MayoClinicNLP–CORE: Semantic representations for textual similarity

Stephen Wu, Dongqing Zhu, Ben Carterette, Hongfang D Liu

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

4 Scopus citations

Abstract

The Semantic Textual Similarity (STS) task examines semantic similarity at a sentence-level. We explored three representations of semantics (implicit or explicit): named entities, semantic vectors, and structured vectorial semantics. From a DKPro baseline, we also performed feature selection and used source-specific linear regression models to combine our features. Our systems placed 5th, 6th, and 8th among 90 submitted systems.

Original languageEnglish (US)
Title of host publication*SEM 2013 - 2nd Joint Conference on Lexical and Computational Semantics
PublisherAssociation for Computational Linguistics (ACL)
Pages148-154
Number of pages7
Volume1
ISBN (Electronic)9781937284480
StatePublished - Jan 1 2013
Event2nd Joint Conference on Lexical and Computational Semantics, *SEM 2013 - Atlanta, United States
Duration: Jun 13 2013Jun 14 2013

Other

Other2nd Joint Conference on Lexical and Computational Semantics, *SEM 2013
CountryUnited States
CityAtlanta
Period6/13/136/14/13

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'MayoClinicNLP–CORE: Semantic representations for textual similarity'. Together they form a unique fingerprint.

  • Cite this

    Wu, S., Zhu, D., Carterette, B., & Liu, H. D. (2013). MayoClinicNLP–CORE: Semantic representations for textual similarity. In *SEM 2013 - 2nd Joint Conference on Lexical and Computational Semantics (Vol. 1, pp. 148-154). Association for Computational Linguistics (ACL).