Word vectors, also known as word embeddings, are a foundational technique in natural language processing (NLP) that facilitate the understanding of language by machines. They are high-dimensional vectors that represent words in a continuous vector space, where semantically similar words are mapped to proximate points. This representation allows algorithms to process text and perform tasks like sentiment analysis, named entity recognition, and machine translation more effectively by capturing the subtleties of language, including synonyms, antonyms, and contextual nuance. Word vectors are generated using algorithms like Word2Vec, GloVe, or FastText, which learn these representations by analyzing large corpora of text and the context in which words appear.
The concept of word vectors gained prominence in the early 2010s, with significant developments like Word2Vec introduced by Mikolov et al. in 2013. These techniques marked a shift from sparse, high-dimensional representations like one-hot encoding to dense, low-dimensional vectors that capture much richer semantic relationships.
Tomas Mikolov and his team at Google were pivotal in the development of Word2Vec, a breakthrough method for creating word embeddings. Similarly, researchers at Stanford University developed GloVe (Global Vectors for Word Representation), another widely used approach for generating word vectors, further advancing the field of NLP.