Leveraging social Q&A collections for improving complex question answering. (January 2015) - Wu, Youzheng; Hori, Chiori; Kashioka, Hideki; Kawai, Hisashi
Abstract : Highlights: The proposed approach leverages social Q&A collections to improve automatic complex QA system. There is no need to manually collect training Q&A pairs that are necessary for supervised machine learning approaches. Extensive comparison experiments are conducted, i.e., LexRank, question-specific and translation-based approaches are compared. Experiments on the extension of NTCIR 2008 test questions indicate that the proposed approach is more effective. Abstract: This paper regards social question-and-answer (Q&A) collections such as Yahoo! Answers as knowledge repositories and investigates techniques to mine knowledge from them to improve sentence-based complex question answering (QA) systems. Specifically, we present a question-type-specific method (QTSM) that extracts question-type-dependent cue expressions from social Q&A pairs in which the question types are the same as the submitted questions. We compare our approach with the question-specific and monolingual translation-based methods presented in previous works. The question-specific method (QSM) extracts question-dependent answer words from social Q&A pairs in which the questions resemble the submitted question. The monolingual translation-based method (MTM) learns word-to-word translation probabilities from all of the social Q&A pairs without considering the question or its type. Experiments on the extension of the NTCIR 2008 Chinese test data set demonstrate that our models that exploit social Q&A collections are significantly more effective than baseline methods such as LexRank. The performance ranking of these methods is QTSM > QSM, MTM. The largest F3 improvements in our proposed QTSM over QSM and MTM reach 6.0% and 5.8%, respectively.