Word2Vec and GloVe are techniques for creating word embeddings—numerical representations of words that capture their meanings and relationships. These embeddings map words into a high-dimensional vector space where similar words are positioned closer together. Both methods analyze large text corpora but use different strategies to learn these representations.
Word2Vec operates using neural networks trained to predict words based on their context. It has two architectures: Continuous Bag of Words (CBOW) and Skip-Gram. CBOW predicts a target word from its surrounding context words (e.g., guessing “cat” from “The ___ sat on the mat”), while Skip-Gram does the reverse, predicting context words from a target word. For example, given “cat,” the model learns to predict nearby words like “sat” or “mat.” During training, the model adjusts word vectors to minimize prediction errors, causing words with similar contexts to develop similar vectors. For instance, “king” and “queen” might end up close in the vector space because they appear in comparable contexts (e.g., “royal” or “throne”).
GloVe (Global Vectors for Word Representation) takes a different approach by leveraging global word co-occurrence statistics across the entire corpus. It constructs a matrix where each entry represents how often two words appear together within a certain window (e.g., “ice” and “solid” might co-occur frequently). GloVe then factorizes this matrix to produce embeddings that preserve these statistical relationships. The key idea is that the dot product of two word vectors should approximate the logarithm of their co-occurrence probability. For example, if “water” and “liquid” often appear together, their vectors will be adjusted to reflect this relationship. Unlike Word2Vec, which processes local context windows, GloVe uses aggregated global data, which can capture more nuanced semantic and syntactic patterns.
The main differences lie in their training objectives and data usage. Word2Vec focuses on local context patterns through prediction tasks, making it efficient for large datasets but potentially missing broader trends. GloVe explicitly models global co-occurrence statistics, which can better capture relationships like analogies (e.g., “king - man + woman ≈ queen”). However, both methods require careful tuning—Word2Vec needs choices like window size and negative sampling, while GloVe relies on building an accurate co-occurrence matrix. Developers often choose between them based on task requirements: Word2Vec is simpler for incremental training, while GloVe may perform better when global statistics are critical.