posgram_finder_demo_et.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tlu-dt-nlp/POSgram-contexts/blob/main/posgram_finder_demo_et.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sõnaliigijärjendite leidja"
      ],
      "metadata": {
        "id": "C3TDmRpCFOR0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Siin lehel saab katsetada sõnaliigijärjendite leidjat, mis otsib trigrammide ehk kolmesõnaliste järjendite kasutuskontekste. Eraldi saab otsida eeskontekste (lause algus või eelnev sõnaliik) ja järelkontekste (lause lõpp või järgnev sõnaliik). Tööriista abil võib analüüsida nii üksikuid tekste kui ka mahukamaid tekstikogusid. Viimasel juhul võimaldab programm luua statistilisi keelemudeleid."
      ],
      "metadata": {
        "id": "vys5apHqFUM9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ettevalmistus"
      ],
      "metadata": {
        "id": "4WesJ61gFXd1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tee endale rakenduse koodihoidlast koopia ja liigu kausta `posgram-contexts`. Paigalda ka Stanza keeletöötluspakett.\n",
        "\n"
      ],
      "metadata": {
        "id": "C1KPVBJnFcKt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pl9_AKeF69Lj",
        "outputId": "17ac9dd2-872f-46ed-e081-725671ddb796"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'posgram-contexts'...\n",
            "remote: Enumerating objects: 35, done.\u001b[K\n",
            "remote: Counting objects: 100% (35/35), done.\u001b[K\n",
            "remote: Compressing objects: 100% (31/31), done.\u001b[K\n",
            "remote: Total 35 (delta 13), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (35/35), 11.44 KiB | 11.44 MiB/s, done.\n",
            "Resolving deltas: 100% (13/13), done.\n",
            "/content/posgram-contexts\n"
          ]
        }
      ],
      "source": [
        "! git clone https://github.com/tlu-dt-nlp/posgram-contexts.git\n",
        "%cd posgram-contexts"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install stanza"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1x8PK5yYF6LX",
        "outputId": "b83ea1b3-84a1-49d3-9f3e-6dc1eb68e6fb"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting stanza\n",
            "  Downloading stanza-1.7.0-py3-none-any.whl (933 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m933.2/933.2 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting emoji (from stanza)\n",
            "  Downloading emoji-2.10.0-py2.py3-none-any.whl (457 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m457.9/457.9 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from stanza) (1.23.5)\n",
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            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.3.0->stanza) (2.1.0)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->stanza) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->stanza) (2023.11.17)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.3.0->stanza) (2.1.4)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.3.0->stanza) (1.3.0)\n",
            "Installing collected packages: emoji, stanza\n",
            "Successfully installed emoji-2.10.0 stanza-1.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Teksti analüüs käsureal"
      ],
      "metadata": {
        "id": "G1NMpe_MsqA1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Käivita rakendus ja sisesta tekst, mida soovid analüüsida. Paarilauselise teksti võib sisestada ühekordsete jutumärkidega, kuid pikema mitmele reale jaotatud teksti ümber tuleb panna kolmekordsed jutumärgid."
      ],
      "metadata": {
        "id": "6GGyOGEuegSK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498,
          "referenced_widgets": [
            "9f5240c13dd34d64aabcd5d5e37f9814",
            "74992c200d39470b95e2d6bf77665857",
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            "e245c450f5c1425e9c0b45e665395f00",
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            "8f503abbf5bb44e592ae48f643d35118",
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            "8eb18ffd445040c5b801ba5aab05b75f",
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            "1080a87b9370477297cf3cdbf7f5c63f",
            "62eb400d03784e4389211f05708ca42f",
            "b02144b416a94a22b76285ef5ec95373",
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            "11c1a9ebeb40411fbc5a5b5b01ef5f2b",
            "9af6b958a13043fd8207ffe990d5bffe",
            "38bf2868b8fa46ac8e543201056a13ac",
            "14d4e400e18a4c949978f3368925da39",
            "57bcd334220945ad9eaaf245a8365fe4",
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            "adbfb846f0784901a9376752157080a3",
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            "f6421c3c347e48ffb369a7eeb77d4685",
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            "00ce1542c73a45cd9beafcafedb4ab81",
            "983fdf0a048d4f099b8f1761a6d3e612",
            "890444e1716d435db1735730b3203a97",
            "391380db5e92461eaf4068b78362b184",
            "ee3a8f1fe11f4648904d613318080154",
            "30aca7c83f924813aa29b6a0e0099e9d",
            "542385aa8a49418cb059d27910c9b240",
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            "5ca0b693bc4e4162891be116cfffe6c0",
            "72fb693297f9401cafa0f9e0b825fe54"
          ]
        },
        "id": "16gM6efz7o9c",
        "outputId": "15a7dee3-db88-4f30-bc08-10396a64834d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:stanza:Checking for updates to resources.json in case models have been updated.  Note: this behavior can be turned off with download_method=None or download_method=DownloadMethod.REUSE_RESOURCES\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.7.0.json:   0%|   …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9f5240c13dd34d64aabcd5d5e37f9814"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading https://huggingface.co/stanfordnlp/stanza-et/resolve/v1.7.0/models/tokenize/edt.pt:   0%|         …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b02144b416a94a22b76285ef5ec95373"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading https://huggingface.co/stanfordnlp/stanza-et/resolve/v1.7.0/models/pos/edt_nocharlm.pt:   0%|     …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e70e71683364e099c6afd62ad50cfb7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading https://huggingface.co/stanfordnlp/stanza-et/resolve/v1.7.0/models/pretrain/conll17.pt:   0%|     …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cdbfd3b18224aa3b5f8a119f1609e6e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:stanza:Loading these models for language: et (Estonian):\n",
            "============================\n",
            "| Processor | Package      |\n",
            "----------------------------\n",
            "| tokenize  | edt          |\n",
            "| pos       | edt_nocharlm |\n",
            "============================\n",
            "\n",
            "INFO:stanza:Using device: cpu\n",
            "INFO:stanza:Loading: tokenize\n",
            "INFO:stanza:Loading: pos\n",
            "INFO:stanza:Done loading processors!\n"
          ]
        }
      ],
      "source": [
        "from posgram_contexts import PosgramContexts\n",
        "p = PosgramContexts(\"\"\"Juku tuli kooli. Juku tuli kooli ruttu.\n",
        "  Unine Juku tuli kooli.\"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trigrammide eeskontekstid trükib välja funktsioon `precontexts()`. See kuvab nii konteksti absoluutsageduse kui ka osakaalu trigrammi kõigi võimalike eeskontekstide suhtes. Trigrammid on reastatud tähestiku järgi."
      ],
      "metadata": {
        "id": "yuMyuHSLxY6e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(p.precontexts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qvHyPlygE7q2",
        "outputId": "ecebfebe-58fc-4144-e82e-fdfd455e1e94"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'ASV': {'^': [1, 100.0]}, 'SVS': {'^': [2, 66.66666666666667], 'A': [1, 33.333333333333336]}, 'VSD': {'S': [1, 100.0]}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sarnaselt saab välja trükkida trigrammide järelkontekstid, kasutades funktsiooni `postcontexts()`."
      ],
      "metadata": {
        "id": "iq5sqYocxp62"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(p.postcontexts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2QOy1i9tsVO",
        "outputId": "0d67cfbc-0b53-42f4-db6d-0f9ccb62729e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'ASV': {'S': [1, 100.0]}, 'SVS': {'$': [2, 66.66666666666667], 'D': [1, 33.333333333333336]}, 'VSD': {'$': [1, 100.0]}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selleks, et salvestada tulemused faili, tuleb funktsiooni argumendina sisestada loodava dokumendi nimi. See luuakse `json`-formaadis. Tulemus trükitakse välja ka käsureal.\n",
        "\n",
        "Peaksite kahte uut dokumenti nägema, kui avate Colabi vasakmenüüst failide vaate ja klõpsate lahti kausta `posgram-contexts`."
      ],
      "metadata": {
        "id": "15Iaam_wx01G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p.precontexts(\"juku_pre.json\")"
      ],
      "metadata": {
        "id": "nNkdi5ZFDWcW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "057eb91e-0a13-4be3-b41d-2fef8ccda3c2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ASV': {'^': [1, 100.0]},\n",
              " 'SVS': {'^': [2, 66.66666666666667], 'A': [1, 33.333333333333336]},\n",
              " 'VSD': {'S': [1, 100.0]}}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p.postcontexts(\"juku_post.json\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tS_hF_5XyQN1",
        "outputId": "b42d9f87-cd1f-4fa5-fcd2-ba931778d459"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ASV': {'S': [1, 100.0]},\n",
              " 'SVS': {'$': [2, 66.66666666666667], 'D': [1, 33.333333333333336]},\n",
              " 'VSD': {'$': [1, 100.0]}}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Failist sisse loetud teksti analüüs"
      ],
      "metadata": {
        "id": "UeFIxV-Eydoe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mahukamat teksti on mugavam sisse lugeda failist. Programmi kausta on testmaterjalina kaasa pandud Eesti vahekeele korpuse ([elle.tlu.ee/tools](https://elle.tlu.ee/tools)) emakeelekõneleja võrdluskorpuse osa, mis hõlmab 2014. aastal Postimehe ja Õhtulehe veebiväljaandes ilmunud arvamuslugusid mahus 107 197 sõna. Analüüs võib paar minutit aega võtta."
      ],
      "metadata": {
        "id": "Yfs73fFaObMH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p = PosgramContexts(open(\"EIC_L1_reference.txt\", \"r\", encoding=\"utf-8\").read())\n",
        "p.precontexts(\"EIC_L1_reference.json\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KFFdKdLWOT5E",
        "outputId": "3a544a6a-42b9-4491-b76a-936ec0b2641b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:stanza:Loading these models for language: et (Estonian):\n",
            "============================\n",
            "| Processor | Package      |\n",
            "----------------------------\n",
            "| tokenize  | edt          |\n",
            "| pos       | edt_nocharlm |\n",
            "============================\n",
            "\n",
            "INFO:stanza:Using device: cpu\n",
            "INFO:stanza:Loading: tokenize\n",
            "INFO:stanza:Loading: pos\n",
            "INFO:stanza:Done loading processors!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'AAA': {'S': [13, 41.935483870967744],\n",
              "  'A': [2, 6.451612903225806],\n",
              "  'J': [1, 3.225806451612903],\n",
              "  'D': [7, 22.580645161290324],\n",
              "  'P': [3, 9.67741935483871],\n",
              "  '^': [2, 6.451612903225806],\n",
              "  'V': [2, 6.451612903225806],\n",
              "  'K': [1, 3.225806451612903],\n",
              "  ...}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Omavalitud teksti saad Colabis analüüsimiseks üles laadida, kui klõpsad vasakul failide vaates töökausta nime kõrval kolme punktiga ikoonil ja valid \"Laadi üles\".\n",
        "\n",
        "Teine võimalus on kasutada allolevat üleslaadimise nuppu. Trigrammide ees- ja järelkontekstid salvestatakse vastavalt failidesse `answer_pre.json` ja `answer_post.json`.\n",
        "\n",
        "Sisendfail peaks olema `txt`-formaadis."
      ],
      "metadata": {
        "id": "5atlMfWRSi1P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import ipywidgets as widgets\n",
        "def fileInPlace(f):\n",
        "    if f[\"name\"] == \"data\":\n",
        "       p = PosgramContexts(f[\"new\"][0].decode(\"utf-8\"))\n",
        "       p.precontexts(\"answer_pre.json\")\n",
        "       p.postcontexts(\"answer_post.json\")\n",
        "\n",
        "loader = widgets.FileUpload(multiple=False)\n",
        "loader.observe(fileInPlace)\n",
        "loader"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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