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.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Qiang Chen, Wenjie Li, Yu Lei, Xule Liu, Yanxiang He
• Keywords: ACL 2015, Computational Linguistics