Multi-Class Bootstrapping Learning Aspect-related Terms for Aspect Identification

摘要：

Aspect identification in entity reviews involving multiple aspects is a top priority for aspect-based opinion mining. Most of previous studies adopted machine learning techniques taking it as a multi-class text classification task. However, since building labeled training data is often expensive, some researchers put more interest in unsupervised techniques. With the subject of online restaurant reviews, this paper presents a new multi-class bootstrapping algorithm to learn aspect-related terms to be used for aspect identification. Experimental results demonstrate that our method without requiring labeled training data achieves good performance in comparison to the state-of-the-art supervised learning techniques.

关键词： Aspect-related terms aspect identification bootstrapping opinion mining

作者: Chunliang ZHANG Jingbo ZHU

作者单位: Natural Language Lab, Northeastern University Foreign Studies College, Northeastern University Natural Language Lab, Northeastern University Shenyang, China

会议类型: 国际会议

会议名称: International Conference on Natural Language Processing and Knowledge Engineering(IEEE自然语言处理与知识工程国际会议 IEEE NLP-KE 2009)

会议地点: 大连

会议语种:英文

页码: 1-6

在线出版日期: 2009-09-24（万方平台首次上网日期，不代表论文的发表时间）