2013 LinguisticRegularitiesinContinu

(Mikolov et al., 2013b) ⇒ Tomáš Mikolov, Wen-tau Yih, and Geoffrey Zweig. (2013). “Linguistic Regularities in Continuous Space Word Representations..” In: HLT-NAACL.

Subject Headings: Word Embedding Task, Analogy Recovery Task.

Notes

Cited By

http://scholar.google.com/scholar?q=%222013%22+Linguistic+Regularities+in+Continuous+Space+Word+Representations.

Quotes

Abstract

Continuous space language models have recently demonstrated outstanding results across a variety of tasks. In this paper, we examine the vector-space word representations that are implicitly learned by the input-layer weights. We find that these representations are surprisingly good at capturing syntactic and semantic regularities in language, and that each relationship is characterized by a relation-specific vector offset. This allows vector-oriented reasoning based on the offsets between words. For example, the male / female relationship is automatically learned, and with the induced vector representations, "King - Man + Woman" results in a vector very close to "Queen." We demonstrate that the word vectors capture syntactic regularities by means of syntactic analogy questions (provided with this paper), and are able to correctly answer almost 40% of the questions. We demonstrate that the word vectors capture semantic regularities by using the vector offset method to answer SemEval-2012 Task 2 questions. Remarkably, this method outperforms the best previous systems.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 LinguisticRegularitiesinContinu	Wen-tau Yih Geoffrey Zweig Tomáš Mikolov			Linguistic Regularities in Continuous Space Word Representations.						2013