Paper: Cross-lingual Opinion Analysis via Negative Transfer Detection

ACL ID P14-2139
Title Cross-lingual Opinion Analysis via Negative Transfer Detection
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Transfer learning has been used in opin- ion analysis to make use of available lan- guage resources for other resource scarce languages. However, the cumulative class noise in transfer learning adversely affects performance when more training data is used. In this paper, we propose a novel method in transductive transfer learning to identify noises through the detection of negative transfers. Evalua- tion on NLP&CC 2013 cross-lingual opinion analysis dataset shows that our approach outperforms the state-of-the-art systems. More significantly, our system shows a monotonic increase trend in per- formance improvement when more train- ing data are used.