基於特徵為本及使用 SVM 的文本對蘊涵關係的自動推論方法

Translated title of the contribution: Textual entailment recognition using textual features and SVM

Tao Hsing Chang, Yao Chi Hsu, Chung Wei Chang, Yao Chuan Hsu, Hsueh Chih Chen

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

Abstract

The aim of this paper is to propose a system, which can automatically infer entailment relations of textual pairs. SVM is utilized as a prediction model of the system and seven features of textual pairs are employed to be input of the prediction model. The performance of this system is evaluated by dataset in CT-MC task held by RITE-2 of NTCIR. Macro-F1 of the proposed method is 46.35%.

Translated title of the contributionTextual entailment recognition using textual features and SVM
Original languageChinese
Title of host publicationProceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013
PublisherThe Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Pages268-277
Number of pages10
ISBN (Electronic)9789573079262
Publication statusPublished - 2013 Oct 1
Event25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013 - Kaohsiung, Taiwan
Duration: 2013 Oct 42013 Oct 5

Publication series

NameProceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013

Conference

Conference25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013
CountryTaiwan
CityKaohsiung
Period13/10/413/10/5

ASJC Scopus subject areas

  • Speech and Hearing
  • Language and Linguistics

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  • Cite this

    Chang, T. H., Hsu, Y. C., Chang, C. W., Hsu, Y. C., & Chen, H. C. (2013). 基於特徵為本及使用 SVM 的文本對蘊涵關係的自動推論方法. In Proceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013 (pp. 268-277). (Proceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013). The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).