16_Implementing_Word_Embedding.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-k_eyY44JlsT"
      },
      "source": [
        "## Implementing Word Embedding using NLTK"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5EmxKmCJwaB"
      },
      "source": [
        "Importing the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lpB0VtyKDEQS",
        "outputId": "7a2de6b6-4135-4a03-b7bc-b17fad4fc63c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to\n",
            "[nltk_data]     C:\\Users\\dell\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to\n",
            "[nltk_data]     C:\\Users\\dell\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to\n",
            "[nltk_data]     C:\\Users\\dell\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "# imports the TruncatedSVD class from scikit-learn, which is used for dimensionality reduction via Singular Value Decomposition (SVD)\n",
        "from sklearn.decomposition import TruncatedSVD\n",
        "# imports the TSNE class from scikit-learn, which is used for t-Distributed Stochastic Neighbor Embedding (t-SNE), a technique for dimensionality reduction and visualization.\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "zEY6fdhrET7C"
      },
      "outputs": [],
      "source": [
        "# Sample texts\n",
        "texts = [\n",
        "    \"Word embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space.\",\n",
        "    \"These word vectors capture semantic meaning and relationships between words.\",\n",
        "    \"Word embeddings are used in various natural language processing applications such as sentiment analysis, language translation, and named entity recognition.\"\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZdZ9-xrKJ1Rl"
      },
      "source": [
        "Preprocessing the text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Dq4wF9E2JRP0"
      },
      "outputs": [],
      "source": [
        "# Tokenization and preprocessing\n",
        "stop_words = set(stopwords.words(\"english\"))\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "def preprocess_text(text):\n",
        "    tokens = word_tokenize(text.lower())\n",
        "    tokens = [token for token in tokens if token.isalnum()]\n",
        "    tokens = [token for token in tokens if token not in stop_words]\n",
        "    tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
        "    return \" \".join(tokens)\n",
        "\n",
        "preprocessed_texts = [preprocess_text(text) for text in texts]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EsYORZTjJ33R"
      },
      "source": [
        "Perform word vectorisation using TF-IDF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Dath9GHbJUiT"
      },
      "outputs": [],
      "source": [
        "# TF-IDF Vectorization\n",
        "vectorizer = TfidfVectorizer()\n",
        "X = vectorizer.fit_transform(preprocessed_texts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YHzx_VPJJ9Eu"
      },
      "source": [
        "This step initializes a TruncatedSVD object with the number of components equal to the number of features in the TF-IDF vectors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "n3vJZh3bJYkb"
      },
      "outputs": [],
      "source": [
        "# Performs dimensionality reduction using TruncatedSVD\n",
        "svd = TruncatedSVD(n_components=X.shape[1])\n",
        "X_reduced = svd.fit_transform(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LAV7XXzwKpDn"
      },
      "source": [
        "t-SNE is a technique for dimensionality reduction and visualization that aims to map high-dimensional data to a lower-dimensional space, typically 2 or 3 dimensions.\n",
        "Perplexity represents the number of close neighbors each point has."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "1kcEbZKXJg0G"
      },
      "outputs": [],
      "source": [
        "# Visualize using t-SNE\n",
        "tsne = TSNE(n_components=2, perplexity=2, random_state=42)\n",
        "X_embedded = tsne.fit_transform(X_reduced)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "id": "TrFgv9utJips",
        "outputId": "b7cb3b0a-0163-48c2-97b7-5bfd93e22628"
      },
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot\n",
        "plt.figure(figsize=(10, 8))\n",
        "for i, txt in enumerate(texts):\n",
        "    plt.scatter(X_embedded[i, 0], X_embedded[i, 1])\n",
        "    plt.annotate(txt, (X_embedded[i, 0], X_embedded[i, 1]))\n",
        "plt.title('Word Embedding Visualization')\n",
        "plt.xlabel('Dimension 1')\n",
        "plt.ylabel('Dimension 2')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sh-NfMdZXg8R"
      },
      "source": [
        "## Implementing word embedding using spaCy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mmV7BWGtXrAx"
      },
      "source": [
        "Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "oXi0PnemJkMw"
      },
      "outputs": [],
      "source": [
        "import spacy\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "# Load the English language model\n",
        "nlp = spacy.load(\"en_core_web_md\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "7L1FlCtiPbCB"
      },
      "outputs": [],
      "source": [
        "# Sample texts\n",
        "texts = [\n",
        "    \"Word embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space.\",\n",
        "    \"These word vectors capture semantic meaning and relationships between words.\",\n",
        "    \"Word embeddings are used in various natural language processing applications such as sentiment analysis, language translation, and named entity recognition.\"\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yf0AEZTxX8A9"
      },
      "source": [
        "Preprocess the text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "k6bYgl7-X5Cd"
      },
      "outputs": [],
      "source": [
        "# Process the texts and get word vectors\n",
        "word_vectors = []\n",
        "for text in texts:\n",
        "    doc = nlp(text)\n",
        "    text_vectors = [token.vector for token in doc if token.has_vector]\n",
        "    word_vectors.extend(text_vectors)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fzw6zBkOYAD6"
      },
      "source": [
        "Convert the text into numpy array and visualize using t-SNE\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "ipmjOR-tX_ex"
      },
      "outputs": [],
      "source": [
        "# Convert word_vectors to a NumPy array\n",
        "word_vectors = np.array(word_vectors)\n",
        "\n",
        "# Visualize using t-SNE\n",
        "tsne = TSNE(n_components=2, random_state=42)\n",
        "word_vectors_embedded = tsne.fit_transform(word_vectors)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6P59GxnYNIr"
      },
      "source": [
        "Plotting the graph"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "qmh342FOYMk0",
        "outputId": "d9774d3d-4981-4a2c-ccab-763c69c21378"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot\n",
        "plt.figure(figsize=(10, 8))\n",
        "for i, vec in enumerate(word_vectors_embedded):\n",
        "    if i < len(texts):  # Check if the index is within the range of texts\n",
        "        plt.scatter(vec[0], vec[1])\n",
        "        plt.annotate(texts[i], (vec[0], vec[1]))\n",
        "plt.title('Word Embedding Visualization')\n",
        "plt.xlabel('Dimension 1')\n",
        "plt.ylabel('Dimension 2')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}