MayoNLP at SemEval-2016 task 1: Semantic textual similarity based on lexical semantic net and deep learning semantic model

Naveed Afzal, Yanshan Wang, Hongfang D Liu

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

13 Scopus citations

Abstract

Given two sentences, participating systems assign a semantic similarity score in the range of 0-5. We applied two different techniques for the task: one is based on lexical semantic net (corresponding to run 1) and the other is based on deep learning semantic model (corresponding to run 2). We also combined these two runs linearly (corresponding to run 3). Our results indicate that the two techniques perform comparably while the combination outperforms the individual ones on four out of five datasets, namely answer-answer, headlines, plagiarism, and question-question, and on the overall weighted mean of STS 2016 and 2015 datasets.

Original languageEnglish (US)
Title of host publicationSemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages674-679
Number of pages6
ISBN (Electronic)9781941643952
StatePublished - Jan 1 2016
Event10th International Workshop on Semantic Evaluation, SemEval 2016 - San Diego, United States
Duration: Jun 16 2016Jun 17 2016

Other

Other10th International Workshop on Semantic Evaluation, SemEval 2016
CountryUnited States
CitySan Diego
Period6/16/166/17/16

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ASJC Scopus subject areas

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

Cite this

Afzal, N., Wang, Y., & Liu, H. D. (2016). MayoNLP at SemEval-2016 task 1: Semantic textual similarity based on lexical semantic net and deep learning semantic model. In SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings (pp. 674-679). Association for Computational Linguistics (ACL).