Syntax_Tree_German_Demo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Syntax Tree German - Demo.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNzlRsktlG/HHHUO6340PPn",
      "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_Demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-UEKa0uezM4",
        "outputId": "243231da-65e3-4d30-8580-50c1e8994534"
      },
      "source": [
        "# import nltk settings\n",
        "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\n",
        "\n",
        "!pip install HanTa\n",
        "\n",
        "from HanTa import HanoverTagger as ht"
      ],
      "execution_count": 48,
      "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",
            "Requirement already satisfied: HanTa in /usr/local/lib/python3.7/dist-packages (0.2.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89lwRZlzfHvF",
        "outputId": "af30a238-867d-49bf-f137-6b26e550013f"
      },
      "source": [
        "# import tree visualization setup\n",
        "!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.\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "!apt install ghostscript python3-tk"
      ],
      "execution_count": 49,
      "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",
            "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": "ZQE6FuyfgdKA"
      },
      "source": [
        "# Example sentences to demo:\n",
        "\n",
        "Ich habe keine Ahnung, was du sagst\n",
        "\n",
        "Mein Mutter sind sehr nett"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yxdtdJsxe7U1",
        "outputId": "c7c33d96-c255-451d-8890-f0b365abbf38"
      },
      "source": [
        "# input sentence\n",
        "sentence = input(\"Enter your sentence: \")\n",
        "print(sentence)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter your sentence: Ich habe keine Ahnung, was du sagst\n",
            "Ich habe keine Ahnung, was du sagst\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "1n445_Dte-QT",
        "outputId": "012337d3-8c5d-4e6a-8c55-74da6bcb0c53"
      },
      "source": [
        "# tokenization\n",
        "tokenized_sent = nltk.tokenize.word_tokenize(sentence,language='german')\n",
        "\n",
        "# lemmatization\n",
        "tagger = ht.HanoverTagger('morphmodel_ger.pgz')\n",
        "\n",
        "# part of speech tagging\n",
        "tags = tagger.tag_sent(tokenized_sent)\n",
        "pos_tags = [tag[1:] for tag in tags if tag[2] != '$,']\n",
        "print(pos_tags)\n",
        "\n",
        "# grammatical rules\n",
        "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",
        "                       \"\"\")\n",
        "\n",
        "output = chunker.parse(pos_tags)\n",
        "print(output)\n",
        "\n",
        "from nltk.tree import Tree\n",
        "from IPython.display import display\n",
        "\n",
        "tree = Tree.fromstring(str(output))\n",
        "display(tree)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('ich', 'PPER'), ('haben', 'VAFIN'), ('keine', 'PIAT'), ('Ahnung', 'NN'), ('was', 'PRELS'), ('du', 'PPER'), ('sagen', 'VVFIN')]\n",
            "(S\n",
            "  (IP (NP ich/PPER) (VP (V haben/VAFIN) (NP keine/PIAT Ahnung/NN)))\n",
            "  (CC (C was/PRELS) (IP (NP du/PPER) (VP (V sagen/VVFIN)))))\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BAwAAZI7AAQAAMkfgKIRwNApHo7xLAQAr+p+Xl9FkkncpUGjcaTQf4WgUjsfDrybhaPzZ5WWyvXl8rB1Vah8capWKdnSUXwEB4A7haOQPLoPRyB9c/uHbb2/+8IfqwYFerzeqFb1eVw8P8y4gioXLYjdkecL403/xw3+8+ae//+Yfw/H49+Px1P+SPwAURDpkfHNzI4SQIaNy8PT//NP/TfdvhA9MIXBk5TFjGP5gEI7Gw68m5A8AuVsUMhaFCX8w8L4YED4whcCxNtFkEo7GwWg0lTBOKhWtUql9cKjXj1dLCcvzR+PoSDuqcAwDWKNoMnHfBumQ8WRvT68fN46OHtSVLQkfRqOh7O9nVH4UEIFjdemEEY7H8pgUqYShHVX0en3t7zs3fzzZ29MqFfIHgJVFk4k/GARfjv3B4MuvvxarhoxFZsPHSaWS7J/wsfUIHA+QV8JYjvwBYGVZh4xFCB87iMCxTDETxnLkDwDLzYYMIcRZQ8s6ZCxC+NgRBI7via+vw/HY+2IQTSbJzKUQonpwIM/TrQ/rWqVSogOA/AFACBFfX/uDS++LQTpkyHVgrQ/rxfngRPjYYrseONIJIxyNk+OwvAljuSR/pDsd8gewlWTIkAs/k88bBQwZi8jwkS484aPUdi5w7FrCWGJRVZA/gPIqe8iYa+4vRfgone0PHEtOq3r9eKcSxnLkD6CklpyPyxsyFiF8lNd2Bg4WLjweQ0FAwTHjsFNJawtsSeAgYWSN/AEUAWsqF9nKuaQtU9bAQcLIF/kD2BhCxkMRPoqpNIFD3syf23sXE/kDWC/uCL4uhI/iKG7g4Avcy4v8Aazgod+RhocifOSrQIGDhLGtkoN86oat5A+AkJGXubdbJXxkKs/AQcLYTYtuGE/+wI4gZBQN4WMzcggc9v/41PtisN4vcEd5Lckfer1+8ZMX9L/YDtFkYrmvHvlt78jaovBx8ZMXJI9HyiFwGP/5v0STrwv75WfI11T+8P/qL+iLsR3i62vV+neEjBJJhw/bODOeNfIuUbkVaA0HAADYVj/IuwAAAGD7ETgAAEDmCBwAACBzBA4AAJA5AgcAAMgcgQMANiGKoryLAOQpw8BhWZaiKL7vCyFc19U0Tdd1+VhVVfmjYRj9fj+7MqAsDMNQVdWyLCFEFEW6rquq6vs+rQVbIAxDXdd7vZ5pmpZlyXaOwrJtW3ZBstsRnLbW5TZLnU7n7OxMPvY8z/O8ZHv68XA4zLQYKIWkqdze3vZ6Pcdx5GNaC8qu2WxeXV3Jx57ndTqdfMuD+5j6M9ERPV7mUyrtdvvOOB/HcdbFQPE1Go0wDOXjIAjkB4tZtBaUi+/7rVZLURT5o67rFxcX+RYJj0dHtIL3s34DXdcdx5mdvHQcx/O8OI4VRdE0LetioPgMw+h2u/1+P4qipHeWaC0otalGO9W8URZ0RI+UeeAQQti2bZpmu91Ob6zVapqmaZrGsQdJVVUhRBzHvV6P1oJtItdw5F0KPBYd0SNt4ioVRVFarZbneemNcvUNfzaktVot13XjOJbhI0FrQXnpuh4EQTICH8cxSw5Lio7okTL88jbXdS3L0jTNdV3xbuZS13W5XVEURVFc1+WPhzRVVfv9fvJxkNaCLRBFUbfbla03juN2u82AfJHZtu15XhRFqqrKnoeOaC34tlgA2AS5JpqogZ1F4AAAAJnjTqMAACBzBA4AAJA5AgcAAMgcgQMAAGSOwAEAADK3iTuNpkWTSTSZ6PX6ht8XZRFfX/uDy2A0CkfjaDIRQuj1eu2DQ71+rB0d5V06YHX+YKAeHqqHh3kXBA8Qjka9z9/897/7u3/+/vvtj06NZw3+givb9GWxlvvql5/+9va//ddNvikKLppM/MFg+NXEH1z+fjyWG08qlT/94Q//37ffhuPxNzc3Qogne3tapaIdVVof1rVKRdnfz7XUwMO897Ofd158bBtneRcEd4smE/dt0Pv89Zdffy2E+Nf/6l/G19f/63//vRCieXz88kcNo9GgC3qoTY9wAFI4GiUjGfKQFkI0j487Lz5uHB3p9eP0wRyORuF4HHw59geDzy4vf/npb4UQJ5WKXj+ufXCo1+t85gDwePH1tRsEzu+Czy4vhRAnlUp6VCMcjZy3Qf/1m88uLy33E6PRePmjBgP290fgwOb4g4H3xSAcjeXBLIR4sren14+NZ43Wh/Ulx612dKQdHZmnQnx/zkUmDyFE9eBAO6rIpMLMC4CHct8G3heDX715I4SoHhx0Xnz88lljqjORHZFtnCVP/tWbN9WDA+NZo/3RKR977sSUCjIUTSbhaByMRum5kurBgV6vN6oVrVJ5fDiQISaaTPzBpZx5EUI0j4+T/MGwJwqCKZUCSgYtZO9x/vx568O68axxn9fK4ZDe529k53ZSqbQ/es5UyxIEDqyZnCsZfjXxB4NkrkROf2SdAORakODLcTgep9eCaJVKo1ph5gX5InAUh1yi4bwNZEfxyGUZ0WTS+/y1+zaQPd6DUstOIXBgDWaHGdILPHOZ40zPvEzN4DSOjrSjCjOv2DACR+7kmIT3xeBVEAohqgcH673wxB8MnN8FbhB8c3PzZG/PPH0+Oy+zywgcWMXc07mcKynmJaz+YCAnd6bWqDLzgo0hcORIrrrYTBSYXXn68lnDPH1OJ0PgwH0lExazcyXlulRk7lW4611ZAswicGze1GTHWUN7+ezZxiY7pq6tPWtorQ/r5unpZt69gAgcWCYZGJhdkrkdN8OIr6/D8VheO5O+4YdeP1YPD/OaD8JWInBsTHx93X/9JlmikftyTnn3sGR8xWg02h8938EPNgQOfM/ypQ8FnCtZr+UrXrWjSllGcVBABI4N6L9+nV6iUbQLVqeKt2u3LuU+HFh4o8/z58937eIOeZ29fJy+pjd9w4/CrlMBdtbUas3z58+LOYRgnp6ap6fJAMzFq08uXn2yO7cuJXDsqAfd6HM3ya+9SKZ7kytxZKcmN27T7BJQOrNLNEqxSELZ37d+/ML68Ytdu3UpUyo7ZNGNPlms8FDpW62Xev0sNokplXWZveNW2S8DmbrPadFmgtaFwLHNNnCjTyy6QlhecMsNP5AgcDye+zZw3r4t7BKNR9r6W5cSOLZNjjf6hFh6XQ/1v+MIHCubvcpju6cetvXWpazh2AaLbvR555eiYe30+h8rPH2r9WTZaXKrdUaYgOXm3sdimz7xL6IeHtrGmW2cJYthf/XmzZNfl/7WpYxwlFLpbvSJ9A0/WEOzmxjhuKe5d+rcqctHp2zNrUsJHKUh2xwLFbfDoquE5MzLFoydYhaB407yA3167WSpP9Cv3eyQzyZvnPp4mw4ccvCfQ2417/3s54JLMbfO1H1QmsfH/l//Zd6FwvpZ7iuGspaTn0i3ZslCdpJFLXr92P23/ybv4tzXpgMHHiMcjQj7W4+/MnZWfH0thOBz1P1Fk0mJxrYJHAAAIHM/yLsAAABg+xE4AABA5ggcAAAgc5sLHFEUbey9ACB3dHpA2t2Bw7KsO5/j+75lWcuPrm63+4ByAUDJbazTI9mgFNYzwqFpWhzHSxq97/uKosjHruuqqqppmq7rhmH0+/1FG6e267quaZrv+1MbkydvH8MwVFWVmS+KIl3XVVX1fT/vcmENLMtSFEX+NV3Xle150YGA0kk6vXt2brM9YdLjiXeHv6Iouq6newC5vdfrmaapaVoev+iKaP8Pteh0UKZKu13s6urK8zzP82a3O47jed7V1dXV1ZXc2Ol0vHdmd3V2dpY8M3ly8ng4HC7aOLU92f+iJ2+fs7Oz5HGv13McJ8fCYL06nU7y993Btl0ow+HQ87yktqe6siAIZKc39ZKkJ5zdYbrTu0/nNrcnnCpGs9mcepfz8/P03h76W+eL9v9Qi04HZam0ZSMcURR5njc1Kui6rkxYcRzrup7+3263K8c5DMNIv8T3fVVVkxGOWXEc37kxDMN+vy9D3Gw5l/wWZddoNMIwlI+DIJiqW5Rdu91ePms59+jA2oVhKHsw+aPjOMmPruvK4YooipI/luwJ5UZVVaf2trzTm/s3nerHlvR4abVaLRnwsG377t+zYGj/D3LP00FhK23Zt8VqmqZp2lRr6PV6SfvWNC355YUQFxcX8thwHCf9EsdxZo8E+ckgjmNFUZKRwLkbxbt1JHJEMb1RUZQ4ji8uLmYP+K1hGEa32+33+1EULQltKCld1x3HmTrZLDoQkB3DMDzPk51ev99vtVpCCHnEGYYRRVEURZqmDYdD+fw4jmWkME1ztv+Z7fSWdG6z/djcHm8uy7L6/b4cbK/VaqZpPqoWNo72/yBLTgelqLRHfT29qqp3nullvcweNrVaTR7b6f+au1EIYdu2qqqyUYZhKGvTtm1d123bLmyaWwtZw3Ec93q9drudd3GwfrZtm6aZ/uMuOhCQNZkq5Eemi4sLuVGe/p8+fXp1dZX0NqZphmHoOE6v15NLK9I7me30lnRus/3Y3B5vim3blmXFcWyapswZlmUtenKR0f7vb8npoBSV9uBFozKMJz/euYCx1+slx22aHCqcqpq5GyVVVecOn1iWFQTBds+qtFot13XlJ6q8y4L1UxSl1Wp5npdsWXIgIDu1Ws2yrHa7Hcex/KQohJCDuPLsnu7KbNvWNM22bblGL90Tzu30lv9NZ/uxRT1e4urqSgiRDjq1Wq2MPSHt/0EWnQ7KUWlL1necn583m81qtdpsNpvNplyHEgRBs9nsdDqdTqfZbAZBcHt763mefNrt7a3jOE+ePDk/P7+9vR0Oh/JBmuM41Wr15OSk2Wwmi6rmbpzaLnU6nWSjfPerq6vk8baqVqtzV+OivGQzTlaBNZtNz/MWHQjYgCAIqtXq7e1tr9dLd1xJjye7RLk28+zs7Pz8XG5PL+Wb7fTu7NzS/Vi3253t8eRum83mkydPku3yTU9OTpJilGvRKO1/ZVOngxJV2opf3iZT/51jd/LjAp/LAZRaGIaKoixaHJrenkunN1sMoID4tlgAAJA5vksFAABkjsABAAAyR+AAAACZI3AAAIDMETgAAEDmHnWnUWxSNJkoe3vK/n7eBUFW3LeB98VACNH6sG48a+RdHABFF19fxzc36uFh3gW5Fy6LLQd/MGj9x//k/dVf6PV63mXBmoWjkfM26L9+883NTbLxyd6eefr85bOGdnSUX9GAjdL/5m+FEP5f/2XeBSmN9372886Lj23jLO+C3AsjHEA+osnEfRv0Pn/95ddfCyHOGlrrw7rRaAgh3CBwfhf88tPf/vLT355UKi+fNYxnjbJ8iAGAuQgcwEbF19cyT3x2eSmEOKlU2h+dTuUJ8/TUPD1NEsnFq08uXn2SJBKm1QBIzePjcDTOuxT3ReAoB2VvTwgRX9/c+UwUlvs2cN6+fRWEQojqwUHnxcfLZ0zUw0Prxy+sH78IR6Pe52/cIHgVhJb7idFosMgDQOkQOMpBnpaC0YjTTOkkceGbm5sne3vnz58/NC5oR0f9nx71f/rnMrL86s2bX715U3UPjGeN9kenTLUAKAUCB5CJaDLpff7afRtMLdF4zISI8axhPGvISZne52/SizzM0+dMtQAoMgIHsE7x9XX/9RvnbfD78Vi8W6Kx3jSg7O8nizxkpkkv8jBPT9f1RgCwRgQOYD36r197XwzSSzSynu9QDw9t48w2zvzBwPldkF7k0f7oOdfTAigUAgfwKMnJPlmi8fJHjQ3fLkWv1/V6vf/TP5eh57tFHgcs8gBQIAQOYBXybl3pJRovnz3LfUmvnGpJpnWSRR7tj55zPS2wfdTDQzcI8i7FfRE4gAeQ98ZIL9G4+MmLop3Llf19eT1tssij/evftH/9m4KkIgDrouzvpe9QXHAEjtI4qVSiySTvUuwoeWHIhpdoPF6yyEN+S4tc5PHk19w0HUAOCByloeztceOvzUtO1ckSjTKux5TX09rGn8nYJKdaqgcHszc5BYCMEDiAOaaWaKxwt64CSl9PKyeG5PW0zePjlz9qFG1iCMCWIXAAfzT1hWrN4+MCLtF4vPRN0+UX1X52edn+9W+2I1cBKCYCBzD9hWrVg4Pu2Z/twlyDdnSkHR0lizzk9bQs8gCQBQIHdlpyohX3+0K1bZVe5OH8LkjfNH0XghdQavH1dSlGYQkc2EXJVMLKX6i2laYWefQ+f52+afr2TS0BZdf6sP7LT38bjscbvtngaggcpaHs7yl7dPdrEE0mjX//H8SavlBtK6UXecivun0VhPH1jfXjF3kXDdtMO6rkXYTyqR4cKHt7eZfiXt67vb3NuwzApvVfv9brdWYK7s99G2hHFWoMwMoIHAAAIHM/yLsAAABg+xE4AABA5ggcAAAgcwQOAACQOQJHyURRlHcRthC1mgVqFcvRQnYNgeNhfN+3LOuex0m/3+/3++stQLfbXe8OCyKKIsuyfN9/0Kssy1rLu29rrT6oua7dttYqFpFH8f3bGy1k1xA4HkbTtDiO73lE6bo+HA7X+O6+7yuKIoQwDENVVXm6jaJI13VVVeXZ2rIsTdMURUmyjuu6qqpqmqa/k5ynbduWr9V13TAM+XJFUeSuXNeVr1rjr7CIqqpPnz71PG8D7zUlqVXx/boyDCOpw9mKkmZr2/d9+TT5r7SB32K2JOKBzXW90rU6t1Hl1dKQHXkU37O93XncLToYpzo0TdN835/auPZPeoWypP8vuls8UKfT8d6Z+q8gCBzHSW/vdDq3t7fyyVdXV+knD4dDx3GCIJA/Xl1dyefM3bN0dnaW7OTs7CzZ3uv1HMeZ+vH8/HyqzFOlWvJjsvMlhVk7z/PS1ZVsn6ooabaillfg3J1I6Vq9/X5ddTqd4XCY/q/Zl0/VdhAEvV4v/eS5r8rC7N/9dl5zXVRRw+EwaaVBEMj/Wl6rssEPh0P52vR/zdbqbKPKq6Vh7WSDkc1A/h3nNqe0+xx3iw7G9PZ0c1p05G6fJf1/kTHCsYputys/OKY/7LquKzO7HFdMtpumGcdxHMeGYSTZ37KsXq8nP+ElO+l2u4ZhJE+eelP50Tn5TNBoNMIwlI+DIEg/P/lx6qNGGIYy+Lfb7eW/YLvdXtdsxUPJWvU8L45jsaCi5NM8z5sakl1UgYt2ImZqdZYsxhJTta1pmmma6SfYtn3nb70Wi/7us811bkVFUdTtdmWjCsMwqdtFtWqapuM4iqJ0u13TNNOjU3NrdW6jyrGlYV0sy3IcRwghz3xy46LmJK123E1tlB3a3LGxrV8dsqT/LzK+S2UVFxcXsoknR5cQQuaJKIo0TUtmUuQhoaqqEELTtG632+/35cEgz0O6rruu2+/3TdPUNK3Vask9B0Ew9aaO46RPXYZhJHtLH7fJj+12u9frJS+R3brcuSzPErquO46z+YM26aRkCRdVlBBC0zRN09LnKkVR5lbgkp2ImVpNNsrEI/e5vMBza3vzlpRkqrkuqihd15PQYJqmbMOLnhyGoaIoSa1qmpZ+x7m1OrdR5dXSsC5RFMVxLD/JpKdr5zanxP2Pu0UHY9KhpXs/OU8Xx/HFxcWdvVypLer/C47AsTbyAHj69OnV1VWSxFVVTdp98iCKojAM0yfLVqu1fOeyVaUbltxbHMe9Xi89YtHr9cIwlKeH9AcC27ZVVb1/z27btmmad46FZCH5NVeoqFlLdjJbq1KtVpOB5s4jeVFtb96GSxLHca1WS35MnwkW1apY0KhybGl4vCiKGo0/fs1y+vGSl9z/uFt0MKY7tDAMZQuUa61s2873YNyARf1/wRE41kOObsnMHsdxcnqTZzt5MMgPhUIIefCkA/6dh0ev17u4uJja2Gq1XNeN4zid5eM4TlYP9ft93/eT8cZ0+rmToiitVsvzvBXO8StTVfXi4sKyLNu25Qeah1bUrCU7mVur8iX3XMC4pLY3bO0lWZ5N5XCdYRiKooRhKMeN5H8tqlWxoFHl0tKwLpqmOY6THjKce/5LN6cHHXdLDsakQ3McZ2rwwzRNuZTygb9Nmczt/4su70UkJeN5XrVabTabt7e3juM8efIkWaPXbDY7nU6n0zk/P08en52ddd5JL5IKgiD5r2azKRdVJXseDofVajVZbDgcDqdWAiaq1Wp6NdbZ2VnywuFweHJyUq1Wf/GLX1Sr1ZOTk2azObVwtdvtNptN+b5yFZLjONVqNVmR1Gw2N7OUT5ZWlrzT6SS/xWxFyefLSpYlbzabcoXaogqcu5O5tSp//dm6mq2o2wW1LZfOJU/ezIrRRSWZ21yXVJTnebKizs/P5YPltXp+fi6fvLytzm1UebU0rF2v10uaTafTOTk5kQs2Z5vT7UOOu0UHY3q71Ol0ko3y6L66ukoeb7Gp/r/4CBzrFATB3KXRQRDMbfry5HTnbrd+xfWd7llRD9oJtTrX1dXVCt10cgqhVneTvI5p7vap5kQL2WV8PT2AFfX7/WQxYI4LZgGUAoEDwIrCMLzPtTwAIAgcAABgA7jxFwAAyByBAwAAZI7AAQAAMkfgAACglPS/+dv+69d5l+K+CBwAAJTSZ5eXw68meZfivggcAAAgcwQOAACQOQIHAADIHIEDAABkjsABAAAyR+AAAACZI3AAAIDMETgAAEDmCBwAACBzBA4AAJA5AgcAAMgcgQMAAGSOwAEAADJH4AAAAJkjcAAAgMwROAAAQOYIHAAAIHPv3d7e5l0GAACw5RjhAAAAmSNwAACAzBE4AABA5ggcAAAgcwQOAACQOQIHAABlFUVR3kW4LwIHAABl1e128y7CfRE4AAAoJd/3FUWRjw3DUFXVsiwhRBRFuq6rqnp6ejq70fd913VVVdU0TX9HPse2bfkcXdcNw5B7tixLURTf94UQruvKV61QWm78BQDAY0VRFIahoiiapiUhIAzDKIoURZk6Q8vtmqaJdzkg2YPMAfJpcRyHYahpWhiGQojZ07xhGP1+P505XNeVj+V2wzDmbhRCWJbVarWSfVqWZdv27ONkSxRFcj8yeayQORjhAADgUVzXlcMAURSpqppslCMQURTJ8QPJNE3HcRRF6Xa7pml6nieEsCyr1+vJgYRkaEEI0e12DcOI4ziO4/R2IYTv+6qqJmlDCNFoNGQ0EUIEQSCfP3djIgzDfr8vhGi328t/x3a7nf4tVvD+Y14MAADiOJbnftM0k8BhGEYURXIkYzgcyo1yFESOH+i6rmmabdty4Wey0XXdfr9vmqYcL0nGIYIgSL+p4zjRSPbkAAABqElEQVRT4xCGYXS73X6/L4dVlmyUZICQO0+KvYiu647jPGaNKoEDAIBHMU0zDEPHceQohTyFy9P506dPr66u4jiWz4zjuFarJS9MZlXCMEyPH7RareXvKNPDVICQoSGO416vl4xYzN0o2batqur9M4Rt26Zp3jkWsgiBAwCAR7Ft27IsmR4sy0rWcsoRiDiOkzChaZqcJVEUJQxDOZghl32khyuSgLJIr9e7uLiY3d5qtVzXlSMuyzdKqqreObaRUBSl1Wp5nndnHpqLRaMAADyKDBAyZCSLK+WMiRAijmM5sSIjRRiGciAkjuMkZ4Rh2O125bk/DEPbtjVN831fztH4vi/XlhqGIadg5CzJ3MKoqtrv96cWdU5tTBadKIrium4yUmLbtud5ciWK/K/kyZqmJb/XxcXFCotGCRwAAKyBXMWZHjCQKzaWDCGYppnODbN7mMuyrHa7ff+RiYIgcAAAsFH9fj9ZRjq18HOLETgAANioMAzlfEpyy41dQOAAAACZ48ZfAAAgcwQOAACQOQIHAADIHIEDAABkjsABAAAyR+AAAACZ+/+9B6HTplylAQAAAABJRU5ErkJggg==",
            "text/plain": [
              "Tree('S', [Tree('IP', [Tree('NP', ['ich/PPER']), Tree('VP', [Tree('V', ['haben/VAFIN']), Tree('NP', ['keine/PIAT', 'Ahnung/NN'])])]), Tree('CC', [Tree('C', ['was/PRELS']), Tree('IP', [Tree('NP', ['du/PPER']), Tree('VP', [Tree('V', ['sagen/VVFIN'])])])])])"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}