Question Analysis and Answer Passages Retrieval for Opinion Question Answering Systems

• Main Authors: Yu-Ting Liang, 梁玉婷
• Other Authors: Hsin-Hsi Chen
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/51602609622807989234

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 95 === Question answering (QA) systems provide an elegant way for people to access an underlying knowledge base. Humans are not only interested in factual questions but also interested in opinions. In this thesis, an opinion QA system dealing with opinion questions are proposed. We attempt to investigate technologies of question analysis and answer passages retrieval. For question analysis, six opinion question types are defined. A two-layered framework utilizing two question type classifiers is proposed. Algorithms for these two classifiers are discussed. The performance achieves 87.8% in general question classification and 92.5% in opinion question classification. The question’s focus and polarity are detected as well to form an IR query and sieve out relevant sentences which have the same polarity to the question. For answer passages retrieval, three components are introduced. Relevant sentences retrieved by the IR system are further identified whether the focus (Focus Scope Identification) is in a scope of opinion text spans (Opinion Scope Identification) or not, and if yes, whether the polarity of the scope matches with the polarity of the question (Polarity Detection). A total of 18 combinations are proposed and experimented. The best model achieves 40.59% of F-measure using partial match at boundary level. With relevance issues removed, the F-measure of the best model boosts up to 87.18%. We further divide the experiment results by topics. The results indicate difficulties of different topics. We conclude with some yet unsolvable but quite interesting problems to study in the future to build a complete opinion QA system.