SemanticZ at SemEval-2016 task 3: Ranking relevant answers in community Question Answering using semantic similarity based on fine-tuned word embeddings

Todor Mihaylov, Preslav Nakov

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

24 Citations (Scopus)

Abstract

We describe our system for finding good answers in a community forum, as defined in SemEval-2016, Task 3 on Community Question Answering. Our approach relies on several semantic similarity features based on fine-tuned word embeddings and topics similarities. In the main Subtask C, our primary submission was ranked third, with a MAP of 51.68 and accuracy of 69.94. In Subtask A, our primary submission was also third, with MAP of 77.58 and accuracy of 73.39.

Original languageEnglish
Title of host publicationSemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages879-886
Number of pages8
ISBN (Electronic)9781941643952
Publication statusPublished - 1 Jan 2016
Event10th International Workshop on Semantic Evaluation, SemEval 2016 - San Diego, United States
Duration: 16 Jun 201617 Jun 2016

Other

Other10th International Workshop on Semantic Evaluation, SemEval 2016
CountryUnited States
CitySan Diego
Period16/6/1617/6/16

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ASJC Scopus subject areas

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

Cite this

Mihaylov, T., & Nakov, P. (2016). SemanticZ at SemEval-2016 task 3: Ranking relevant answers in community Question Answering using semantic similarity based on fine-tuned word embeddings. In SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings (pp. 879-886). Association for Computational Linguistics (ACL).