Syntax_Tree_German_Final_Version.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Syntax Tree German - Final Version.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP5YrxxQlb4Ti+Ow7gk1PNB",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jennkimerson/German_Syntax/blob/main/Syntax_Tree_German_Final_Version.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WyR6qLVXOto4"
      },
      "source": [
        "# Set up\n",
        "Import NLTK, Natural Language Toolkit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "teUxDyzPOK5e",
        "outputId": "0239182a-f1bf-4729-d3be-8688727cb3a5"
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('tagsets')\n",
        "\n",
        "from nltk import pos_tag, word_tokenize, RegexpParser"
      ],
      "execution_count": 1,
      "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 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",
            "[nltk_data] Downloading package tagsets to /root/nltk_data...\n",
            "[nltk_data]   Package tagsets is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rBQP2dRO9po"
      },
      "source": [
        "# Sentence recognition (Satzerkennung)\n",
        "Read in an input sentence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jqfLAErOar2"
      },
      "source": [
        "# Example text\n",
        "sentence = \"Es ist traurig, dass alle Menschen sterben müssen\""
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PPjYHf2PBl_"
      },
      "source": [
        "# Tokenization\n",
        "Set parsing language as German, rather than the default (English)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-wPjy9mOdt-"
      },
      "source": [
        "tokenized_sent = nltk.tokenize.word_tokenize(sentence,language='german')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPqIlsuLOyTq"
      },
      "source": [
        "# Lemmatization (Lemmatisierung)\n",
        "\n",
        "Install Hanover Tagger"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1bWysxUKN9J9",
        "outputId": "7bbe2720-dae4-4952-d3ce-d3208ec8a205"
      },
      "source": [
        "!pip install HanTa\n",
        "\n",
        "from HanTa import HanoverTagger as ht\n",
        "\n",
        "tagger = ht.HanoverTagger('morphmodel_ger.pgz')\n",
        "\n",
        "#test to see if the tagger works\n",
        "# print(tagger.analyze('Fachmärkte'))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: HanTa in /usr/local/lib/python3.7/dist-packages (0.2.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "akepZgIFPXcp"
      },
      "source": [
        "# Part-of-Speech Tagging (Wortarterkennung) \n",
        "\n",
        "Find all parts of speech in input sentence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z71rT9ZeRYGR"
      },
      "source": [
        "# Reference for Part-of-Speech Tagging\n",
        "# nltk.help.upenn_tagset()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7w1eLUO0Pt9U",
        "outputId": "f9ba112f-12e5-4ae9-b45b-3d08a4c758e9"
      },
      "source": [
        "tags = tagger.tag_sent(tokenized_sent)\n",
        "print(tags)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('Es', 'es', 'PPER'), ('ist', 'sein', 'VAFIN'), ('traurig', 'traurig', 'ADJD'), (',', '--', '$,'), ('dass', 'dass', 'KOUS'), ('alle', 'alle', 'PIAT'), ('Menschen', 'Mensch', 'NN'), ('sterben', 'sterben', 'VVINF'), ('müssen', 'müssen', 'VMINF')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYSIWqfTOplw",
        "outputId": "bb7aa712-29d6-4b28-a1b2-570b90c1859c"
      },
      "source": [
        "#trim data\n",
        "pos_tags = [tag[1:] for tag in tags if tag[2] != '$,']\n",
        "print(pos_tags)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('es', 'PPER'), ('sein', 'VAFIN'), ('traurig', 'ADJD'), ('dass', 'KOUS'), ('alle', 'PIAT'), ('Mensch', 'NN'), ('sterben', 'VVINF'), ('müssen', 'VMINF')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0o0PCESSQPX0"
      },
      "source": [
        "Construct the grammatical rules of German (manually)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZ7G1W9lQN-A"
      },
      "source": [
        "chunker = RegexpParser(\"\"\"\n",
        "                       S: {<NP> <VP>}\n",
        "                       S: {<S> <CC>}\n",
        "                       C:{<KOUS|PWS|PRELS>}\n",
        "                       NP: {<DT>?<JJ|PIAT>*<NN|PPOSAT>*} #To extract Noun Phrases\n",
        "                       NP: {<PPER|PDS|NE>}\n",
        "                       P: {<IN>}                        #To extract Prepositions\n",
        "                       V: {<V.*>}                       #To extract Verbs\n",
        "                       PP: {<P|APPR> <NP>}              #To extract Prepostional Phrases\n",
        "                       VP: {<V> <NP|PP|V|ADVP|ADJD>*}   #To extarct Verb Phrases\n",
        "                       ADVP: {<ADV> <ADV|ADJD>}         #To extract Adverbil Phrases\n",
        "                       IP: {<NP|,>+<VP>?}\n",
        "                       CC:{<C|,>+<IP>?}\n",
        "                       \"\"\")"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbMQlW3-QWSR",
        "outputId": "c2953132-d084-4c59-b880-4e01f59139ce"
      },
      "source": [
        "output = chunker.parse(pos_tags)\n",
        "print(output)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(S\n",
            "  (IP (NP es/PPER) (VP (V sein/VAFIN) traurig/ADJD))\n",
            "  (CC\n",
            "    (C dass/KOUS)\n",
            "    (IP\n",
            "      (NP alle/PIAT Mensch/NN)\n",
            "      (VP (V sterben/VVINF) (V müssen/VMINF)))))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nAO3I7YcQclz"
      },
      "source": [
        "# Construct Syntax Tree\n",
        "\n",
        "Setup:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r29T0C1-QcO7",
        "outputId": "80ddf923-14c8-4480-b44a-992cb6d36ea3"
      },
      "source": [
        "!apt-get install -y xvfb # Install X Virtual Frame Buffer\n",
        "import os\n",
        "os.system('Xvfb :1 -screen 0 1600x1200x16  &')    # create virtual display with size 1600x1200 and 16 bit color. Color can be changed to 24 or 8\n",
        "os.environ['DISPLAY']=':1.0'    # tell X clients to use our virtual DISPLAY :1.0."
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "xvfb is already the newest version (2:1.19.6-1ubuntu4.9).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 34 not upgraded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8BtD3DoQicb"
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n39UpOjAQjAC",
        "outputId": "edf4efda-d6b7-430e-88a7-0b2f6f3f90d0"
      },
      "source": [
        "!apt install ghostscript python3-tk"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "ghostscript is already the newest version (9.26~dfsg+0-0ubuntu0.18.04.14).\n",
            "python3-tk is already the newest version (3.6.9-1~18.04).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 34 not upgraded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6DtbsVtQk5x"
      },
      "source": [
        "Draw Syntax Tree:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "TLans5F_QkUa",
        "outputId": "042fd8a4-8729-4de5-b33d-43aa8d74cbae"
      },
      "source": [
        "from nltk.tree import Tree\n",
        "from IPython.display import display\n",
        "\n",
        "tree = Tree.fromstring(str(output))\n",
        "display(tree)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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",
            "text/plain": [
              "Tree('S', [Tree('IP', [Tree('NP', ['es/PPER']), Tree('VP', [Tree('V', ['sein/VAFIN']), 'traurig/ADJD'])]), Tree('CC', [Tree('C', ['dass/KOUS']), Tree('IP', [Tree('NP', ['alle/PIAT', 'Mensch/NN']), Tree('VP', [Tree('V', ['sterben/VVINF']), Tree('V', ['müssen/VMINF'])])])])])"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQ_1e_eTVG_j"
      },
      "source": [
        "# Citation:\n",
        "\n",
        "https://textmining.wp.hs-hannover.de/Preprocessing.html\n",
        "\n",
        "https://theclassicjournal.uga.edu/index.php/2020/10/29/under-the-surface/\n",
        "\n",
        "https://github.com/Simon-Will/montesniere"
      ]
    }
  ]
}