Lab05_Santiago_del_Rey_Juarez_Nikita_Belooussov.ipynb

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      "name": "Lab05-Santiago-del-Rey-Juarez_Nikita-Belooussov.ipynb",
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      "collapsed_sections": []
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      "hash": "56b22a985f18d44d50171c67439d384ccb9cd51faf15dc17dcf099ae374960d9"
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      "display_name": "Python 3.9.7 64-bit ('.venv': poetry)",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2jMkKylwoeys"
      },
      "source": [
        "Written by Santiago del Rey Juarez and Nikita Belooussov\n",
        "\n",
        "Given the following (lemma, category) pairs:\n",
        "\n",
        "```python\n",
        "(’the’,’DT’), (’man’,’NN’), (’swim’,’VB’), (’with’, ’PR’), (’a’, ’DT’),\n",
        "(’girl’,’NN’), (’and’, ’CC’), (’a’, ’DT’), (’boy’, ’NN’), (’whilst’, ’PR’),\n",
        "(’the’, ’DT’), (’woman’, ’NN’), (’walk’, ’VB’)\n",
        "```\n",
        "\n",
        "For each pair, when possible, print their most frequent WordNet synset, their corresponding least common subsumer (LCS) and their similarity value, using the following functions:\n",
        "\n",
        "* Path Similarity\n",
        "* Leacock-Chodorow Similarity\n",
        "* Wu-Palmer Similarity\n",
        "* Lin Similarity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Nv-sb04fXE1",
        "outputId": "a35cc345-8843-4a63-f109-6db1d60463c9"
      },
      "source": [
        "import nltk\n",
        "import numpy as np\n",
        "nltk.download('wordnet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to\n",
            "[nltk_data]     C:\\Users\\santi\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 187,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9JQ7tDdefeLz",
        "outputId": "7b3d0f2f-320f-413c-9830-1c68c735ba88"
      },
      "source": [
        "nltk.download('wordnet_ic')\n",
        "from nltk.corpus import wordnet as wn\n",
        "from nltk.corpus import wordnet_ic\n",
        "brown_ic=wordnet_ic.ic('ic-brown.dat')\n",
        "words=[('the','DT'), ('man','NN'), ('swim','VB'), ('with', 'PR'), ('a', 'DT'),\n",
        "('girl','NN'), ('and', 'CC'), ('a', 'DT'), ('boy', 'NN'), ('whilst', 'PR'),\n",
        "('the', 'DT'), ('woman', 'NN'), ('walk', 'VB'),('dog','NN'),('table','NN'),('lamp','NN')]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet_ic to\n",
            "[nltk_data]     C:\\Users\\santi\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Package wordnet_ic is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "38Tue4SCgP-r",
        "outputId": "d8f9fc7a-8f99-4e6b-a310-f74739fb4edf"
      },
      "source": [
        "syn=[]\n",
        "for word in words:\n",
        "    try:\n",
        "        syn.append(wn.synsets(word[0],word[1].lower()[0])[0])\n",
        "        print(f\"Most common synset of {word[0]}:\")\n",
        "        print(wn.synsets(word[0],word[1].lower()[0])[0])\n",
        "        print (\"\\n\")\n",
        "    except:\n",
        "        pass"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Most common synset of man:\n",
            "Synset('man.n.01')\n",
            "\n",
            "\n",
            "Most common synset of swim:\n",
            "Synset('swim.v.01')\n",
            "\n",
            "\n",
            "Most common synset of girl:\n",
            "Synset('girl.n.01')\n",
            "\n",
            "\n",
            "Most common synset of boy:\n",
            "Synset('male_child.n.01')\n",
            "\n",
            "\n",
            "Most common synset of woman:\n",
            "Synset('woman.n.01')\n",
            "\n",
            "\n",
            "Most common synset of walk:\n",
            "Synset('walk.v.01')\n",
            "\n",
            "\n",
            "Most common synset of dog:\n",
            "Synset('dog.n.01')\n",
            "\n",
            "\n",
            "Most common synset of table:\n",
            "Synset('table.n.01')\n",
            "\n",
            "\n",
            "Most common synset of lamp:\n",
            "Synset('lamp.n.01')\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 1000
        },
        "id": "XTwOXL2djLQ5",
        "outputId": "72bdffe4-c3a1-48d2-fe78-cfc5142db431"
      },
      "source": [
        "pSimA=[round(syn[0].path_similarity(syn[0]),3)]\n",
        "wSimA=[round(syn[0].wup_similarity(syn[0]),3)]\n",
        "liSimA=[round(syn[0].lin_similarity(syn[0],brown_ic),3)]\n",
        "lSimA=[round(syn[0].lch_similarity(syn[0]),3)]\n",
        "\n",
        "for i,word in enumerate(syn):\n",
        "    for j,word2 in enumerate(syn[i+1:]):\n",
        "        print(\"\\nPath Similarity:\")\n",
        "        pSim=round(word.path_similarity(word2),3)\n",
        "        pSimA.append(round(word.path_similarity(word2),3))\n",
        "        print(str(word)+\" \"+str(word2))\n",
        "        print(pSim)\n",
        "\n",
        "        print(\"\\nLeacock Chodorow Similarity:\")\n",
        "        try:\n",
        "            lSim=round(word.lch_similarity(word2)/lSimA[0],3)\n",
        "            lSimA.append(round(word.lch_similarity(word2)/lSimA[0],3))\n",
        "            print(str(word)+\" \"+str(word2))\n",
        "            print(lSim)\n",
        "        except:\n",
        "            lSimA.append(0)\n",
        "            print(\"None\")\n",
        "\n",
        "        print(\"\\nWu-Palmer Similarity:\")\n",
        "        wSim=round(word.wup_similarity(word2),3)\n",
        "        wSimA.append(round(word.wup_similarity(word2),3))\n",
        "        print(str(word)+\" \"+str(word2))\n",
        "        print(wSim)\n",
        "\n",
        "        print(\"\\nlin Similarity:\")\n",
        "        try:\n",
        "            liSim=round(word.lin_similarity(word2,brown_ic),3)\n",
        "            liSimA.append(round(word.lin_similarity(word2,brown_ic),3))\n",
        "            print(str(word)+\" \"+str(word2))\n",
        "            print(pSim)\n",
        "        except:\n",
        "            liSimA.append(0)\n",
        "            print(\"None\")\n",
        "        \n",
        "        print(\"Least common subsumer\")\n",
        "        print(word.lowest_common_hypernyms(word2))\n",
        "\n",
        "        print(\"\\n\\n\")\n",
        "\n",
        "print (\"Normalized Vectors:\")\n",
        "print([pSimA,wSimA,liSimA,lSimA])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Path Similarity:\n",
            "Synset('man.n.01') Synset('swim.v.01')\n",
            "0.1\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('man.n.01') Synset('swim.v.01')\n",
            "0.182\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('man.n.01') Synset('girl.n.01')\n",
            "0.25\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('man.n.01') Synset('girl.n.01')\n",
            "0.619\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('man.n.01') Synset('girl.n.01')\n",
            "0.632\n",
            "\n",
            "lin Similarity:\n",
            "Synset('man.n.01') Synset('girl.n.01')\n",
            "0.25\n",
            "Least common subsumer\n",
            "[Synset('adult.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('man.n.01') Synset('male_child.n.01')\n",
            "0.333\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('man.n.01') Synset('male_child.n.01')\n",
            "0.698\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('man.n.01') Synset('male_child.n.01')\n",
            "0.667\n",
            "\n",
            "lin Similarity:\n",
            "Synset('man.n.01') Synset('male_child.n.01')\n",
            "0.333\n",
            "Least common subsumer\n",
            "[Synset('male.n.02')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('man.n.01') Synset('woman.n.01')\n",
            "0.333\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('man.n.01') Synset('woman.n.01')\n",
            "0.698\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('man.n.01') Synset('woman.n.01')\n",
            "0.667\n",
            "\n",
            "lin Similarity:\n",
            "Synset('man.n.01') Synset('woman.n.01')\n",
            "0.333\n",
            "Least common subsumer\n",
            "[Synset('adult.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('man.n.01') Synset('walk.v.01')\n",
            "0.1\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('man.n.01') Synset('walk.v.01')\n",
            "0.182\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('man.n.01') Synset('dog.n.01')\n",
            "0.143\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('man.n.01') Synset('dog.n.01')\n",
            "0.465\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('man.n.01') Synset('dog.n.01')\n",
            "0.667\n",
            "\n",
            "lin Similarity:\n",
            "Synset('man.n.01') Synset('dog.n.01')\n",
            "0.143\n",
            "Least common subsumer\n",
            "[Synset('organism.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('man.n.01') Synset('table.n.01')\n",
            "0.091\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('man.n.01') Synset('table.n.01')\n",
            "0.341\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('man.n.01') Synset('table.n.01')\n",
            "0.167\n",
            "\n",
            "lin Similarity:\n",
            "Synset('man.n.01') Synset('table.n.01')\n",
            "0.091\n",
            "Least common subsumer\n",
            "[Synset('entity.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('man.n.01') Synset('lamp.n.01')\n",
            "0.091\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('man.n.01') Synset('lamp.n.01')\n",
            "0.341\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('man.n.01') Synset('lamp.n.01')\n",
            "0.444\n",
            "\n",
            "lin Similarity:\n",
            "Synset('man.n.01') Synset('lamp.n.01')\n",
            "0.091\n",
            "Least common subsumer\n",
            "[Synset('whole.n.02')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('swim.v.01') Synset('girl.n.01')\n",
            "0.091\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('swim.v.01') Synset('girl.n.01')\n",
            "0.167\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('swim.v.01') Synset('male_child.n.01')\n",
            "0.1\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('swim.v.01') Synset('male_child.n.01')\n",
            "0.182\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('swim.v.01') Synset('woman.n.01')\n",
            "0.1\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('swim.v.01') Synset('woman.n.01')\n",
            "0.182\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('swim.v.01') Synset('walk.v.01')\n",
            "0.333\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('swim.v.01') Synset('walk.v.01')\n",
            "0.594\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('swim.v.01') Synset('walk.v.01')\n",
            "0.333\n",
            "\n",
            "lin Similarity:\n",
            "Synset('swim.v.01') Synset('walk.v.01')\n",
            "0.333\n",
            "Least common subsumer\n",
            "[Synset('travel.v.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('swim.v.01') Synset('dog.n.01')\n",
            "0.083\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('swim.v.01') Synset('dog.n.01')\n",
            "0.154\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('swim.v.01') Synset('table.n.01')\n",
            "0.111\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('swim.v.01') Synset('table.n.01')\n",
            "0.2\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('swim.v.01') Synset('lamp.n.01')\n",
            "0.083\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('swim.v.01') Synset('lamp.n.01')\n",
            "0.154\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('girl.n.01') Synset('male_child.n.01')\n",
            "0.167\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('girl.n.01') Synset('male_child.n.01')\n",
            "0.507\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('girl.n.01') Synset('male_child.n.01')\n",
            "0.632\n",
            "\n",
            "lin Similarity:\n",
            "Synset('girl.n.01') Synset('male_child.n.01')\n",
            "0.167\n",
            "Least common subsumer\n",
            "[Synset('person.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('girl.n.01') Synset('woman.n.01')\n",
            "0.5\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('girl.n.01') Synset('woman.n.01')\n",
            "0.809\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('girl.n.01') Synset('woman.n.01')\n",
            "0.632\n",
            "\n",
            "lin Similarity:\n",
            "Synset('girl.n.01') Synset('woman.n.01')\n",
            "0.5\n",
            "Least common subsumer\n",
            "[Synset('woman.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('girl.n.01') Synset('walk.v.01')\n",
            "0.091\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('girl.n.01') Synset('walk.v.01')\n",
            "0.167\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('girl.n.01') Synset('dog.n.01')\n",
            "0.125\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('girl.n.01') Synset('dog.n.01')\n",
            "0.428\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('girl.n.01') Synset('dog.n.01')\n",
            "0.632\n",
            "\n",
            "lin Similarity:\n",
            "Synset('girl.n.01') Synset('dog.n.01')\n",
            "0.125\n",
            "Least common subsumer\n",
            "[Synset('organism.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('girl.n.01') Synset('table.n.01')\n",
            "0.083\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('girl.n.01') Synset('table.n.01')\n",
            "0.317\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('girl.n.01') Synset('table.n.01')\n",
            "0.154\n",
            "\n",
            "lin Similarity:\n",
            "Synset('girl.n.01') Synset('table.n.01')\n",
            "0.083\n",
            "Least common subsumer\n",
            "[Synset('entity.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('girl.n.01') Synset('lamp.n.01')\n",
            "0.083\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('girl.n.01') Synset('lamp.n.01')\n",
            "0.317\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('girl.n.01') Synset('lamp.n.01')\n",
            "0.421\n",
            "\n",
            "lin Similarity:\n",
            "Synset('girl.n.01') Synset('lamp.n.01')\n",
            "0.083\n",
            "Least common subsumer\n",
            "[Synset('whole.n.02')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('male_child.n.01') Synset('woman.n.01')\n",
            "0.2\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('male_child.n.01') Synset('woman.n.01')\n",
            "0.557\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('male_child.n.01') Synset('woman.n.01')\n",
            "0.667\n",
            "\n",
            "lin Similarity:\n",
            "Synset('male_child.n.01') Synset('woman.n.01')\n",
            "0.2\n",
            "Least common subsumer\n",
            "[Synset('person.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('male_child.n.01') Synset('walk.v.01')\n",
            "0.1\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('male_child.n.01') Synset('walk.v.01')\n",
            "0.182\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('male_child.n.01') Synset('dog.n.01')\n",
            "0.143\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('male_child.n.01') Synset('dog.n.01')\n",
            "0.465\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('male_child.n.01') Synset('dog.n.01')\n",
            "0.667\n",
            "\n",
            "lin Similarity:\n",
            "Synset('male_child.n.01') Synset('dog.n.01')\n",
            "0.143\n",
            "Least common subsumer\n",
            "[Synset('organism.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('male_child.n.01') Synset('table.n.01')\n",
            "0.091\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('male_child.n.01') Synset('table.n.01')\n",
            "0.341\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('male_child.n.01') Synset('table.n.01')\n",
            "0.167\n",
            "\n",
            "lin Similarity:\n",
            "Synset('male_child.n.01') Synset('table.n.01')\n",
            "0.091\n",
            "Least common subsumer\n",
            "[Synset('entity.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('male_child.n.01') Synset('lamp.n.01')\n",
            "0.091\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('male_child.n.01') Synset('lamp.n.01')\n",
            "0.341\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('male_child.n.01') Synset('lamp.n.01')\n",
            "0.444\n",
            "\n",
            "lin Similarity:\n",
            "Synset('male_child.n.01') Synset('lamp.n.01')\n",
            "0.091\n",
            "Least common subsumer\n",
            "[Synset('whole.n.02')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('woman.n.01') Synset('walk.v.01')\n",
            "0.1\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('woman.n.01') Synset('walk.v.01')\n",
            "0.182\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('woman.n.01') Synset('dog.n.01')\n",
            "0.143\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('woman.n.01') Synset('dog.n.01')\n",
            "0.465\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('woman.n.01') Synset('dog.n.01')\n",
            "0.667\n",
            "\n",
            "lin Similarity:\n",
            "Synset('woman.n.01') Synset('dog.n.01')\n",
            "0.143\n",
            "Least common subsumer\n",
            "[Synset('organism.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('woman.n.01') Synset('table.n.01')\n",
            "0.091\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('woman.n.01') Synset('table.n.01')\n",
            "0.341\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('woman.n.01') Synset('table.n.01')\n",
            "0.167\n",
            "\n",
            "lin Similarity:\n",
            "Synset('woman.n.01') Synset('table.n.01')\n",
            "0.091\n",
            "Least common subsumer\n",
            "[Synset('entity.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('woman.n.01') Synset('lamp.n.01')\n",
            "0.091\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('woman.n.01') Synset('lamp.n.01')\n",
            "0.341\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('woman.n.01') Synset('lamp.n.01')\n",
            "0.444\n",
            "\n",
            "lin Similarity:\n",
            "Synset('woman.n.01') Synset('lamp.n.01')\n",
            "0.091\n",
            "Least common subsumer\n",
            "[Synset('whole.n.02')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('walk.v.01') Synset('dog.n.01')\n",
            "0.083\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('walk.v.01') Synset('dog.n.01')\n",
            "0.154\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('walk.v.01') Synset('table.n.01')\n",
            "0.111\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('walk.v.01') Synset('table.n.01')\n",
            "0.2\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('walk.v.01') Synset('lamp.n.01')\n",
            "0.083\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "None\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('walk.v.01') Synset('lamp.n.01')\n",
            "0.154\n",
            "\n",
            "lin Similarity:\n",
            "None\n",
            "Least common subsumer\n",
            "[]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('dog.n.01') Synset('table.n.01')\n",
            "0.071\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('dog.n.01') Synset('table.n.01')\n",
            "0.274\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('dog.n.01') Synset('table.n.01')\n",
            "0.133\n",
            "\n",
            "lin Similarity:\n",
            "Synset('dog.n.01') Synset('table.n.01')\n",
            "0.071\n",
            "Least common subsumer\n",
            "[Synset('entity.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('dog.n.01') Synset('lamp.n.01')\n",
            "0.091\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('dog.n.01') Synset('lamp.n.01')\n",
            "0.341\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('dog.n.01') Synset('lamp.n.01')\n",
            "0.444\n",
            "\n",
            "lin Similarity:\n",
            "Synset('dog.n.01') Synset('lamp.n.01')\n",
            "0.091\n",
            "Least common subsumer\n",
            "[Synset('whole.n.02')]\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Path Similarity:\n",
            "Synset('table.n.01') Synset('lamp.n.01')\n",
            "0.071\n",
            "\n",
            "Leacock Chodorow Similarity:\n",
            "Synset('table.n.01') Synset('lamp.n.01')\n",
            "0.274\n",
            "\n",
            "Wu-Palmer Similarity:\n",
            "Synset('table.n.01') Synset('lamp.n.01')\n",
            "0.133\n",
            "\n",
            "lin Similarity:\n",
            "Synset('table.n.01') Synset('lamp.n.01')\n",
            "0.071\n",
            "Least common subsumer\n",
            "[Synset('entity.n.01')]\n",
            "\n",
            "\n",
            "\n",
            "Normalized Vectors:\n",
            "[[1.0, 0.1, 0.25, 0.333, 0.333, 0.1, 0.143, 0.091, 0.091, 0.091, 0.1, 0.1, 0.333, 0.083, 0.111, 0.083, 0.167, 0.5, 0.091, 0.125, 0.083, 0.083, 0.2, 0.1, 0.143, 0.091, 0.091, 0.1, 0.143, 0.091, 0.091, 0.083, 0.111, 0.083, 0.071, 0.091, 0.071], [1.0, 0.182, 0.632, 0.667, 0.667, 0.182, 0.667, 0.167, 0.444, 0.167, 0.182, 0.182, 0.333, 0.154, 0.2, 0.154, 0.632, 0.632, 0.167, 0.632, 0.154, 0.421, 0.667, 0.182, 0.667, 0.167, 0.444, 0.182, 0.667, 0.167, 0.444, 0.154, 0.2, 0.154, 0.133, 0.444, 0.133], [1.0, 0, 0.714, 0.729, 0.787, 0, 0.292, -0.0, 0.2, 0, 0, 0, 0.491, 0, 0, 0, 0.293, 0.907, 0, 0.269, -0.0, 0.184, 0.318, 0, 0.256, -0.0, 0.175, 0, 0.291, -0.0, 0.199, 0, 0, 0, -0.0, 0.169, -0.0], [3.638, 0, 0.619, 0.698, 0.698, 0, 0.465, 0.341, 0.341, 0, 0, 0, 0.594, 0, 0, 0, 0.507, 0.809, 0, 0.428, 0.317, 0.317, 0.557, 0, 0.465, 0.341, 0.341, 0, 0.465, 0.341, 0.341, 0, 0, 0, 0.274, 0.341, 0.274]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxNAuDkWsOUK"
      },
      "source": [
        "size = len(syn)\n",
        "\n",
        "subsumerMat =[[None]*size for _ in range(size)]\n",
        "pSimMat=np.ndarray((size,size))\n",
        "wSimMat=np.ndarray((size,size))\n",
        "lSimMat=np.ndarray((size,size))\n",
        "liSimMat=np.ndarray((size,size))\n",
        "\n",
        "for i,word in enumerate(syn):\n",
        "    for j,word2 in enumerate(syn):\n",
        "        pSim=word.path_similarity(word2)\n",
        "        pSimMat[i,j]=pSim\n",
        "\n",
        "        try:\n",
        "            lSim=word.lch_similarity(word2)/lSimA[0]\n",
        "            lSimMat[i,j]=lSim\n",
        "        except:\n",
        "            lSimMat[i,j]=0\n",
        "\n",
        "        wSim=word.wup_similarity(word2)\n",
        "        wSimMat[i,j]=wSim\n",
        "\n",
        "        try:\n",
        "            liSim=word.lin_similarity(word2,brown_ic)\n",
        "            liSimMat[i,j]=liSim\n",
        "        except:\n",
        "            liSimMat[i,j]=0\n",
        "        \n",
        "        subsumers = word.lowest_common_hypernyms(word2)\n",
        "        if len(subsumers) > 0:\n",
        "            subsumerMat[i][j] = subsumers[0].name()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9d1jgHAsOUL",
        "outputId": "46aa39c8-5b69-4593-e265-6078a21bd723"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "word_list = [\"man\", \"swim\", \"girl\", \"boy\", \"woman\", \"walk\", \"dog\", \"table\", \"lamp\"]\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
        "\n",
        "df_subsummer = pd.DataFrame(subsumerMat, index = word_list, columns = word_list)\n",
        "print(\"Matrix of least common subsumers\")\n",
        "print(df_subsummer)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matrix of least common subsumers\n",
            "                 man         swim           girl              boy  \\\n",
            "man         man.n.01         None     adult.n.01        male.n.02   \n",
            "swim            None    swim.v.01           None             None   \n",
            "girl      adult.n.01         None      girl.n.01      person.n.01   \n",
            "boy        male.n.02         None    person.n.01  male_child.n.01   \n",
            "woman     adult.n.01         None     woman.n.01      person.n.01   \n",
            "walk            None  travel.v.01           None             None   \n",
            "dog    organism.n.01         None  organism.n.01    organism.n.01   \n",
            "table    entity.n.01         None    entity.n.01      entity.n.01   \n",
            "lamp      whole.n.02         None     whole.n.02       whole.n.02   \n",
            "\n",
            "               woman         walk            dog        table         lamp  \n",
            "man       adult.n.01         None  organism.n.01  entity.n.01   whole.n.02  \n",
            "swim            None  travel.v.01           None         None         None  \n",
            "girl      woman.n.01         None  organism.n.01  entity.n.01   whole.n.02  \n",
            "boy      person.n.01         None  organism.n.01  entity.n.01   whole.n.02  \n",
            "woman     woman.n.01         None  organism.n.01  entity.n.01   whole.n.02  \n",
            "walk            None    walk.v.01           None         None         None  \n",
            "dog    organism.n.01         None       dog.n.01  entity.n.01   whole.n.02  \n",
            "table    entity.n.01         None    entity.n.01   table.n.01  entity.n.01  \n",
            "lamp      whole.n.02         None     whole.n.02  entity.n.01    lamp.n.01  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpbGA5JlsOUM",
        "outputId": "258ff29d-d4c9-4075-e136-8e2a5e281440"
      },
      "source": [
        "df_path = pd.DataFrame(pSimMat, index = word_list, columns = word_list)\n",
        "print(\"Matrix of Path Similarity Method\")\n",
        "print(df_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matrix of Path Similarity Method\n",
            "        man  swim  girl   boy  woman  walk   dog  table  lamp\n",
            "man   1.000 0.100 0.250 0.333  0.333 0.100 0.143  0.091 0.091\n",
            "swim  0.100 1.000 0.091 0.100  0.100 0.333 0.083  0.111 0.083\n",
            "girl  0.250 0.091 1.000 0.167  0.500 0.091 0.125  0.083 0.083\n",
            "boy   0.333 0.100 0.167 1.000  0.200 0.100 0.143  0.091 0.091\n",
            "woman 0.333 0.100 0.500 0.200  1.000 0.100 0.143  0.091 0.091\n",
            "walk  0.100 0.333 0.091 0.100  0.100 1.000 0.083  0.111 0.083\n",
            "dog   0.143 0.083 0.125 0.143  0.143 0.083 1.000  0.071 0.091\n",
            "table 0.091 0.111 0.083 0.091  0.091 0.111 0.071  1.000 0.071\n",
            "lamp  0.091 0.083 0.083 0.091  0.091 0.083 0.091  0.071 1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kb4SvK_TsOUN",
        "outputId": "eacf7958-10b4-4f9a-ed7e-256063f84086"
      },
      "source": [
        "df_wu = pd.DataFrame(wSimMat, index = word_list, columns = word_list)\n",
        "print(\"Matrix of Wu-Palmer Similarity Method\")\n",
        "print(df_wu)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matrix of Wu-Palmer Similarity Method\n",
            "        man  swim  girl   boy  woman  walk   dog  table  lamp\n",
            "man   1.000 0.182 0.632 0.667  0.667 0.182 0.667  0.167 0.444\n",
            "swim  0.182 1.000 0.167 0.182  0.182 0.333 0.154  0.200 0.154\n",
            "girl  0.632 0.167 1.000 0.632  0.632 0.167 0.632  0.154 0.421\n",
            "boy   0.667 0.182 0.632 1.000  0.667 0.182 0.667  0.167 0.444\n",
            "woman 0.667 0.182 0.947 0.667  1.000 0.182 0.667  0.167 0.444\n",
            "walk  0.182 0.333 0.167 0.182  0.182 1.000 0.154  0.200 0.154\n",
            "dog   0.667 0.154 0.632 0.667  0.667 0.154 0.929  0.133 0.444\n",
            "table 0.167 0.200 0.154 0.167  0.167 0.200 0.133  1.000 0.133\n",
            "lamp  0.444 0.154 0.421 0.444  0.444 0.154 0.444  0.133 1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HkyURSHZsOUO",
        "outputId": "99e80709-5e6f-4196-9ffb-8d00a9e4c42d"
      },
      "source": [
        "df_l = pd.DataFrame(lSimMat, index = word_list, columns = word_list)\n",
        "print(\"Matrix of Leacock Chodorow Similarity Method\")\n",
        "print(df_l)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matrix of Leacock Chodorow Similarity Method\n",
            "        man  swim  girl   boy  woman  walk   dog  table  lamp\n",
            "man   1.000 0.000 0.619 0.698  0.698 0.000 0.465  0.341 0.341\n",
            "swim  0.000 0.896 0.000 0.000  0.000 0.594 0.000  0.000 0.000\n",
            "girl  0.619 0.000 1.000 0.507  0.809 0.000 0.428  0.317 0.317\n",
            "boy   0.698 0.000 0.507 1.000  0.557 0.000 0.465  0.341 0.341\n",
            "woman 0.698 0.000 0.809 0.557  1.000 0.000 0.465  0.341 0.341\n",
            "walk  0.000 0.594 0.000 0.000  0.000 0.896 0.000  0.000 0.000\n",
            "dog   0.465 0.000 0.428 0.465  0.465 0.000 1.000  0.274 0.341\n",
            "table 0.341 0.000 0.317 0.341  0.341 0.000 0.274  1.000 0.274\n",
            "lamp  0.341 0.000 0.317 0.341  0.341 0.000 0.341  0.274 1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7LNNVl6sOUO",
        "outputId": "cf931205-b2a9-4887-ecb2-14c99fee7e32"
      },
      "source": [
        "df_lin = pd.DataFrame(liSimMat, index = word_list, columns = word_list)\n",
        "print(\"Matrix of Lin Similarity Method\")\n",
        "print(df_lin)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matrix of Lin Similarity Method\n",
            "         man  swim   girl    boy  woman  walk    dog  table   lamp\n",
            "man    1.000 0.000  0.714  0.729  0.787 0.000  0.292 -0.000  0.200\n",
            "swim   0.000 1.000  0.000  0.000  0.000 0.491  0.000  0.000  0.000\n",
            "girl   0.714 0.000  1.000  0.293  0.907 0.000  0.269 -0.000  0.184\n",
            "boy    0.729 0.000  0.293  1.000  0.318 0.000  0.256 -0.000  0.175\n",
            "woman  0.787 0.000  0.907  0.318  1.000 0.000  0.291 -0.000  0.199\n",
            "walk   0.000 0.491  0.000  0.000  0.000 1.000  0.000  0.000  0.000\n",
            "dog    0.292 0.000  0.269  0.256  0.291 0.000  1.000 -0.000  0.169\n",
            "table -0.000 0.000 -0.000 -0.000 -0.000 0.000 -0.000  1.000 -0.000\n",
            "lamp   0.200 0.000  0.184  0.175  0.199 0.000  0.169 -0.000  1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZM1AQBx3eqLn"
      },
      "source": [
        "Normalize similarity values when necessary. What similarity seems better?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1A4nqtaqsOUP"
      },
      "source": [
        "According to our results the Lin similarties and Leacock Chodorow were the best to use. We added a couple more words to get a better idea of how it compares different types of nouns to each other, for example animals and furniture. It appears that the Leacock Chodorow is the perfered method for nouns, due to nouns such as humans being more close together than nouns that are animals. The Lin method is better for verbs. This is becuase in the Leacock Chodorow similarity the verb compared to itself wouldn't produce the result of 1. A 1 meant that the nouns were similar so it should have mirrored this for the verbs. In the Lin method all the words compared to them selves would produce a 1. So it would be better suited for comparing verbs. The other two methods produced results such as stating that a boy is pratically just as similar to a girl as a dog is. Which in most cases would be stated as not being true. "
      ]
    }
  ]
}