Word Embeddings

Word embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space. This technique captures the semantic meaning of words by placing similar words closer together in this space, thus enabling machines to understand context and relationships between words. Word embeddings are commonly used in natural language processing (NLP) tasks such as sentiment analysis, machine translation, and text classification. Popular algorithms for generating word embeddings include Word2Vec, GloVe, and FastText, each with unique methods for creating these representations.