Abstract

In Semantic Textual Similarity (STS), systems rate the degree of semantic equivalence, on a graded scale from 0 to 5, with 5 being the most similar. This year we set up two tasks: (i) a core task (CORE), and (ii) a typed-similarity task (TYPED). CORE is similar in set up to SemEval STS 2012 task with pairs of sentences from sources related to those of 2012, yet different in genre from the 2012 set, namely, this year we included newswire headlines, machine translation evaluation datasets and multiple lexical resource glossed sets. TYPED, on the other hand, is novel and tries to characterize why two items are deemed similar, using cultural heritage items which are described with metadata such as title, author or description. Several types of similarity have been defined, including similar author, similar time period or similar location. The annotation for both tasks leverages crowdsourcing, with relative high interannotator correlation, ranging from 62% to 87%. The CORE task attracted 34 participants with 89 runs, and the TYPED task attracted 6 teams with 14 runs.

Keywords

Computer scienceTask (project management)CrowdsourcingNatural language processingSimilarity (geometry)Set (abstract data type)Semantic similarityMetadataSemEvalArtificial intelligenceAnnotationEquivalence (formal languages)Machine translationInformation retrievalScale (ratio)World Wide WebLinguisticsImage (mathematics)

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Publication Info

Year
2013
Type
article
Volume
1
Pages
32-43
Citations
385
Access
Closed

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Cite This

Eneko Agirre, Daniel Cer, Mona Diab et al. (2013). *SEM 2013 shared task: Semantic Textual Similarity. Joint Conference on Lexical and Computational Semantics , 1 , 32-43.