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

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

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 proceeding › Conference 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 contribution	Textual entailment recognition using textual features and SVM
Original language	Chinese (Traditional)
Title of host publication	Proceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013
Publisher	The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Pages	268-277
Number of pages	10
ISBN (Electronic)	9789573079262
Publication status	Published - 2013 Oct 1
Event	25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013 - Kaohsiung, Taiwan Duration: 2013 Oct 4 → 2013 Oct 5

Publication series

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

Conference

Conference	25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013
Country/Territory	Taiwan
City	Kaohsiung
Period	2013/10/04 → 2013/10/05

ASJC Scopus subject areas

Speech and Hearing
Language and Linguistics

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).

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. / Chang, Tao Hsing; Hsu, Yao Chi; Chang, Chung Wei et al.
Proceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2013. p. 268-277 (Proceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chang, TH, Hsu, YC, Chang, CW, Hsu, YC & Chen, HC 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. Proceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013, The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), pp. 268-277, 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013, Kaohsiung, Taiwan, 2013/10/04.

Chang TH, Hsu YC, Chang CW, Hsu YC, Chen HC. 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. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP). 2013. p. 268-277. (Proceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013).

Chang, Tao Hsing ; Hsu, Yao Chi ; Chang, Chung Wei et al. / 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. Proceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2013. pp. 268-277 (Proceedings of the 25th Conference on Computational Linguistics and Speech Processing, ROCLING 2013).

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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%.",

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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

Abstract

Publication series

Conference

ASJC Scopus subject areas

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