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date: 19 February 2019

Abstract and Keywords

A fundamental challenge in natural-language processing is to represent words as mathematical entities that can be read, reasoned, and manipulated by computational models. The current leading approach represents words as vectors in a continuous real-valued space, in such a way that similarities in the vector space correlate with semantic similarities between words. This chapter surveys various frameworks and methods for acquiring word vectors, while tying together related ideas and concepts.

Keywords: word representation, word embedding, embedding, distributed representation, lexical semantics, distributional semantics, distributional similarity, deep learning, dimensionality reduction, matrix factorization

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