Computer Science ›› 2017, Vol. 44 ›› Issue (6): 216-221.doi: 10.11896/j.issn.1002-137X.2017.06.036

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Research on Multi-domain Natural Language Question Understanding

YE Zhong-lin, JIA Zhen and YIN Hong-feng   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Question understanding is one of the main tasks of question answering system.Current question understan-ding methods aim to solve semantic understanding of simple sentences or specific structure sentences.The method proposed in this paper addresses multi-domain question understanding which includes people,movie,music,book,game,and application domains.Firstly,the question classification based on CRF algorithm and the subject recognition based on CRF algorithm approach are presented.And then the prediction dictionary and semantic analysis are applied to recognize prediction.Finally,the prediction disambiguation method is proposed to deal with the problem that prediction in question has different ways of expression.Experimental results show that the average F-measure value is 93.88% and 92.44% in question classification and semantic analysis experiments.The average accuracy is 91.03% and 81.78% in the prediction recognition and question understanding.Thus,the works in this paper can meet the needs of question understanding.

Key words: QA system,Question understanding,Prediction disambiguation,Question classification,Subject recognition

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