7_Lematization.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "7. Lematization.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyP4IgOqCmUvUXHK1pN1XdTf"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "68plQWJOWVdW",
        "colab_type": "text"
      },
      "source": [
        "**Difference between Stemming and Lemmatisation**\n",
        "\n",
        "*   A stemmer operates on a single word without knowledge of the context, and therefore cannot discriminate between words which have different meanings depending on part of speech.\n",
        "*   While converting any word to the root/base word, stemming can create non-existent work but lemmatization creates actual dictionary words.\n",
        "*   Stemmers are typically easier to implement than Lemmatizers.\n",
        "*   Stemmers run faster than Lemmatizers.\n",
        "*   The accuracy of stemming is less than that of lemmatization.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqw8vgqAZPX6",
        "colab_type": "text"
      },
      "source": [
        "![alt text](https://miro.medium.com/max/585/1*uVgEZI7UFLMjHqemI_MzGA.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hL3RZmjlc2TT",
        "colab_type": "text"
      },
      "source": [
        "# Menggunakan NLTK"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fKDwXlGUtfx",
        "colab_type": "code",
        "outputId": "5b4614b4-e7ef-444c-a0ea-03735c899329",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "\n",
        "from nltk.tokenize import word_tokenize \n",
        "from nltk.stem import WordNetLemmatizer"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lVMNueRQU4Ch",
        "colab_type": "code",
        "outputId": "028bd6b8-31c0-4d83-a4a6-99456fcc5da1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "wordnet_lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "word_data = \"It originated from the idea that there are readers who prefer learning new skills from the comforts of their drawing rooms\"\n",
        "nltk_tokens = nltk.word_tokenize(word_data)\n",
        "for w in nltk_tokens:\n",
        "       print (\"Actual: %s  Lemma: %s\"  % (w,wordnet_lemmatizer.lemmatize(w)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Actual: It  Lemma: It\n",
            "Actual: originated  Lemma: originated\n",
            "Actual: from  Lemma: from\n",
            "Actual: the  Lemma: the\n",
            "Actual: idea  Lemma: idea\n",
            "Actual: that  Lemma: that\n",
            "Actual: there  Lemma: there\n",
            "Actual: are  Lemma: are\n",
            "Actual: readers  Lemma: reader\n",
            "Actual: who  Lemma: who\n",
            "Actual: prefer  Lemma: prefer\n",
            "Actual: learning  Lemma: learning\n",
            "Actual: new  Lemma: new\n",
            "Actual: skills  Lemma: skill\n",
            "Actual: from  Lemma: from\n",
            "Actual: the  Lemma: the\n",
            "Actual: comforts  Lemma: comfort\n",
            "Actual: of  Lemma: of\n",
            "Actual: their  Lemma: their\n",
            "Actual: drawing  Lemma: drawing\n",
            "Actual: rooms  Lemma: room\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NocT1tdxW0sX",
        "colab_type": "code",
        "outputId": "7ec282f9-360e-4ad3-8cc8-23b96240e13a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "text = \"It originated from the idea that there are readers who prefer learning new skills from the comforts of their drawing rooms\"\n",
        "text = text.lower()\n",
        " \n",
        "words = word_tokenize(text)\n",
        "print(\"Tokenization :\", words)\n",
        " \n",
        "lemmatizer = WordNetLemmatizer()\n",
        "words_lemma = [lemmatizer.lemmatize(word) for word in words]\n",
        "print(\"Lematisation :\", words_lemma)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tokenization : ['it', 'originated', 'from', 'the', 'idea', 'that', 'there', 'are', 'readers', 'who', 'prefer', 'learning', 'new', 'skills', 'from', 'the', 'comforts', 'of', 'their', 'drawing', 'rooms']\n",
            "Lematisation : ['it', 'originated', 'from', 'the', 'idea', 'that', 'there', 'are', 'reader', 'who', 'prefer', 'learning', 'new', 'skill', 'from', 'the', 'comfort', 'of', 'their', 'drawing', 'room']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pyeiopp8aEyv",
        "colab_type": "code",
        "outputId": "08b8ed76-415a-4ee0-aa8f-e54a2f3d4cb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "from nltk.corpus import wordnet as wn\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "from nltk import word_tokenize, pos_tag\n",
        "from collections import defaultdict\n",
        "nltk.download('averaged_perceptron_tagger')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukmhs3vjaXYt",
        "colab_type": "code",
        "outputId": "2dc71b9f-f438-46e5-8ff9-5918b186a6b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "tag_map = defaultdict(lambda : wn.NOUN)\n",
        "tag_map['J'] = wn.ADJ\n",
        "tag_map['V'] = wn.VERB\n",
        "tag_map['R'] = wn.ADV\n",
        "\n",
        "text = \"lemmatizer minimizes text ambiguity. Example words like bicycle or bicycles are converted to base word bicycle\"\n",
        "tokens = word_tokenize(text)\n",
        "lemma_function = WordNetLemmatizer()\n",
        "\n",
        "for token, tag in pos_tag(tokens):\n",
        "\tlemma = lemma_function.lemmatize(token, tag_map[tag[0]])\n",
        "\tprint(token, \"=>\", lemma)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "lemmatizer => lemmatizer\n",
            "minimizes => minimizes\n",
            "text => text\n",
            "ambiguity => ambiguity\n",
            ". => .\n",
            "Example => Example\n",
            "words => word\n",
            "like => like\n",
            "bicycle => bicycle\n",
            "or => or\n",
            "bicycles => bicycle\n",
            "are => be\n",
            "converted => convert\n",
            "to => to\n",
            "base => base\n",
            "word => word\n",
            "bicycle => bicycle\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1zyGq_Nc6Rz",
        "colab_type": "text"
      },
      "source": [
        "# Menggunakan Spacy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVZHqY-Na0EY",
        "colab_type": "code",
        "outputId": "e5dab120-2429-421b-b93a-3b5e21b122a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "pip install spacy"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: spacy in /usr/local/lib/python3.6/dist-packages (2.2.4)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.2)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (7.4.0)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.0.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy) (45.2.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (3.0.2)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.18.1)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (0.4.1)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.0)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.2)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.1.3)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (0.6.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (4.43.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.21.0)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy) (1.5.0)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2019.11.28)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (1.24.3)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.0.4)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy) (3.1.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOBHMyIFa0_F",
        "colab_type": "code",
        "outputId": "07f11e98-4a53-4f69-afaf-cd978c0e0134",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "import spacy\n",
        "\n",
        "sp = spacy.load('en_core_web_sm')\n",
        "kalimat = 'She had been with her father and sister when she was attacked and received first aid at the scene, an official said'\n",
        "lemma_sent = sp(kalimat)\n",
        "for word in lemma_sent:\n",
        "  print(word.text + '  ===>', word.lemma_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "She  ===> -PRON-\n",
            "had  ===> have\n",
            "been  ===> be\n",
            "with  ===> with\n",
            "her  ===> -PRON-\n",
            "father  ===> father\n",
            "and  ===> and\n",
            "sister  ===> sister\n",
            "when  ===> when\n",
            "she  ===> -PRON-\n",
            "was  ===> be\n",
            "attacked  ===> attack\n",
            "and  ===> and\n",
            "received  ===> receive\n",
            "first  ===> first\n",
            "aid  ===> aid\n",
            "at  ===> at\n",
            "the  ===> the\n",
            "scene  ===> scene\n",
            ",  ===> ,\n",
            "an  ===> an\n",
            "official  ===> official\n",
            "said  ===> say\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mS-oRZmJn45T",
        "colab_type": "code",
        "outputId": "5e3fa373-0c43-4e07-fd52-7464923a9ba5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "source": [
        "from spacy import displacy\n",
        "\n",
        "nlp = spacy.load('en_core_web_sm')\n",
        "about_interest_text = 'He is interested in learning, Natural Language Processing.'\n",
        "about_interest_doc = nlp(about_interest_text)\n",
        "displacy.render(about_interest_doc, style='dep', jupyter=True)"
      ],
      "execution_count": 0,
      "outputs": [
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