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ACL
2015

Learning to Adapt Credible Knowledge in Cross-lingual Sentiment Analysis

4 years 3 months ago
Learning to Adapt Credible Knowledge in Cross-lingual Sentiment Analysis
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.
Qiang Chen, Wenjie Li, Yu Lei, Xule Liu, Yanxiang
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACL
Authors Qiang Chen, Wenjie Li, Yu Lei, Xule Liu, Yanxiang He
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