Word embedding

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Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing where words or phrases from the vocabulary are mapped to vectors of real numbers in a low-dimensional space relative to the vocabulary size ("continuous space").

Methods to generate this mapping include neural networks,^[1] dimensionality reduction on the word co-occurrence matrix,^[2][3][4] and explicit representation in terms of the context in which words appear.^[5]

Word and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing^[6] and sentiment analysis.^[7]

See also

• Brown clustering

References