Learning semantic textual similarity with structural representations

Aliaksei Severyn, Massimo Nicosia, Alessandro Moschitti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

Measuring semantic textual similarity (STS) is at the cornerstone of many NLP applications. Different from the majority of approaches, where a large number of pairwise similarity features are used to represent a text pair, our model features the following: (i) it directly encodes input texts into relational syntactic structures; (ii) relies on tree kernels to handle feature engineering automatically; (iii) combines both structural and feature vector representations in a single scoring model, i.e., in Support Vector Regression (SVR); and (iv) delivers significant improvement over the best STS systems.

Original languageEnglish
Title of host publicationACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages714-718
Number of pages5
Volume2
ISBN (Print)9781937284510
Publication statusPublished - 1 Jan 2013
Event51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria
Duration: 4 Aug 20139 Aug 2013

Other

Other51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
CountryBulgaria
CitySofia
Period4/8/139/8/13

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ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Severyn, A., Nicosia, M., & Moschitti, A. (2013). Learning semantic textual similarity with structural representations. In ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Vol. 2, pp. 714-718). Association for Computational Linguistics (ACL).