Word embedding

Word embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space. This representation is useful in natural language processing (NLP) tasks because it captures semantic relationships between words, with similar words being located close to each other in this vector space. Word embeddings are widely used in various NLP applications such as text classification, sentiment analysis, and machine translation, benefiting researchers and developers in the field of computational linguistics.

Word embedding was discovered on May 8th 2020 and it currently has a search volume of 18.1K with a growth of +24%.