Distributional semantics beyond words : supervised learning of analogy and paraphrase

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AuthorSearch for:
TypeArticle
Journal titleTransactions of the Association for Computational Linguistics (TACL)
Volume1
Pages353366; # of pages: 14
AbstractThere have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or noncontiguous). One way to extend beyond words is to compare two tuples using a function that combines pairwise similarities between the component words in the tuples. A strength of this approach is that it works with both relational similarity (analogy) and compositional similarity (paraphrase). However, past work required hand-coding the combination function for different tasks. The main contribution of this paper is that combination functions are generated by supervised learning. We achieve state-of-the-art results in measuring relational similarity between word pairs (SAT analogies and SemEval 2012 Task 2) and measuring compositional similarity between nounmodifier phrases and unigrams (multiplechoice paraphrase questions).
Publication date
PublisherACL
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21270982
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Record identifier2a8518e2-c383-4291-ae25-03fadbf71fca
Record created2014-02-21
Record modified2016-05-09
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