Word2Vec

Word2Vec is a shallow, two-layer neural network that was designed to reconstruct the linguistic contexts of words. Introduced by Tomas Mikolov and his team at Google in 2013, Word2Vec was groundbreaking as it was the first word embedding model capable of preserving the context of words in a vector space. By training on large corpora of text data, it captures semantic relationships between words, allowing for vector operations that reflect meaningful analogies. For instance, the relationship expressed by “France – Paris” is similar to “Italy – Rome,” illustrating that Word2Vec successfully encodes such analogical relationships. This ability to capture and represent word meanings and relationships in continuous vector space has had a profound impact on natural language processing tasks, significantly improving the performance of various applications, including machine translation, sentiment analysis, and information retrieval.