practice_examples/07_Wordnet+AMR+Ngram.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# WordNet\n",
        "Lexical database   \n",
        "Synsets = sets of cognitive synonyms   \n",
        "Relations of synsets: (hyperonymy, hyponymy and ISA relations)\n",
        "\n",
        "[Homepage](https://wordnet.princeton.edu/)\n",
        "\n",
        "## Using NLTK to handle WordNet"
      ],
      "metadata": {
        "id": "bskwO63Lwwox"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LstVMaXTo9ja",
        "outputId": "6979300a-e6be-455a-e800-ef077e2f55a5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "from nltk.corpus import wordnet as wn\n",
        "import nltk\n",
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\"\n",
        "\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Looking up a word\n",
        "sets = wn.synsets(\"language\")\n",
        "print(sets)"
      ],
      "metadata": {
        "id": "N_tgZ0e5xo1e",
        "outputId": "0d6a92ee-4b5d-40f0-fa7b-26fbc04de67a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Synset('language.n.01'), Synset('speech.n.02'), Synset('lyric.n.01'), Synset('linguistic_process.n.02'), Synset('language.n.05'), Synset('terminology.n.01')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Specify POS\n",
        "\n",
        "sets = wn.synsets(\"lock\", wn.VERB)\n",
        "print(sets)"
      ],
      "metadata": {
        "id": "V8zh0gVSzsBF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de7d0791-9f29-4fd2-c972-7f0f4e5bbba5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Synset('lock.n.01'), Synset('lock.n.02'), Synset('lock.n.03'), Synset('lock.n.04'), Synset('lock.n.05'), Synset('lock.n.06')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Analyze synsets and lemmas\n",
        "sets = wn.synsets(\"language\")\n",
        "for synset in sets:\n",
        "  print(synset.definition().ljust(90), synset.examples()[0])\n",
        "  lemmas=synset.lemmas()\n",
        "  for lemma in lemmas:\n",
        "    print(str(lemma.name()), lemma.count(), end=\", \")\n",
        "  print(\"\\n\")"
      ],
      "metadata": {
        "id": "K-JPwphvx1UM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da750286-1a0b-42b1-9308-f49e00a49073"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a systematic means of communicating by the use of sounds or conventional symbols           he taught foreign languages\n",
            "language 48, linguistic_communication 0, \n",
            "\n",
            "(language) communication by word of mouth                                                  his speech was garbled\n",
            "speech 7, speech_communication 0, spoken_communication 0, spoken_language 2, language 5, voice_communication 0, oral_communication 0, \n",
            "\n",
            "the text of a popular song or musical-comedy number                                        his compositions always started with the lyrics\n",
            "lyric 12, words 8, language 2, \n",
            "\n",
            "the cognitive processes involved in producing and understanding linguistic communication   he didn't have the language to express his feelings\n",
            "linguistic_process 0, language 1, \n",
            "\n",
            "the mental faculty or power of vocal communication                                         language sets homo sapiens apart from all other animals\n",
            "language 1, speech 0, \n",
            "\n",
            "a system of words used to name things in a particular discipline                           legal terminology\n",
            "terminology 2, nomenclature 0, language 0, \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Multi-language support\n",
        "nltk.download('omw')\n",
        "\n",
        "wn.langs()"
      ],
      "metadata": {
        "id": "UlsmTShFyFQL",
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          "base_uri": "https://localhost:8080/"
        },
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package omw to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['eng']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wn.synset('police.n.01').lemma_names('spa')"
      ],
      "metadata": {
        "id": "f1INMAnN00kL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b431d6d9-1611-4ecb-a3e2-38c0b475f502"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['guardia', 'policía']"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wn.synset('police.n.01').lemma_names('jpn')"
      ],
      "metadata": {
        "id": "mVMaDaPT0mRo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37b74b12-ff81-46aa-920e-a7a528885fbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['お回り',\n",
              " 'お巡り',\n",
              " 'さつ',\n",
              " 'その筋',\n",
              " 'サツ',\n",
              " 'ポリス',\n",
              " '其の筋',\n",
              " '其筋',\n",
              " '刑事警察',\n",
              " '官憲',\n",
              " '察',\n",
              " '巡査',\n",
              " '御回',\n",
              " '御回り',\n",
              " '御巡',\n",
              " '御巡り',\n",
              " '警官',\n",
              " '警官隊',\n",
              " '警察',\n",
              " '警察当局',\n",
              " '警察隊']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Easier access with morphy\n",
        "Utilizes ending rules and exceptions for lemmatization"
      ],
      "metadata": {
        "id": "wl3PZ34j3RWJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wn.morphy('locking', pos=wn.NOUN)"
      ],
      "metadata": {
        "id": "DDawrzfH3RGb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "00584b98-c6f8-48a5-d892-9da38fc20598"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'locking'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wn.morphy('locking', pos=wn.VERB)"
      ],
      "metadata": {
        "id": "o5r85Wcy3v5J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3dfb0453-d2e3-4333-d13c-e946fb8ebb1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'lock'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "wn.synsets(\"locking\")"
      ],
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        "id": "QCEvTg071SGh",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b2f8994-8fc5-40eb-8275-da37abd8c81b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Synset('locking.n.01'),\n",
              " Synset('lock.v.01'),\n",
              " Synset('engage.v.10'),\n",
              " Synset('lock.v.03'),\n",
              " Synset('lock.v.04'),\n",
              " Synset('interlock.v.03'),\n",
              " Synset('lock.v.06'),\n",
              " Synset('lock_in.v.02'),\n",
              " Synset('lock.v.08'),\n",
              " Synset('lock.v.09')]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Antonyms and synonyms\n",
        "\n"
      ],
      "metadata": {
        "id": "bLfPcoJY56Ov"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cold = wn.synset(\"cold.a.01\").lemmas()[0]\n",
        "print(cold.name())\n",
        "print([lemma.name() for lemma in cold.antonyms()])"
      ],
      "metadata": {
        "id": "0-AhLktw5587",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3755ae1c-794d-48db-f6f1-9eb7c7aa191e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cold\n",
            "['hot']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Pertainyms -> pertain \n",
        "synset = wn.synset(\"lingual.a.01\").lemmas()[0]\n",
        "print(synset.name())\n",
        "print([lemma.name() for lemma in synset.pertainyms()])"
      ],
      "metadata": {
        "id": "rAp2Ohru5sOq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df46b894-27a5-4d61-c0da-b97d7f3ab8f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "linguistic\n",
            "['language']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wn.synsets(\"pertain\")"
      ],
      "metadata": {
        "id": "l9AjB7VUBXmo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4c8f62e-e295-4bbc-808f-06d1b0e177fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Synset('refer.v.02'), Synset('pertain.v.02')]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wn.lemma(\"icy.a.01.frigid\").derivationally_related_forms()"
      ],
      "metadata": {
        "id": "l9KOqhd5Agam",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60f2e3bb-5f0c-4e02-a532-e8d3bf858a7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Lemma('coldness.n.02.frigidness'), Lemma('coldness.n.02.frigidity')]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Synonyms\n",
        "set([lemma.name() for synset in wn.synsets(\"icy\") for lemma in synset.lemmas()])"
      ],
      "metadata": {
        "id": "zx967CQZCWnj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "048d0e13-912c-4d0e-88ae-fb36e4130a96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'arctic',\n",
              " 'frigid',\n",
              " 'frosty',\n",
              " 'frozen',\n",
              " 'gelid',\n",
              " 'glacial',\n",
              " 'icy',\n",
              " 'polar',\n",
              " 'wintry'}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Synset relations\n",
        "\n",
        "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/b/b4/Hyponym_and_hypernym.svg/1200px-Hyponym_and_hypernym.svg.png\" width=60%>"
      ],
      "metadata": {
        "id": "-yJXRfZA3-wz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dog = wn.synset(\"dog.n.01\")\n",
        "dog.hypernyms()"
      ],
      "metadata": {
        "id": "rxg_Nlo83zpp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e9ea18c-75c2-4793-8476-4c5a78ce686f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Synset('canine.n.02'), Synset('domestic_animal.n.01')]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wn.synset(\"domestic_animal.n.01\").hyponyms()"
      ],
      "metadata": {
        "id": "51F0ql395jiU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aae4204f-de76-43c1-fbc3-a5c369350c57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Synset('dog.n.01'),\n",
              " Synset('domestic_cat.n.01'),\n",
              " Synset('feeder.n.01'),\n",
              " Synset('head.n.02'),\n",
              " Synset('stocker.n.01'),\n",
              " Synset('stray.n.01')]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Member holonyms: Name of a group populated with synset members (holonym = \"whole\"+\"name\")\n",
        "wn.synset(\"tree.n.01\").member_holonyms()"
      ],
      "metadata": {
        "id": "ccW_lmZu6ycI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c52ab7c7-7bc2-439d-8b5e-085fc5fcba20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Synset('forest.n.01')]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Member meronyms: The things the synset consists of (meronym = \"part\"+\"name\")\n",
        "wn.synset(\"forest.n.01\").member_meronyms()"
      ],
      "metadata": {
        "id": "pN4CBovv7iem",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3487fe6c-14c5-4676-fd41-72a19ac22310"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Synset('tree.n.01'), Synset('underbrush.n.01')]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wn.synset(\"tree.n.01\").part_meronyms()"
      ],
      "metadata": {
        "id": "WFMAGwKR8vRK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51a1f5a2-0e4b-42f0-94ad-2a24efb851c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Synset('burl.n.02'),\n",
              " Synset('crown.n.07'),\n",
              " Synset('limb.n.02'),\n",
              " Synset('stump.n.01'),\n",
              " Synset('trunk.n.01')]"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wn.synset(\"coffee.n.01\").substance_meronyms()"
      ],
      "metadata": {
        "id": "gMoL2rql9Fn0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78af731e-f4b6-49e8-d11e-22ea3fa01314"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Synset('caffeine.n.01'), Synset('coffee_bean.n.01')]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wn.synset('computer.n.01').lowest_common_hypernyms(wn.synset('robot.n.01'))"
      ],
      "metadata": {
        "id": "aGI_3ZUgDcgY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2564a16e-cc9c-41dc-f5ab-53f065b00352"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Synset('device.n.01')]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wn.synset('tree.n.01').root_hypernyms()"
      ],
      "metadata": {
        "id": "TVHAoGmAECjM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49f4d1e2-0c33-4aed-c755-bdeb70562ed9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Synset('entity.n.01')]"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Distance in the taxonomy tree"
      ],
      "metadata": {
        "id": "s1t2AxysEXIa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "computer = wn.synset('computer.n.01')\n",
        "machine = wn.synset('machine.n.01')\n",
        "apple = wn.synset('apple.n.01')"
      ],
      "metadata": {
        "id": "z1iSySAAEGkC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "computer.path_similarity(machine)"
      ],
      "metadata": {
        "id": "gqCumb9RC326",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1512978-e804-42fb-b447-3d7a3df2a9cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "computer.path_similarity(apple)"
      ],
      "metadata": {
        "id": "xwW51exVExJS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0529e7fc-a621-47d9-9e3d-4f2504f5e45d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07692307692307693"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "machine.path_similarity(apple)"
      ],
      "metadata": {
        "id": "MfQeCDTtEzuZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17c86f3c-d41b-4730-b5c0-7f21271dcebc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08333333333333333"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Leacock-Chodorow Similarity:   \n",
        "The relationship is given as -log(p/2d) where p is the shortest path length and d the taxonomy depth."
      ],
      "metadata": {
        "id": "12LWAxdxFKCQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "computer.lch_similarity(machine)"
      ],
      "metadata": {
        "id": "FH09HO1KFUhW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b57e4f91-06ab-4c21-e13e-e31e881ed04b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.9444389791664407"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "computer.lch_similarity(apple)"
      ],
      "metadata": {
        "id": "mUKtsNWPFB6f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9961182-9ae9-453d-af72-ace8b225d412"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.072636802264849"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "machine.lch_similarity(apple)"
      ],
      "metadata": {
        "id": "xkFgmeINFbz9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fb8569e-affc-4044-e336-e1fea551bd5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.1526795099383855"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AMR (Abstract Meaning Representation)\n",
        "\n",
        "- semantic representation language\n",
        "- directed acyclic graphs\n",
        "- abstract nodes of semantics\n",
        "- abstract relations\n",
        "- equivalent to logical formal languages\n",
        "\n",
        "Generated using (typically graph-based deep learning-based) parsers.\n",
        "\n",
        "Example from wiki:   \n",
        "<img src=\"https://wikimedia.org/api/rest_v1/media/math/render/svg/aa09d9486ebbe2b7ea839a0574e095a92504da8d\">   \n",
        "\n",
        "(w / want-01   \n",
        "- :arg0 (b / boy)\n",
        "- :arg1 (g / go-01\n",
        " -    :arg0 b))\n"
      ],
      "metadata": {
        "id": "rpILuy0mFjF7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def download_from_drive(link, filename):\n",
        "  id = link.split(\"/\")[-2]\n",
        "  !wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id={id}' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id={id}\" -O {\"/content/\"+filename+\".rar\"} && rm -rf /tmp/cookies.txt\n",
        "  !unrar x -Y {\"/content/\"+filename+\".rar\"} {\"/content/\"+filename+\"/\"}\n",
        "\n",
        "download_from_drive(\"https://drive.google.com/file/d/15JrK2IY2aaq1sxp_H19-FnAvdW8lbCst/view?usp=sharing\", \"data\")"
      ],
      "metadata": {
        "id": "EazJd8c7Fe-3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c085bbe-301d-454b-e846-1ac2212c2dce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-02 14:04:28--  https://docs.google.com/uc?export=download&confirm=t&id=15JrK2IY2aaq1sxp_H19-FnAvdW8lbCst\n",
            "Resolving docs.google.com (docs.google.com)... 142.251.120.139, 142.251.120.102, 142.251.120.101, ...\n",
            "Connecting to docs.google.com (docs.google.com)|142.251.120.139|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-14-2k-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/skd2g9eqefajussdc4sikbbjobb4oefi/1683036225000/01308971736394707036/*/15JrK2IY2aaq1sxp_H19-FnAvdW8lbCst?e=download&uuid=5fdb36ce-546d-4606-b1a4-5a82e892e8c5 [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2023-05-02 14:04:28--  https://doc-14-2k-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/skd2g9eqefajussdc4sikbbjobb4oefi/1683036225000/01308971736394707036/*/15JrK2IY2aaq1sxp_H19-FnAvdW8lbCst?e=download&uuid=5fdb36ce-546d-4606-b1a4-5a82e892e8c5\n",
            "Resolving doc-14-2k-docs.googleusercontent.com (doc-14-2k-docs.googleusercontent.com)... 74.125.132.132, 2607:f8b0:4001:c00::84\n",
            "Connecting to doc-14-2k-docs.googleusercontent.com (doc-14-2k-docs.googleusercontent.com)|74.125.132.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2784768180 (2.6G) [application/rar]\n",
            "Saving to: ‘/content/data.rar’\n",
            "\n",
            "/content/data.rar   100%[===================>]   2.59G   110MB/s    in 29s     \n",
            "\n",
            "2023-05-02 14:04:57 (92.0 MB/s) - ‘/content/data.rar’ saved [2784768180/2784768180]\n",
            "\n",
            "\n",
            "UNRAR 5.61 beta 1 freeware      Copyright (c) 1993-2018 Alexander Roshal\n",
            "\n",
            "\n",
            "Extracting from /content/data.rar\n",
            "\n",
            "Creating    /content/data                                             OK\n",
            "Creating    /content/data/model_stog                                  OK\n",
            "Extracting  /content/data/model_stog/amrlib_meta.json                    \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  /content/data/model_stog/config.json                         \b\b\b\b  0%\b\b\b\b\b  OK \n",
            "Extracting  /content/data/model_stog/pytorch_model.bin                   \b\b\b\b  0%\b\b\b\b  1%\b\b\b\b  2%\b\b\b\b  3%\b\b\b\b  4%\b\b\b\b  5%\b\b\b\b  6%\b\b\b\b  7%\b\b\b\b  8%\b\b\b\b  9%\b\b\b\b 10%\b\b\b\b 11%\b\b\b\b 12%\b\b\b\b 13%\b\b\b\b 14%\b\b\b\b 15%\b\b\b\b 16%\b\b\b\b 17%\b\b\b\b 18%\b\b\b\b 19%\b\b\b\b 20%\b\b\b\b 21%\b\b\b\b 22%\b\b\b\b 23%\b\b\b\b 24%\b\b\b\b 25%\b\b\b\b 26%\b\b\b\b 27%\b\b\b\b 28%\b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  /content/data/model_stog/training_args.bin                   \b\b\b\b 29%\b\b\b\b\b  OK \n",
            "Extracting  /content/data/huwiki.1.tsv                                   \b\b\b\b 29%\b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Creating    /content/data/model_gtos                                  OK\n",
            "Extracting  /content/data/model_gtos/amrlib_meta.json                    \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  /content/data/model_gtos/config.json                         \b\b\b\b 30%\b\b\b\b\b  OK \n",
            "Extracting  /content/data/model_gtos/pytorch_model.bin                   \b\b\b\b 30%\b\b\b\b 31%\b\b\b\b 32%\b\b\b\b 33%\b\b\b\b 34%\b\b\b\b 35%\b\b\b\b 36%\b\b\b\b 37%\b\b\b\b 38%\b\b\b\b 39%\b\b\b\b 40%\b\b\b\b 41%\b\b\b\b 42%\b\b\b\b 43%\b\b\b\b 44%\b\b\b\b 45%\b\b\b\b 46%\b\b\b\b 47%\b\b\b\b 48%\b\b\b\b 49%\b\b\b\b 50%\b\b\b\b 51%\b\b\b\b 52%\b\b\b\b 53%\b\b\b\b 54%\b\b\b\b 55%\b\b\b\b 56%\b\b\b\b 57%\b\b\b\b 58%\b\b\b\b 59%\b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  /content/data/model_gtos/training_args.bin                   \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Creating    /content/data/training_1_pretrained                       OK\n",
            "Extracting  /content/data/training_1_pretrained/checkpoint               \b\b\b\b 60%\b\b\b\b\b  OK \n",
            "Extracting  /content/data/training_1_pretrained/cp.ckpt.data-00000-of-00001     \b\b\b\b 60%\b\b\b\b 61%\b\b\b\b 62%\b\b\b\b 63%\b\b\b\b 64%\b\b\b\b 65%\b\b\b\b 66%\b\b\b\b 67%\b\b\b\b 68%\b\b\b\b 69%\b\b\b\b 70%\b\b\b\b 71%\b\b\b\b 72%\b\b\b\b 73%\b\b\b\b 74%\b\b\b\b 75%\b\b\b\b 76%\b\b\b\b 77%\b\b\b\b 78%\b\b\b\b 79%\b\b\b\b 80%\b\b\b\b 81%\b\b\b\b 82%\b\b\b\b 83%\b\b\b\b 84%\b\b\b\b 85%\b\b\b\b 86%\b\b\b\b 87%\b\b\b\b 88%\b\b\b\b 89%\b\b\b\b 90%\b\b\b\b 91%\b\b\b\b 92%\b\b\b\b 93%\b\b\b\b 94%\b\b\b\b 95%\b\b\b\b 96%\b\b\b\b 97%\b\b\b\b 98%\b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/data/training_1_pretrained/cp.ckpt.index            \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "Extracting  /content/data/newdata.csv                                    \b\b\b\b 99%\b\b\b\b\b  OK \n",
            "All OK\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0_S9GvID9Yai"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/bjascob/amrlib\n",
        "\n",
        "\n",
        "!pip install -r amrlib/requirements.txt\n",
        "!pip install amrlib\n",
        "!pip install sentencepiece\n",
        "\n",
        "%mkdir /usr/local/lib/python3.10/dist-packages/amrlib/data\n",
        "%mv /content/data/model_gtos \"/usr/local/lib/python3.10/dist-packages/amrlib/data/model_gtos\"\n",
        "%mv /content/data/model_stog \"/usr/local/lib/python3.10/dist-packages/amrlib/data/model_stog\""
      ],
      "metadata": {
        "id": "GKgqe2t7Jant",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e980da86-230f-4cad-9166-35883e7f3b71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'amrlib'...\n",
            "remote: Enumerating objects: 1259, done.\u001b[K\n",
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            "remote: Total 1259 (delta 25), reused 32 (delta 13), pack-reused 1196\u001b[K\n",
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            "Resolving deltas: 100% (801/801), done.\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting penman>=1.1.0 (from -r amrlib/requirements.txt (line 3))\n",
            "  Downloading Penman-1.2.2-py3-none-any.whl (43 kB)\n",
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            "\u001b[?25hCollecting smatch (from -r amrlib/requirements.txt (line 9))\n",
            "  Downloading smatch-1.0.4.tar.gz (26 kB)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting PyQt5 (from -r amrlib/requirements.txt (line 16))\n",
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            "  Downloading word2number-1.1.zip (9.7 kB)\n",
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            "Collecting huggingface-hub<1.0,>=0.11.0 (from transformers>=4.16->-r amrlib/requirements.txt (line 8))\n",
            "  Downloading huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers>=4.16->-r amrlib/requirements.txt (line 8))\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting PyQt5-sip<13,>=12.11 (from PyQt5->-r amrlib/requirements.txt (line 16))\n",
            "  Downloading PyQt5_sip-12.12.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.whl (360 kB)\n",
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            "\u001b[?25hCollecting PyQt5-Qt5>=5.15.2 (from PyQt5->-r amrlib/requirements.txt (line 16))\n",
            "  Downloading PyQt5_Qt5-5.15.2-py3-none-manylinux2014_x86_64.whl (59.9 MB)\n",
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            "Building wheels for collected packages: smatch, word2number\n",
            "  Building wheel for smatch (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for smatch: filename=smatch-1.0.4-py3-none-any.whl size=24067 sha256=394293caaaa80eea19623453f01e043dcf9dd86934469bdb0373dad9f5585e5a\n",
            "  Stored in directory: /root/.cache/pip/wheels/2a/5e/2d/61b88bc74b337fbf3e998b9aa6b43bec72227e18a84a8335e8\n",
            "  Building wheel for word2number (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5582 sha256=ed47163f663591232b3a6da72cc8d5a74b8ef5d83b43c2e5bae6b64b74defb99\n",
            "  Stored in directory: /root/.cache/pip/wheels/84/ff/26/d3cfbd971e96c5aa3737ecfced81628830d7359b55fbb8ca3b\n",
            "Successfully built smatch word2number\n",
            "Installing collected packages: word2number, tokenizers, smatch, PyQt5-Qt5, penman, unidecode, PyQt5-sip, PyQt5, huggingface-hub, transformers\n",
            "Successfully installed PyQt5-5.15.9 PyQt5-Qt5-5.15.2 PyQt5-sip-12.12.1 huggingface-hub-0.14.1 penman-1.2.2 smatch-1.0.4 tokenizers-0.13.3 transformers-4.28.1 unidecode-1.3.6 word2number-1.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting amrlib\n",
            "  Downloading amrlib-0.7.1-py3-none-any.whl (354 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m354.2/354.2 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Installing collected packages: amrlib\n",
            "Successfully installed amrlib-0.7.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "import amrlib\n",
        "import penman\n",
        "\n",
        "stog = amrlib.load_stog_model()\n",
        "gtos = amrlib.load_gtos_model()\n",
        "\n",
        "amrlib.setup_spacy_extension()\n",
        "nlp = spacy.load('en_core_web_sm')"
      ],
      "metadata": {
        "id": "-UAyT_O9IHI_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341,
          "referenced_widgets": [
            "f2fbcc561589411297f109064759767c",
            "c62d80ed74f94435ac06dbc249766bd0",
            "ff3d0eb0bc2c4a04b1c2a725af7a357a",
            "9dca0a1e550c4ba09664a2b163a47e85",
            "0a0f7dc554164ab083bdf4ddc76a2e7f",
            "0e1e871d57f74415be60338b97b9b231",
            "f94d5ddc77c04e0695784fa278e529b3",
            "90ac3687c1784c64853583c79676a9be",
            "1f75c1b2f72048408c9567c5ebe4e863",
            "8c3232a26f15432983090ef17553ba60",
            "2d466e3d80704ea78469f1fde67e5384",
            "602fcb6124974118831ce1830d7f072e",
            "d25b86501f794230b9e32541852a529c",
            "cdbe3da3bf47481f97dcc71c26bd357a",
            "1440f7f9e8ee46b5b45ef57dfe1d91c9",
            "5e7043113c574e0980a600c45f31029b",
            "7e86e291ba924d829decf7535d3ac65b",
            "8e6455edeede4029a8dec4d00daea832",
            "2b2c7bc9eb704cd788fbbff926331c99",
            "4e5ec34dd37d448ea36ce2daa21002ae",
            "61b2e4fd9b654d889349d6edb6df232b",
            "ec4ef050d9a647aca10fae70a6cd01ff",
            "f08631fecacc4041900bf95fc95ab40d",
            "c4b40b92b0594ee98e61270d226e6ecb",
            "754f01fb88dd4075b3f732ad13126212",
            "3fbefcbdb10c4b838bb13548924700af",
            "46a1c7117b6045dca00eab10ab73ce31",
            "7f80babf383d4d22b1afe46a7fd02831",
            "33431542b1d047af8b4a239f357c7248",
            "2b9d65eabb8841b2ab861d65c0c65ff6",
            "07525e168265407b995fee9b6515f375",
            "5dca7ea43d4f4f7f9c4371cdebf17649",
            "b07fdc3e6c2e4ad2ac0d0886fa8bdb72"
          ]
        },
        "outputId": "ba17b372-47eb-4375-9633-1545f3882f83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2fbcc561589411297f109064759767c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "602fcb6124974118831ce1830d7f072e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f08631fecacc4041900bf95fc95ab40d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/t5/tokenization_t5_fast.py:155: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/models/t5/tokenization_t5.py:163: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc = nlp('I have a severe headache.')\n",
        "graphs = doc._.to_amr()\n",
        "for graph in graphs:\n",
        "    print(graph)"
      ],
      "metadata": {
        "id": "UWB913EEMFlL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8838994-9b74-4a1c-fd2c-5b9a5fb913b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# ::snt I have a severe headache.\n",
            "(h / have-03\n",
            "      :ARG0 (ii / i)\n",
            "      :ARG1 (h2 / headache\n",
            "            :mod (s / severe)))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ode to a Nightingale line\n",
        "doc = nlp('Returning to the action and spectacle that have captured moviegoers around the world, Transformers: Rise of the Beasts will take audiences on a ‘90s globetrotting adventure with the Autobots and introduce a whole new faction of Transformers – the Maximals – to join them as allies in the existing battle for earth. Directed by Steven Caple Jr. and starring Anthony Ramos and Dominique Fishback, the film arrives in theatres June 9, 2023.')\n",
        "graphs = doc._.to_amr()\n",
        "for graph in graphs:\n",
        "    print(graph)"
      ],
      "metadata": {
        "id": "9bTqWa_QIwY2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb6b00ac-f95b-4198-a5e1-b611e319654c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# ::snt Returning to the action and spectacle that have captured moviegoers around the world, Transformers: Rise of the Beasts will take audiences on a ‘90s globetrotting adventure with the Autobots and introduce a whole new faction of Transformers – the Maximals – to join them as allies in the existing battle for earth.\n",
            "(a / and\n",
            "      :op1 (t / take-01\n",
            "            :ARG0 (w / work-of-art\n",
            "                  :name (n / name\n",
            "                        :op1 \"Transformers\"\n",
            "                        :op2 \":\"\n",
            "                        :op3 \"Rise\"\n",
            "                        :op4 \"of\"\n",
            "                        :op5 \"the\"\n",
            "                        :op6 \"Beasts\"))\n",
            "            :ARG1 (a2 / audience)\n",
            "            :ARG2 (a3 / adventure\n",
            "                  :mod (g / globetrotting)\n",
            "                  :accompanier (w2 / work-of-art\n",
            "                        :name (n2 / name\n",
            "                              :op1 \"Autobots\"))\n",
            "                  :time (d / date-entity\n",
            "                        :decade 1990)))\n",
            "      :op2 (ii / introduce-02\n",
            "            :ARG0 w\n",
            "            :ARG1 (f / faction\n",
            "                  :mod (w3 / whole)\n",
            "                  :ARG1-of (n3 / new-01)\n",
            "                  :consist-of (w4 / work-of-art\n",
            "                        :name (n4 / name\n",
            "                              :op1 \"The\"\n",
            "                              :op2 \"Maximales\")))\n",
            "            :purpose (j / join-01\n",
            "                  :ARG0 f\n",
            "                  :ARG1 f\n",
            "                  :ARG2 (a4 / ally-01\n",
            "                        :ARG1 f\n",
            "                        :ARG2 (b / battle-01\n",
            "                              :ARG1 (p / planet\n",
            "                                    :name (n5 / name\n",
            "                                          :op1 \"Earth\"))\n",
            "                              :ARG1-of (e / exist-01)))))\n",
            "      :ARG1-of (r / return-01\n",
            "            :ARG2 (a5 / and\n",
            "                  :op1 (a6 / action)\n",
            "                  :op2 (s / spectacle)\n",
            "                  :ARG0-of (c / capture-01\n",
            "                        :ARG1 (p2 / person\n",
            "                              :ARG0-of (w5 / watch-01\n",
            "                                    :ARG1 (m / movie)))\n",
            "                        :location (a7 / around\n",
            "                              :op1 (w6 / world))))))\n",
            "# ::snt Directed by Steven Caple Jr. and starring Anthony Ramos and Dominique Fishback, the film arrives in theatres June 9, 2023.\n",
            "(a / arrive-01\n",
            "      :ARG1 (f / film\n",
            "            :ARG1-of (d / direct-01\n",
            "                  :ARG0 (p / person\n",
            "                        :name (n / name\n",
            "                              :op1 \"Steven\"\n",
            "                              :op2 \"Caple\"\n",
            "                              :op3 \"Jr.\")))\n",
            "            :ARG0-of (s / star-01\n",
            "                  :ARG1 (a2 / and\n",
            "                        :op1 (p2 / person\n",
            "                              :name (n2 / name\n",
            "                                    :op1 \"Anthony\"\n",
            "                                    :op2 \"Ramos\"))\n",
            "                        :op2 (p3 / person\n",
            "                              :name (n3 / name\n",
            "                                    :op1 \"Dominique\"\n",
            "                                    :op2 \"Fishback\")))))\n",
            "      :ARG4 (t / theater)\n",
            "      :time (d2 / date-entity\n",
            "            :year 2023\n",
            "            :month 6\n",
            "            :day 9))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentences=['Bob found the pen on the table.']\n",
        "graphs = stog.parse_sents(sentences)\n",
        "for graph in graphs:\n",
        "    print(graph+\"\\n\")"
      ],
      "metadata": {
        "id": "JY1oWWP6MQ8M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aca26749-f235-4449-fd09-8966f5d71cb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# ::snt Bob found the pen on the table.\n",
            "(f / find-01\n",
            "      :ARG0 (p / person\n",
            "            :name (n / name\n",
            "                  :op1 \"Bob\"))\n",
            "      :ARG1 (p2 / pen)\n",
            "      :location (t / table))\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pgraph = penman.decode(graphs[0])\n",
        "print(pgraph)\n",
        "new_amr_string = penman.encode(pgraph)\n",
        "print(new_amr_string)"
      ],
      "metadata": {
        "id": "M8HGhnHvIwQS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21776636-b990-4148-cce9-172b3ae30240"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph(\n",
            "  [('f', ':instance', 'find-01'),\n",
            "   ('f', ':ARG0', 'p'),\n",
            "   ('p', ':instance', 'person'),\n",
            "   ('p', ':name', 'n'),\n",
            "   ('n', ':instance', 'name'),\n",
            "   ('n', ':op1', '\"Bob\"'),\n",
            "   ('f', ':ARG1', 'p2'),\n",
            "   ('p2', ':instance', 'pen'),\n",
            "   ('f', ':location', 't'),\n",
            "   ('t', ':instance', 'table')],\n",
            "  epidata={('f', ':instance', 'find-01'): [],\n",
            "    ('f', ':ARG0', 'p'): [Push(p)],\n",
            "    ('p', ':instance', 'person'): [],\n",
            "    ('p', ':name', 'n'): [Push(n)],\n",
            "    ('n', ':instance', 'name'): [],\n",
            "    ('n', ':op1', '\"Bob\"'): [POP, POP],\n",
            "    ('f', ':ARG1', 'p2'): [Push(p2)],\n",
            "    ('p2', ':instance', 'pen'): [POP],\n",
            "    ('f', ':location', 't'): [Push(t)],\n",
            "    ('t', ':instance', 'table'): [POP]})\n",
            "# ::snt Bob found the pen on the table.\n",
            "(f / find-01\n",
            "   :ARG0 (p / person\n",
            "            :name (n / name\n",
            "                     :op1 \"Bob\"))\n",
            "   :ARG1 (p2 / pen)\n",
            "   :location (t / table))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gtos.generate([new_amr_string,])[0][0]"
      ],
      "metadata": {
        "id": "38MOISh7IqPd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "97584f29-60f7-42ac-869c-0574e7f7bc9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Bob found a pen on the table.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Changing the graph root for paraphrasing\n",
        "print('Existing top', pgraph.top)\n",
        "print('variables:', pgraph.variables())\n",
        "for triple in pgraph.instances():\n",
        "  print('var=%s concept=%s role=%s' % (triple.source.ljust(5), triple.target.ljust(15), triple.role))"
      ],
      "metadata": {
        "id": "VMUCfLf6PAc3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bb5aa17-30d4-472a-a584-30e77c5df3b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Existing top f\n",
            "variables: {'t', 'f', 'n', 'p', 'p2'}\n",
            "var=f     concept=find-01         role=:instance\n",
            "var=p     concept=person          role=:instance\n",
            "var=n     concept=name            role=:instance\n",
            "var=p2    concept=pen             role=:instance\n",
            "var=t     concept=table           role=:instance\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Change to pen\n",
        "new_amr_string = penman.encode(pgraph, top=\"p2\")\n",
        "print(new_amr_string)\n",
        "gtos.generate([new_amr_string,])[0][0]\n"
      ],
      "metadata": {
        "id": "uzY2yZd-PlMa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "outputId": "2cb8591f-ea46-4891-a471-61416afa765d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# ::snt Bob found the pen on the table.\n",
            "(p2 / pen\n",
            "    :ARG1-of (f / find-01\n",
            "                :location (t / table)\n",
            "                :ARG0 (p / person\n",
            "                         :name (n / name\n",
            "                                  :op1 \"Bob\"))))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The pen Bob found on the table.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Change to Bob (name)\n",
        "new_amr_string = penman.encode(pgraph, top=\"n\")\n",
        "print(new_amr_string)\n",
        "gtos.generate([new_amr_string,])[0][0]"
      ],
      "metadata": {
        "id": "qwGJXJjbP8aq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "outputId": "42eead47-e446-4595-8278-8b71ade1a540"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# ::snt Bob found the pen on the table.\n",
            "(n / name\n",
            "   :name-of (p / person\n",
            "               :ARG0-of (f / find-01\n",
            "                           :ARG1 (p2 / pen)\n",
            "                           :location (t / table)))\n",
            "   :op1 \"Bob\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Bob, who found the pen on the table.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pgraph.triples"
      ],
      "metadata": {
        "id": "jfCU9pHMT2rH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0a818f3-90e1-484b-8b07-992d3ef60ca1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('f', ':instance', 'find-01'),\n",
              " ('f', ':ARG0', 'p'),\n",
              " ('p', ':instance', 'person'),\n",
              " ('p', ':name', 'n'),\n",
              " ('n', ':instance', 'name'),\n",
              " ('n', ':op1', '\"Bob\"'),\n",
              " ('f', ':ARG1', 'p2'),\n",
              " ('p2', ':instance', 'pen'),\n",
              " ('f', ':location', 't'),\n",
              " ('t', ':instance', 'table')]"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pgraph.edges()"
      ],
      "metadata": {
        "id": "eKyIhtx9UjrF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22ac512f-772d-4b6c-d29c-3ec60c9609aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Edge(source='f', role=':ARG0', target='p'),\n",
              " Edge(source='p', role=':name', target='n'),\n",
              " Edge(source='f', role=':ARG1', target='p2'),\n",
              " Edge(source='f', role=':location', target='t')]"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Replace \n",
        "tripleMod = list(pgraph.triples[-2])\n",
        "tripleMod[1] = \":location-under\"\n",
        "pgraph.triples[-2]=tuple(tripleMod)\n",
        "new_amr_string = penman.encode(pgraph)\n",
        "print(new_amr_string)\n",
        "gtos.generate([new_amr_string,])[0][0]"
      ],
      "metadata": {
        "id": "rWz1bv4nQHco",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "outputId": "2a6adb9a-eab8-46fd-e987-ab0542df614d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# ::snt Bob found the pen on the table.\n",
            "(f / find-01\n",
            "   :ARG0 (p / person\n",
            "            :name (n / name\n",
            "                     :op1 \"Bob\"))\n",
            "   :ARG1 (p2 / pen)\n",
            "   :location-under (t / table))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Bob found a pen under the table.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Add a magnet to the sentence, so Bob can use it\n",
        "pgraph.triples.append((\"u\",\":instance\",\"use-01\"))\n",
        "pgraph.triples.append((\"m\",\":instance\",\"karate moves\"))\n",
        "pgraph.triples.append((\"f\",\":manner\",\"u\"))\n",
        "pgraph.triples.append((\"u\",\":ARG0\",\"p\"))\n",
        "pgraph.triples.append((\"u\",\":ARG1\",\"m\"))\n",
        "\n",
        "new_amr_string = penman.encode(pgraph)\n",
        "print(new_amr_string)\n",
        "gtos.generate([new_amr_string,])[0][0]"
      ],
      "metadata": {
        "id": "NDf5Eu6tRy-f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "c9fc4fea-51b3-4a9b-c09e-e16758efcb7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:amrlib.models.generate_t5wtense.inference:Unable to add tense information to graph\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# ::snt Bob found the pen on the table.\n",
            "(f / find-01\n",
            "   :ARG0 (p / person\n",
            "            :name (n / name\n",
            "                     :op1 \"Bob\"))\n",
            "   :ARG1 (p2 / pen)\n",
            "   :location-under (t / table)\n",
            "   :manner (u / use-01\n",
            "              :ARG0 p\n",
            "              :ARG1 (m / karate moves)))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Bob found a pen under the table using karate moves.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RVGTyazlM0PV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# N-grams and similarity"
      ],
      "metadata": {
        "id": "Um4Lrs5rVugc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Download a larger text corpus\n",
        "def download_from_drive(link, filename):\n",
        "  id = link.split(\"/\")[-2]\n",
        "  !wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id={id}' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id={id}\" -O {\"/content/\"+filename} && rm -rf /tmp/cookies.txt\n",
        "\n",
        "download_from_drive(\"https://drive.google.com/file/d/1dqiWtQuykbraWeE01masIygj35ZdVrOu/view?usp=sharing\", \"imdb.txt\")\n"
      ],
      "metadata": {
        "id": "y3Aps_q-SWCO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6efa1e29-cc86-4f0e-a73e-58da2703faa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-02 14:22:19--  https://docs.google.com/uc?export=download&confirm=&id=1dqiWtQuykbraWeE01masIygj35ZdVrOu\n",
            "Resolving docs.google.com (docs.google.com)... 142.251.120.101, 142.251.120.113, 142.251.120.139, ...\n",
            "Connecting to docs.google.com (docs.google.com)|142.251.120.101|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-0c-1k-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/spkquv54uiokcvjug94rbsrt02n74ubv/1683037275000/02440328774490686871/*/1dqiWtQuykbraWeE01masIygj35ZdVrOu?e=download&uuid=5164e244-d059-4073-8bf3-33c45280ff2a [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2023-05-02 14:22:21--  https://doc-0c-1k-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/spkquv54uiokcvjug94rbsrt02n74ubv/1683037275000/02440328774490686871/*/1dqiWtQuykbraWeE01masIygj35ZdVrOu?e=download&uuid=5164e244-d059-4073-8bf3-33c45280ff2a\n",
            "Resolving doc-0c-1k-docs.googleusercontent.com (doc-0c-1k-docs.googleusercontent.com)... 74.125.132.132, 2607:f8b0:4001:c00::84\n",
            "Connecting to doc-0c-1k-docs.googleusercontent.com (doc-0c-1k-docs.googleusercontent.com)|74.125.132.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 35435236 (34M) [text/plain]\n",
            "Saving to: ‘/content/imdb.txt’\n",
            "\n",
            "/content/imdb.txt   100%[===================>]  33.79M   156MB/s    in 0.2s    \n",
            "\n",
            "2023-05-02 14:22:21 (156 MB/s) - ‘/content/imdb.txt’ saved [35435236/35435236]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "7dwhe_bNVuVJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/imdb.txt\",sep=\" ::: \",names=[\"ID\",\"title\",\"genre\",\"description\"])\n",
        "df.head()"
      ],
      "metadata": {
        "id": "9pL_UmWmVsrV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "24f09a43-c890-47da-8821-8b7f7f8eb12a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-149-85455d0595d8>:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  df = pd.read_csv(\"/content/imdb.txt\",sep=\" ::: \",names=[\"ID\",\"title\",\"genre\",\"description\"])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ID                             title     genre  \\\n",
              "0   1      Oscar et la dame rose (2009)     drama   \n",
              "1   2                      Cupid (1997)  thriller   \n",
              "2   3  Young, Wild and Wonderful (1980)     adult   \n",
              "3   4             The Secret Sin (1915)     drama   \n",
              "4   5            The Unrecovered (2007)     drama   \n",
              "\n",
              "                                         description  \n",
              "0  Listening in to a conversation between his doc...  \n",
              "1  A brother and sister with a past incestuous re...  \n",
              "2  As the bus empties the students for their fiel...  \n",
              "3  To help their unemployed father make ends meet...  \n",
              "4  The film's title refers not only to the un-rec...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6ba08779-d7f6-45c9-b673-9accbce0c6f2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>title</th>\n",
              "      <th>genre</th>\n",
              "      <th>description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Oscar et la dame rose (2009)</td>\n",
              "      <td>drama</td>\n",
              "      <td>Listening in to a conversation between his doc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Cupid (1997)</td>\n",
              "      <td>thriller</td>\n",
              "      <td>A brother and sister with a past incestuous re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Young, Wild and Wonderful (1980)</td>\n",
              "      <td>adult</td>\n",
              "      <td>As the bus empties the students for their fiel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>The Secret Sin (1915)</td>\n",
              "      <td>drama</td>\n",
              "      <td>To help their unemployed father make ends meet...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>The Unrecovered (2007)</td>\n",
              "      <td>drama</td>\n",
              "      <td>The film's title refers not only to the un-rec...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ba08779-d7f6-45c9-b673-9accbce0c6f2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ba08779-d7f6-45c9-b673-9accbce0c6f2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ba08779-d7f6-45c9-b673-9accbce0c6f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop([\"ID\",\"title\"], axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "id": "ad7VPjdnaD7X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "bd5e39bc-4468-462d-f73c-d83b84afbe6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      genre                                        description\n",
              "0     drama  Listening in to a conversation between his doc...\n",
              "1  thriller  A brother and sister with a past incestuous re...\n",
              "2     adult  As the bus empties the students for their fiel...\n",
              "3     drama  To help their unemployed father make ends meet...\n",
              "4     drama  The film's title refers not only to the un-rec..."
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>genre</th>\n",
              "      <th>description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>drama</td>\n",
              "      <td>Listening in to a conversation between his doc...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>adult</td>\n",
              "      <td>As the bus empties the students for their fiel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>drama</td>\n",
              "      <td>To help their unemployed father make ends meet...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>drama</td>\n",
              "      <td>The film's title refers not only to the un-rec...</td>\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-a71223b9-6fca-4a69-a7d4-3f9a0f7156b0 button.colab-df-convert');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Statistics\n",
        "df.groupby(\"genre\").count()"
      ],
      "metadata": {
        "id": "fgNSprWTam2L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "bf0ff324-d87c-4ff8-8c4d-640e091a314a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             description\n",
              "genre                   \n",
              "action              1315\n",
              "adult                590\n",
              "adventure            775\n",
              "animation            498\n",
              "biography            265\n",
              "comedy              7447\n",
              "crime                505\n",
              "documentary        13096\n",
              "drama              13613\n",
              "family               784\n",
              "fantasy              323\n",
              "game-show            194\n",
              "history              243\n",
              "horror              2204\n",
              "music                731\n",
              "musical              277\n",
              "mystery              319\n",
              "news                 181\n",
              "reality-tv           884\n",
              "romance              672\n",
              "sci-fi               647\n",
              "short               5073\n",
              "sport                432\n",
              "talk-show            391\n",
              "thriller            1591\n",
              "war                  132\n",
              "western             1032"
            ],
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              "      <th>biography</th>\n",
              "      <td>265</td>\n",
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              "      <td>7447</td>\n",
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              "      <td>2204</td>\n",
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              "      <th>music</th>\n",
              "      <td>731</td>\n",
              "    </tr>\n",
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              "      <th>musical</th>\n",
              "      <td>277</td>\n",
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              "      <td>319</td>\n",
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              "      <th>reality-tv</th>\n",
              "      <td>884</td>\n",
              "    </tr>\n",
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              "      <td>647</td>\n",
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              "      <th>short</th>\n",
              "      <td>5073</td>\n",
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              "    <tr>\n",
              "      <th>sport</th>\n",
              "      <td>432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>talk-show</th>\n",
              "      <td>391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thriller</th>\n",
              "      <td>1591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>war</th>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>western</th>\n",
              "      <td>1032</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1615af26-ca5f-40ba-a67b-c3068edbf5d8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Keep western, thriller and action\n",
        "\n",
        "df = df[df[\"genre\"].isin([\"western\",\"thriller\",\"action\"])]\n",
        "df.head()"
      ],
      "metadata": {
        "id": "oZgzfVZNbCRz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "76280001-01d5-432e-a3c2-59813c83397b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       genre                                        description\n",
              "1   thriller  A brother and sister with a past incestuous re...\n",
              "13  thriller  A photographer is sailing with his wife, her s...\n",
              "24    action  Dong-woo is released from prison after a short...\n",
              "28  thriller  Marie is an insecure housewife whose husband, ...\n",
              "32    action  Thai Town is a six-block area near Hollywood, ..."
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f4985fbf-899e-4372-b8aa-939b96d41afe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f4985fbf-899e-4372-b8aa-939b96d41afe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thr_text = \" \".join(df[\"description\"][df[\"genre\"]==\"thriller\"].to_list())\n",
        "act_text = \" \".join(df[\"description\"][df[\"genre\"]==\"action\"].to_list())\n",
        "west_text = \" \".join(df[\"description\"][df[\"genre\"]==\"western\"].to_list())"
      ],
      "metadata": {
        "id": "SyPOPmckbbsu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from collections import Counter\n",
        "def sent2ngrams_regex(text, n):\n",
        "    rgx = '(?=('+'\\S'*n+'))'\n",
        "    return re.findall(rgx,text)\n",
        "\n",
        "def getTopNgramSignature(text, nmin, nmax, topn):\n",
        "\n",
        "  counters = []\n",
        "  for i in range(nmin, nmax+1):\n",
        "    grams = sent2ngrams_regex(text.lower(),i)\n",
        "    cnt = Counter(grams)\n",
        "    sumgrams=len(grams)\n",
        "    for gram in cnt:\n",
        "      cnt[gram] /= sumgrams\n",
        "    counters.append(cnt)\n",
        "\n",
        "  cnt = Counter()\n",
        "  for counter in counters:\n",
        "    cnt += counter\n",
        "  \n",
        "  commons = cnt.most_common(topn)\n",
        "  ngrams = [common[0] for common in commons]\n",
        "  counts = [common[1] for common in commons]\n",
        "  return ngrams, counts\n"
      ],
      "metadata": {
        "id": "4lEHIwbscQUa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(sent2ngrams_regex(\"This is it.\",3))"
      ],
      "metadata": {
        "id": "UYKkGRD6qLB3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a9f1a04-7505-49ff-bbfe-46c69883dfdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Thi', 'his', 'it.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maxused = 20\n",
        "ngrams, counts = getTopNgramSignature(act_text, 2, 5, maxused)"
      ],
      "metadata": {
        "id": "mV46VRC2e6cr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(14,6))\n",
        "plt.plot(counts)\n",
        "plt.xticks(range(maxused),ngrams)\n",
        "plt.grid()\n",
        "print(ngrams)"
      ],
      "metadata": {
        "id": "2qrHrV6pdKL4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "99b3b8ac-3b3b-490a-86a7-4a2efef85c5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['th', 'he', 'the', 'in', 'an', 'er', 'nd', 'is', 'and', 'on', 'to', 're', 'es', 'en', 'ng', 'ing', 'hi', 'ar', 'or', 'st']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Zipf-law"
      ],
      "metadata": {
        "id": "j9_bx8bDQxrz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rank distance measures:\n",
        "\n",
        "- At the same index: 0\n",
        "- At different index: abs(i1-i2)\n",
        "- Does not exist: Maximal value (array length)"
      ],
      "metadata": {
        "id": "sE4eMe-9j3pL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def rankDiff(profile, doc):\n",
        "  assert len(doc)==len(profile)\n",
        "  diff = 0\n",
        "  for i in range(len(doc)):\n",
        "    if doc[i] in profile:\n",
        "      j = profile.index(doc[i])\n",
        "      diff += abs(i-j)\n",
        "    else:\n",
        "      diff += len(profile)\n",
        "  \n",
        "  return diff"
      ],
      "metadata": {
        "id": "O0cz4aM7eFgU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "maxused = 250\n",
        "thr_profile, _ = getTopNgramSignature(thr_text, 2,5, maxused)\n",
        "act_profile, _ = getTopNgramSignature(act_text, 2,5, maxused)\n",
        "west_profile, _ = getTopNgramSignature(west_text, 2,5, maxused)"
      ],
      "metadata": {
        "id": "eiq0ve3Xmqg1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def categorize(doc_profile):\n",
        "  distances = np.array([rankDiff(thr_profile, doc_profile), rankDiff(act_profile, doc_profile), rankDiff(west_profile, doc_profile)])\n",
        "  genres = [\"thriller\",\"action\",\"western\"]\n",
        "  bestidx = np.argmin(distances)\n",
        "  return genres[bestidx], distances"
      ],
      "metadata": {
        "id": "KtW-cElbm6Kx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Spider-Man 2021\n",
        "doc = \"With Spider-Man's identity now revealed, Peter asks Doctor Strange for help. When a spell goes wrong, dangerous foes from other worlds start to appear, forcing Peter to discover what it truly means to be Spider-Man.\"\n",
        "doc_profile, _ = getTopNgramSignature(doc, 2,5, maxused)\n",
        "categorize(doc_profile)"
      ],
      "metadata": {
        "id": "Qs_pzQ20nZPS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfb037e0-2d78-45a7-8cb8-440c7a2eabfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('action', array([51753, 50941, 51152]))"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#The Power of The Dog 2021\n",
        "doc = \"Charismatic rancher Phil Burbank inspires fear and awe in those around him. When his brother brings home a new wife and her son, Phil torments them until he finds himself exposed to the possibility of love.\"\n",
        "doc_profile, _ = getTopNgramSignature(doc, 2,5, maxused)\n",
        "categorize(doc_profile)"
      ],
      "metadata": {
        "id": "86HifJ95oEUQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40fbbd06-b49d-469c-8712-a68a7829909f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('western', array([51569, 51450, 50377]))"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Scream 2022\n",
        "doc = \"Twenty-five years after the original series of murders in Woodsboro, a new Ghostface emerges, and Sidney Prescott must return to uncover the truth.\"\n",
        "doc_profile, _ = getTopNgramSignature(doc, 2,5, maxused)\n",
        "categorize(doc_profile)"
      ],
      "metadata": {
        "id": "WyHdFMsIpCoq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66860be4-0d0d-483f-abf4-9158738250da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('thriller', array([52240, 52801, 52673]))"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vp8-D-XypYnA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}