Word embeddings (Word2Vec, GloVe) - Deep Learning Tutorial

Word embeddings are a fundamental concept in natural language processing (NLP) and deep learning. They are dense vector representations of words that capture semantic relationships between words in a corpus. Word embeddings enable us to transform words into numerical vectors, making it easier for machine learning models to process and understand text data. In this tutorial, we will explore Word2Vec and GloVe, two popular techniques for creating word embeddings, and their practical applications in NLP tasks.

1. Word2Vec

Word2Vec is a popular word embedding technique introduced by Mikolov et al. in 2013. It is based on the idea that words with similar meanings tend to occur in similar contexts. Word2Vec offers two architectures: Continuous Bag of Words (CBOW) and Skip-gram.

Code Example using Gensim for Word2Vec

Below is a simple example of creating Word2Vec embeddings using Gensim library in Python:


    import gensim
    from gensim.models import Word2Vec

    # Sample sentences
    sentences = [['I', 'love', 'deep', 'learning'], ['Word', 'embeddings', 'are', 'useful']]

    # Create Word2Vec model
    model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, sg=0)

    # Get the word vector for a word
    word_vector = model.wv['deep']

2. GloVe (Global Vectors for Word Representation)

GloVe is another popular word embedding technique that was introduced by Pennington et al. in 2014. It uses global statistics of word co-occurrence to generate word vectors. GloVe aims to factorize the word co-occurrence matrix to obtain word embeddings.

Code Example using Gensim for GloVe

Below is a simple example of creating GloVe embeddings using Gensim library in Python:


    from gensim.scripts.glove2word2vec import glove2word2vec
    from gensim.models import KeyedVectors

    # Convert GloVe to Word2Vec format
    glove_input_file = 'glove.6B.100d.txt'
    word2vec_output_file = 'glove.6B.100d.word2vec.txt'
    glove2word2vec(glove_input_file, word2vec_output_file)

    # Load the model
    model = KeyedVectors.load_word2vec_format(word2vec_output_file)
    word_vector = model['deep']

Common Mistakes with Word Embeddings

Using too small training data, leading to poor quality embeddings.
Ignoring hyperparameters like vector size, window size, and min_count, affecting the performance.
Mixing case sensitivity in text preprocessing, resulting in different embeddings for the same word.
Not handling out-of-vocabulary (OOV) words properly during inference.

Frequently Asked Questions (FAQs)

What are word embeddings used for in NLP?
How do Word2Vec and GloVe differ from each other?
Can word embeddings be used for sentiment analysis?
How do I choose the right vector size for word embeddings?
Are pre-trained word embeddings available for different languages?
How can I visualize word embeddings in a lower-dimensional space?
Can word embeddings be used for languages with morphological variations?
What are the limitations of word embeddings?
How do word embeddings capture semantic relationships?
What is the purpose of context window in Word2Vec?

Summary

Word embeddings, such as Word2Vec and GloVe, are powerful techniques that enable us to represent words as dense vectors in NLP tasks. They capture semantic relationships between words and find applications in sentiment analysis, machine translation, and document clustering. Careful parameter tuning and handling of OOV words are crucial to obtain meaningful word embeddings. With the advancements in deep learning, word embeddings continue to play a significant role in enhancing the performance of various NLP applications.