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• W2115008472 abstract "There 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 noun-modifier phrases and unigrams (multiple-choice paraphrase questions)." @default.
• W2115008472 created "2016-06-24" @default.
• W2115008472 creator A5027819738 @default.
• W2115008472 date "2013-10-18" @default.
• W2115008472 modified "2023-09-27" @default.
• W2115008472 title "Distributional semantics beyond words: Supervised learning of analogy and paraphrase" @default.
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• W2115008472 hasPublicationYear "2013" @default.
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