Learning to adapt credible knowledge in cross-lingual sentiment analysis

Qiang Chen; Wenjie Li; Yu Lei; Xule Liu; Yanxiang He

Learning to adapt credible knowledge in cross-lingual sentiment analysis

Qiang Chen, Wenjie Li, Yu Lei, Xule Liu, Yanxiang He

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

28 Citations (Scopus)

Abstract

Cross-lingual sentiment analysis is a task of identifying sentiment polarities of texts in a low-resource language by using sentiment knowledge in a resource-Abundant language. While most existing approaches are driven by transfer learning, their performance does not reach to a promising level due to the transferred errors. In this paper, we propose to integrate into knowledge transfer a knowledge validation model, which aims to prevent the negative influence from the wrong knowledge by distinguishing highly credible knowledge. Experiment results demonstrate the necessity and effectiveness of the model.

Original language	English
Title of host publication	ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference
Publisher	Association for Computational Linguistics (ACL)
Pages	419-429
Number of pages	11
Volume	1
ISBN (Electronic)	9781941643723
Publication status	Published - 1 Jan 2015
Event	53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015 - Beijing, China Duration: 26 Jul 2015 → 31 Jul 2015

Conference

Conference	53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015
Country/Territory	China
City	Beijing
Period	26/07/15 → 31/07/15

ASJC Scopus subject areas

Artificial Intelligence
Software

Cite this

Chen, Q., Li, W., Lei, Y., Liu, X., & He, Y. (2015). Learning to adapt credible knowledge in cross-lingual sentiment analysis. In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference (Vol. 1, pp. 419-429). Association for Computational Linguistics (ACL).

Chen, Qiang ; Li, Wenjie ; Lei, Yu et al. / Learning to adapt credible knowledge in cross-lingual sentiment analysis. ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference. Vol. 1 Association for Computational Linguistics (ACL), 2015. pp. 419-429

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title = "Learning to adapt credible knowledge in cross-lingual sentiment analysis",

abstract = "Cross-lingual sentiment analysis is a task of identifying sentiment polarities of texts in a low-resource language by using sentiment knowledge in a resource-Abundant language. While most existing approaches are driven by transfer learning, their performance does not reach to a promising level due to the transferred errors. In this paper, we propose to integrate into knowledge transfer a knowledge validation model, which aims to prevent the negative influence from the wrong knowledge by distinguishing highly credible knowledge. Experiment results demonstrate the necessity and effectiveness of the model.",

author = "Qiang Chen and Wenjie Li and Yu Lei and Xule Liu and Yanxiang He",

year = "2015",

month = jan,

day = "1",

language = "English",

volume = "1",

pages = "419--429",

booktitle = "ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference",

publisher = "Association for Computational Linguistics (ACL)",

note = "53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015 ; Conference date: 26-07-2015 Through 31-07-2015",

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Chen, Q, Li, W, Lei, Y, Liu, X & He, Y 2015, Learning to adapt credible knowledge in cross-lingual sentiment analysis. in ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference. vol. 1, Association for Computational Linguistics (ACL), pp. 419-429, 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015, Beijing, China, 26/07/15.

Learning to adapt credible knowledge in cross-lingual sentiment analysis. / Chen, Qiang; Li, Wenjie; Lei, Yu et al.
ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference. Vol. 1 Association for Computational Linguistics (ACL), 2015. p. 419-429.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Liu, Xule

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AB - Cross-lingual sentiment analysis is a task of identifying sentiment polarities of texts in a low-resource language by using sentiment knowledge in a resource-Abundant language. While most existing approaches are driven by transfer learning, their performance does not reach to a promising level due to the transferred errors. In this paper, we propose to integrate into knowledge transfer a knowledge validation model, which aims to prevent the negative influence from the wrong knowledge by distinguishing highly credible knowledge. Experiment results demonstrate the necessity and effectiveness of the model.

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PB - Association for Computational Linguistics (ACL)

T2 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015

Y2 - 26 July 2015 through 31 July 2015

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Chen Q, Li W, Lei Y, Liu X, He Y. Learning to adapt credible knowledge in cross-lingual sentiment analysis. In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference. Vol. 1. Association for Computational Linguistics (ACL). 2015. p. 419-429

Learning to adapt credible knowledge in cross-lingual sentiment analysis

Abstract

Conference

ASJC Scopus subject areas

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