spacy<3.5.0,>=3.4.0->en-core-web-sm==3.4.1) (2.1.1)\n",
      "Installing collected packages: en-core-web-sm\n",
      "Successfully installed en-core-web-sm-3.4.1\n",
      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
      "You can now load the package via spacy.load('en_core_web_sm')\n"
     ]
    }
   ],
   "source": [
    "python3 -m spacy download en_core_web_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
      "you\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",
      "you\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": 20,
   "id": "surgical-demonstration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'program': [(14, 24), (291, 298), (468, 475), (516, 523), (533, 540)],\n",
       " 'application': [(80, 91), (164, 175), (322, 333)],\n",
       " 'applet': [(302, 308)],\n",
       " 'compile': [(56, 63), (134, 141), (504, 511)]}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "def terminology_lookup():\n",
    "    answer = {pattern:[] for pattern in dictionary}\n",
    "\n",
    "    for pattern in dictionary:\n",
    "        for token in doc:\n",
    "            if pattern in token.lemma_:\n",
    "                answer[pattern].append((token.idx,token.idx+len(token.lemma_)))\n",
    "    return answer\n",
    "\n",
    "terminology_lookup()"
   ]
  },
  {
   "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": 73,
   "id": "superb-butterfly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['programmers',\n",
       " 'section',\n",
       " 'Swing',\n",
       " 'application',\n",
       " 'command',\n",
       " 'line',\n",
       " 'information',\n",
       " 'Swing',\n",
       " 'application',\n",
       " 'compilation',\n",
       " 'instructions',\n",
       " 'Swing',\n",
       " 'programs',\n",
       " 'applets',\n",
       " 'applications',\n",
       " 'steps',\n",
       " 'release',\n",
       " 'platform',\n",
       " 'program',\n",
       " 'Swing',\n",
       " 'components',\n",
       " 'program',\n",
       " 'program']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import spacy\n",
    "def get_nouns(text):\n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    doc = nlp(text)\n",
    "    nouns = [token.text for token in doc if token.pos_ == \"NOUN\"]\n",
    "    return nouns\n",
    "\n",
    "get_nouns(text)"
   ]
  },
  {
   "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": 74,
   "id": "eight-redhead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'programmer': 1,\n",
       " 'section': 1,\n",
       " 'swing': 4,\n",
       " 'application': 3,\n",
       " 'command': 1,\n",
       " 'line': 1,\n",
       " 'information': 1,\n",
       " 'compilation': 1,\n",
       " 'instruction': 1,\n",
       " 'program': 4,\n",
       " 'applet': 1,\n",
       " 'step': 1,\n",
       " 'release': 1,\n",
       " 'platform': 1,\n",
       " 'component': 1}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "import spacy\n",
    "\n",
    "def extract_terms(text):\n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    doc = nlp(text)\n",
    "    tally = {}\n",
    "    nouns = [token.lemma_ for token in doc if token.pos_ == \"NOUN\"]\n",
    "    nouns_counts = Counter(nouns)\n",
    "   \n",
    "    for word, count in nouns_counts.items():\n",
    "        tally.update({word: count})\n",
    "    return tally\n",
    "\n",
    "extract_terms(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loaded-smell",
   "metadata": {},
   "source": [
    "### Ćwiczenie 6: Rozszerz powyższy program o ekstrację czasowników i przymiotników."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "monetary-mambo",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'adjectives': {'late': 1},\n",
      " 'nouns': {'applet': 1,\n",
      "           'application': 3,\n",
      "           'command': 1,\n",
      "           'compilation': 1,\n",
      "           'component': 1,\n",
      "           'information': 1,\n",
      "           'instruction': 1,\n",
      "           'line': 1,\n",
      "           'platform': 1,\n",
      "           'program': 4,\n",
      "           'programmer': 1,\n",
      "           'release': 1,\n",
      "           'section': 1,\n",
      "           'step': 1,\n",
      "           'swing': 4},\n",
      " 'verbs': {'compile': 3,\n",
      "           'create': 1,\n",
      "           'do': 1,\n",
      "           'explain': 1,\n",
      "           'follow': 1,\n",
      "           'install': 1,\n",
      "           'need': 1,\n",
      "           'run': 3,\n",
      "           'see': 1,\n",
      "           'use': 2,\n",
      "           'work': 1}}\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "from collections import Counter\n",
    "import spacy\n",
    "\n",
    "def extract_terms(text):\n",
    "    \n",
    "    nlp = spacy.load(\"en_core_web_sm\")\n",
    "    doc = nlp(text)\n",
    "    \n",
    "    nouns, verbs, adjectives = [], [], []\n",
    "    tally = {\"nouns\": {}, \"verbs\": {}, \"adjectives\": {}}\n",
    "    \n",
    "    for token in doc:\n",
    "        if token.pos_ == \"NOUN\":\n",
    "            nouns.append(token.lemma_)\n",
    "        elif token.pos_ == \"VERB\":\n",
    "            verbs.append(token.lemma_)\n",
    "        elif token.pos_ == \"ADJ\":\n",
    "            adjectives.append(token.lemma_)\n",
    "    \n",
    "    nouns_counts = Counter(nouns)\n",
    "    verbs_counts = Counter(verbs)\n",
    "    adjectives_counts = Counter(adjectives)\n",
    "\n",
    "    for word, count in nouns_counts.items():\n",
    "         tally[\"nouns\"].update({word: count})\n",
    " \n",
    "    for word, count in verbs_counts.items():\n",
    "         tally[\"verbs\"].update({word: count})\n",
    "      \n",
    "    for word, count in adjectives_counts.items():\n",
    "         tally[\"adjectives\"].update({word: count})\n",
    "\n",
    "    pprint(tally)\n",
    "    #return tally\n",
    "\n",
    "extract_terms(text)"
   ]
  }
 ],
 "metadata": {
  "author": "Rafał Jaworski",
  "email": "rjawor@amu.edu.pl",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.2"
  },
  "subtitle": "3. Terminologia",
  "title": "Komputerowe wspomaganie tłumaczenia",
  "year": "2021"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}