Word embedding is a key technique in natural language processing (NLP), which converts words into dense vectors of real numbers. This tutorial will guide you through the process of training word embeddings.

Overview

  • What is Word Embedding?

    • Word embedding is a method to represent words as dense vectors in a multi-dimensional space.
    • It captures the semantic and syntactic relationships between words.

  • Why Use Word Embedding?

    • It helps in understanding the meaning of words and sentences.
    • It is used in various NLP tasks like text classification, sentiment analysis, and machine translation.

Getting Started

  1. Install Required Libraries

    • Python libraries like numpy, scikit-learn, and gensim are essential for training word embeddings.

  2. Corpus Preparation

    • Prepare a large corpus of text data for training the embeddings.
    • The quality and size of the corpus directly affect the quality of the embeddings.

  3. Word Embedding Models

    • Word2Vec: A popular model that uses a neural network to learn word embeddings.
    • GloVe: Global Vectors for Word Representation, a pre-trained word embedding model.

Step-by-Step Guide

  1. Load the Corpus

    • Use gensim to load the corpus.
    • from gensim.models import word2vec
corpus = word2vec.Text8Corpus('your_text_corpus.txt')

  2. Train the Model

    • Train the word2vec model using the corpus.
    • model = word2vec.Word2Vec(corpus, vector_size=100, window=5, min_count=5)

  3. Use the Model

    • Use the trained model to get the embedding of a word.
    • word_embedding = model.wv['word']

Resources

For more detailed information and advanced tutorials, check out our Word Embedding Deep Dive.

To visualize word embeddings, you can use tools like t-SNE or UMAP.

Example of a Word Embedding

Here's an example of a word embedding for the word "cat":

cat_embedding = model.wv['cat']

You can visualize this embedding using a scatter plot or a 2D plot with t-SNE or UMAP.

cat_embedding Visualization