Question answering 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. This paper deals with question analysis and answer passage retrieval in opinion QA systems. 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 described. The performance achieves 87.8% in general question classification and 92.5% in opinion question classification. The question focus is detected to form a query for the information retrieval system and the question polarity is detected to retain relevant sentences which have the same polarity as the question. For answer passage retrieval, three components are introduced. Relevant sentences retrieved are further identified whether the focus (Focus Detection) is in a scope of opinion (Opinion Scope Identification) or not, and if yes, whether the polarity of the scope matches with the polarity of the question (Polarity Detection). The best model achieves an F-measure of 40.59% using partial match at the level of meaningful unit. With relevance issues removed, the F-measure of the best model boosts up to 84.96%.
As compared to REF , the question types of holder and majority are not included.
Question Analysis and Answer Passage Retrieval for Opinion Question Answering Systems
ROCLING
Computer Science