Word embeddings like Word2Vec and GloVe are techniques for converting words into numerical vectors, enabling machines to process and analyze language. These vectors capture semantic and syntactic relationships between words, allowing algorithms to recognize that “king” and “queen” are related or that “running” and “jumping” describe similar actions. Unlike simpler methods like one-hot encoding, which treat words as isolated symbols, embeddings place words in a continuous vector space where proximity reflects meaning.
Word2Vec, introduced in 2013, uses neural networks to create embeddings by analyzing local word contexts. It offers two training approaches: Continuous Bag of Words (CBOW) and Skip-Gram. CBOW predicts a target word from its surrounding context, while Skip-Gram does the reverse, predicting context words from a target. For example, in the sentence “The cat sits on the mat,” Skip-Gram might learn that “sits” is associated with “cat,” “on,” and “mat” within a fixed window size. Word2Vec excels at capturing analogies (e.g., “king - man + woman ≈ queen”) and scales efficiently to large datasets. However, it treats each context window independently, potentially missing global statistical patterns.
GloVe (Global Vectors), developed in 2014, addresses this limitation by incorporating global co-occurrence statistics. It constructs a matrix counting how often words appear together in a corpus (e.g., “ice” co-occurs with “cold” frequently) and factorizes this matrix to produce embeddings. This hybrid approach combines the local context sensitivity of Word2Vec with insights from overall word frequencies. For instance, GloVe might assign similar vectors to “dog” and “puppy” because they co-occur with words like “bark” or “pet,” even if they rarely appear in the same sentence. GloVe often performs better on tasks requiring broader semantic understanding, though it requires more memory to store co-occurrence data.
Both methods produce dense, low-dimensional vectors (typically 100-300 dimensions) and are foundational for tasks like text classification, machine translation, and recommendation systems. Developers can implement them using libraries like Gensim (Word2Vec) or GloVe’s official tools, often starting with pre-trained embeddings for common languages. Choosing between them depends on the problem: Word2Vec is lightweight and suitable for local patterns, while GloVe leverages global data for richer semantic relationships.