Word Embeddings

Word embeddings are a type of representation for text where words are mapped to vectors of real numbers in a continuous vector space, capturing semantic and syntactic relationships. They are fundamental in natural language processing (NLP) for tasks like sentiment analysis, machine translation, and text classification. Techniques like Word2Vec, GloVe, and FastText generate these embeddings by learning from large text corpora.

Also known as: Word Vectors, Word Representations, Embedding Models, NLP Embeddings, Semantic Vectors

Why learn Word Embeddings?

Developers should learn word embeddings when working on NLP projects to improve model performance by providing dense, meaningful representations of words that capture context and meaning. They are essential for tasks such as language modeling, recommendation systems, and chatbots, where understanding word similarities and relationships is crucial. Using pre-trained embeddings can save time and computational resources compared to training from scratch.

Learning Resources

• tutorial: Word2Vec Tutorial - TensorFlow
• docs: GloVe: Global Vectors for Word Representation
• docs: FastText: Enriching Word Vectors with Subword Information
• tutorial: Word Embeddings Explained - Towards Data Science
• course: Coursera: Natural Language Processing Specialization