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There have been several efforts to extend distributional semantics beyondindividual words, to measure the similarity of word pairs, phrases, andsentences (briefly, tuples; ordered sets of words, contiguous ornoncontiguous). One way to extend beyond words is to compare two tuples using afunction that combines pairwise similarities between the component words in thetuples. A strength of this approach is that it works with both relationalsimilarity (analogy) and compositional similarity (paraphrase). However, pastwork required hand-coding the combination function for different tasks. Themain contribution of this paper is that combination functions are generated bysupervised learning. We achieve state-of-the-art results in measuringrelational similarity between word pairs (SAT analogies and SemEval~2012 Task2) and measuring compositional similarity between noun-modifier phrases andunigrams (multiple-choice paraphrase questions).

Distributional semantics beyond words: Supervised learning of analogy and paraphrase