{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Mu6B0Rgsd1XM", "outputId": "d011c076-74a6-4217-9ce8-31c11542088a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: nltk in /usr/local/lib/python3.12/dist-packages (3.9.1)\n", "Requirement already satisfied: click in /usr/local/lib/python3.12/dist-packages (from nltk) (8.3.1)\n", "Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from nltk) (1.5.3)\n", "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.12/dist-packages (from nltk) (2025.11.3)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from nltk) (4.67.1)\n" ] }, { "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" ] } ], "source": [ "!pip install nltk\n", "import nltk\n", "nltk.download('wordnet')\n", "nltk.download('omw-1.4')\n", "from nltk.corpus import wordnet\n" ] }, { "cell_type": "code", "source": [ "syns = wordnet.synsets(\"program\")\n", "print(syns[0].name())\n", "print(syns[0].lemmas()[0].name())\n", "print(syns[0].definition())\n", "print(syns[0].examples())\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1rK2NcStd6A_", "outputId": "e6dc4464-61c8-4fe2-ba16-3dee4fe69b32" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "plan.n.01\n", "plan\n", "a series of steps to be carried out or goals to be accomplished\n", "['they drew up a six-step plan', 'they discussed plans for a new bond issue']\n" ] } ] }, { "cell_type": "code", "source": [ "synonyms = []\n", "\n", "for syn in wordnet.synsets(\"good\"):\n", " for lemma in syn.lemmas():\n", " synonyms.append(lemma.name())\n", "\n", "print(set(synonyms))\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2GXqUrN1d6F-", "outputId": "be79d25d-0931-44c8-ed7e-5c9e8902c511" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'right', 'secure', 'adept', 'sound', 'in_force', 'respectable', 'thoroughly', 'upright', 'unspoiled', 'undecomposed', 'goodness', 'well', 'commodity', 'trade_good', 'ripe', 'dependable', 'near', 'expert', 'honorable', 'effective', 'good', 'salutary', 'practiced', 'beneficial', 'safe', 'proficient', 'dear', 'full', 'soundly', 'skilful', 'honest', 'unspoilt', 'serious', 'estimable', 'skillful', 'in_effect', 'just'}\n" ] } ] }, { "cell_type": "code", "source": [ "antonyms = []\n", "\n", "for syn in wordnet.synsets(\"good\"):\n", " for lemma in syn.lemmas():\n", " if lemma.antonyms():\n", " antonyms.append(lemma.antonyms()[0].name())\n", "\n", "print(set(antonyms))\n" ], "metadata": { "id": "7khIS8aJd6Ig", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "3476c18d-50fa-44f8-b8d8-0f5b9673e886" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'bad', 'badness', 'evil', 'ill', 'evilness'}\n" ] } ] }, { "cell_type": "code", "source": [ "def get_synonyms_antonyms(word):\n", " synonyms = set()\n", " antonyms = set()\n", "\n", " for syn in wordnet.synsets(word):\n", " for lemma in syn.lemmas():\n", " synonyms.add(lemma.name())\n", " if lemma.antonyms():\n", " antonyms.add(lemma.antonyms()[0].name())\n", "\n", " return synonyms, antonyms\n", "\n", "syns, ants = get_synonyms_antonyms(\"happy\")\n", "print(\"Synonyms:\", syns)\n", "print(\"Antonyms:\", ants)\n" ], "metadata": { "id": "wYJNfNSWeUxf", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2d8a09f6-fa0d-4d76-9232-85b5da797ae6" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Synonyms: {'felicitous', 'well-chosen', 'happy', 'glad'}\n", "Antonyms: {'unhappy'}\n" ] } ] }, { "cell_type": "code", "source": [ "\n", "w1 = wordnet.synset('run.v.01')\n", "w2 = wordnet.synset('sprint.v.01')\n", "print(\"Verb similarity:\", w1.wup_similarity(w2))\n", "\n", "\n", "w1 = wordnet.synset('ship.n.01')\n", "w2 = wordnet.synset('boat.n.01')\n", "print(\"Noun similarity:\", w1.wup_similarity(w2))\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LqQsu0BBv49D", "outputId": "d3360f01-6b75-4fd0-ef77-2cd7f5a1884a" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Verb similarity: 0.8571428571428571\n", "Noun similarity: 0.9090909090909091\n" ] } ] }, { "cell_type": "code", "source": [ "from nltk.corpus import wordnet\n", "\n", "syn = wordnet.synset('dog.n.01')\n", "print(syn.hypernyms())\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FU7Z-smEv4_q", "outputId": "e50eebaa-5be4-4f57-b763-084214ce21c8" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[Synset('domestic_animal.n.01'), Synset('canine.n.02')]\n" ] } ] }, { "cell_type": "code", "source": [ "syn = wordnet.synset('dog.n.01')\n", "print(syn.hyponyms())\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RTeQIgE3wqKm", "outputId": "1f672596-0111-48b3-b6c4-6b44d2f115db" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[Synset('pug.n.01'), Synset('leonberg.n.01'), Synset('griffon.n.02'), Synset('spitz.n.01'), Synset('toy_dog.n.01'), Synset('basenji.n.01'), Synset('great_pyrenees.n.01'), Synset('working_dog.n.01'), Synset('hunting_dog.n.01'), Synset('poodle.n.01'), Synset('mexican_hairless.n.01'), Synset('puppy.n.01'), Synset('pooch.n.01'), Synset('newfoundland.n.01'), Synset('corgi.n.01'), Synset('dalmatian.n.02'), Synset('cur.n.01'), Synset('lapdog.n.01')]\n" ] } ] }, { "cell_type": "code", "source": [ "syn = wordnet.synset('tree.n.01')\n", "print(syn.part_meronyms())\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hAHbyFVYwqNH", "outputId": "8373e854-e1ce-492f-cbdd-b11a908e6c06" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[Synset('stump.n.01'), Synset('crown.n.07'), Synset('burl.n.02'), Synset('trunk.n.01'), Synset('limb.n.02')]\n" ] } ] }, { "cell_type": "code", "source": [ "\n" ], "metadata": { "id": "CRAqAQ0mwqPk" }, "execution_count": 11, "outputs": [] } ] }