An answer extraction method based on discourse structure and rank learning

Abstract

For the complex questions of Chinese question answering system such as 'why', 'how' these non-factoid questions, we proposed an answer extraction method using discourse structures features and ranking algorithm. This method takes the j udge problem of answers relevance as learning to rank answers. First, the method analyses questions to generate the query string, and then uses rhetorical structure theory and the natural language processing technology of vocabulary, syntax, semantic analysis to analyze the retrieved documents, so as to determine the inherent relationship between paragraphs or sentences and generate the answer candidate paragraphs or sentences. Thirdly, construct the answer ranking model, extract five group features of similarity features, density and frequency features, translation features, discourse structure features and external knowledge features to train ranking model. Finally, re-ranking the answers with the training model and find the optimal answers. Experiments show that the proposed method can effectively improve the accuracy and quality of non factoid answers.

Two Discourse Tree - Based Approaches to Indexing Answers

“…Discourse analysis was proved to be useful in different aspects of question-answering: answer extraction (Zong et al, 2011), modeling rationale in design questions (Kim et al, 2004), query expansion based on relations between sequential questions (Sun and Chai, 2007), etc.…”