Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55397
Title: Learning to adapt credible knowledge in cross-lingual sentiment analysis
Authors: Chen, Q
Li, W
Lei, Y
Liu, X
He, Y
Issue Date: 2015
Publisher: Association for Computational Linguistics (ACL)
Source: 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, 26-31 July 2015, v. 1, p. 419-429 How to cite?
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.
URI: http://hdl.handle.net/10397/55397
ISBN: 9781941643723
Appears in Collections:Conference Paper

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