Static Word Embeddings


Introduced by Bojanowski et al. in Enriching Word Vectors with Subword Information

fastText embeddings exploit subword information to construct word embeddings. Representations are learnt of character $n$-grams, and words represented as the sum of the $n$-gram vectors. This extends the word2vec type models with subword information. This helps the embeddings understand suffixes and prefixes. Once a word is represented using character $n$-grams, a skipgram model is trained to learn the embeddings.

Source: Enriching Word Vectors with Subword Information


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