A Mixed Model for Cross Lingual Opinion Analysis

摘要：

　　The performances of machine learning based opinion analysis systems are always puzzled by the insufficient training opinion corpus.Such problem becomes more serious for the resource-poor languages.Thus, the cross-lingual opinion analysis (CLOA) technique, which leverages opinion resources on one (source) language to another (target) language for improving the opinion analysis on target language, attracts more research interests.Currently, the transfer learning based CLOA approach sometimes falls to over fitting on single language resource, while the performance of the co-training based CLOA approach always achieves limited improvement during bi-lingual decision.Target to these problems, in this study, we propose a mixed CLOA model, which estimates the confidence of each monolingual opinion analysis system by using their training errors through bilingual transfer self-training and co-training, respectively.By using the weighted average distances between samples and classification hyper-planes as the confidence, the opinion polarity of testing samples are classified.The evaluations on NLP&CC 2013 CLOA bakeoff dataset show that this approach achieves the best performance, which outperforms transfer learning and co-training based approaches.

关键词： Cross lingual Opinion Analysis Transfer Self-Training Co Training Mixed Model

作者: Lin Gui Ricky Cheung Ruifeng Xu Jun Xu Li Yuan Yuanlin Yao Jiyun Zhou Qiaoyun Qiu Shuwei Wang Kam-Fai Wong

作者单位: Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, Shenzhen Social Analytics (Hong Kong) Co.Ltd., Hong Kong Department of System Engineering and Engineering Management, The Chinese University of Hong Kong, Ho

会议类型: 国际会议

会议名称: Second CCF Conference,NLPCC2013(第二届自然语言处理与中文计算会议)

会议地点: 重庆

会议语种:英文

页码: 93-104

在线出版日期: 2013-11-15（万方平台首次上网日期，不代表论文的发表时间）