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SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity

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arxiv 1608.00869 v4 pith:VGEUDTGF submitted 2016-08-02 cs.CL

Daniela Gerz , Ivan Vulić , Felix Hill , Roi Reichart , Anna Korhonen This is my paper · ORCID
classification cs.CL
keywords simverb-3500verbdevelopmentevaluationmeaninghumansemanticssimilarity
verification ladder T0 review T1 audit T2 compute T3 formal
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Verbs play a critical role in the meaning of sentences, but these ubiquitous words have received little attention in recent distributional semantics research. We introduce SimVerb-3500, an evaluation resource that provides human ratings for the similarity of 3,500 verb pairs. SimVerb-3500 covers all normed verb types from the USF free-association database, providing at least three examples for every VerbNet class. This broad coverage facilitates detailed analyses of how syntactic and semantic phenomena together influence human understanding of verb meaning. Further, with significantly larger development and test sets than existing benchmarks, SimVerb-3500 enables more robust evaluation of representation learning architectures and promotes the development of methods tailored to verbs. We hope that SimVerb-3500 will enable a richer understanding of the diversity and complexity of verb semantics and guide the development of systems that can effectively represent and interpret this meaning.

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    A method that represents each word by the concatenated autoencoder latent codes of images of its dictionary definition terms, evaluated on word similarity, categorization, and outlier detection.

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