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word embedding
Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing where words from the vocabulary (and possibly phrases thereof) are mapped to vectors of real numbers in a low dimensional space, relative to the vocabulary size ("continuous space").
There are several methods for generating this mapping. They include neural networks, dimensionality reduction on the word co-occurrence matrix, and explicit representation in terms of the context in which words appear.
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 and sentiment analysis.
== See also ==
* Brown clustering
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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