2014 LookingforHyponymsinVectorSpace

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Subject Headings: Is-a-Type-of Relation Mention, Distributional Word Embedding, Neural Network-based Word Embedding, word2vec, Dependency-based Vector Space Model.

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Abstract

The task of detecting and generating hyponyms is at the core of semantic understanding of language, and has numerous practical applications. We investigate how neural network embeddings perform on this task, compared to dependency-based vector space models, and evaluate a range of similarity measures on hyponym generation. A new asymmetric similarity measure and a combination approach are described, both of which significantly improve precision. We release three new datasets of lexical vector representations trained on the BNC and our evaluation dataset for hyponym generation.


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2014 LookingforHyponymsinVectorSpaceMarek Rei
Ted Briscoe		Looking for Hyponyms in Vector Space2014
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