Research On Question Classification And Candidate Answer Sentences Extraction In Chinese Question Answering System

Question Answering (QA) is the next generation of search engine which is related to natural language processing, information retrieval and etc. A Question Answering contains question classification, query expansion, text retrieval, answer extraction and answer selection. Question classification and answer extraction are the most important. Natural language processing is used to research the two key techniques which are question classification and answer sentence extraction in this paper.Question classification as the most important model has two functions. First, it can efficiently reduce the space of candidate answers to improve system's performance, and the second is that the question type can decide an optimum strategy of answer extraction. Because of the difference between text classification and question classification, a new method using support vector machine and the related words of Subject-Predicate structure is proposed in this paper. This method substantially reduces the noise, and stresses the main features of question classification to improve performance. At the same time, because general hierarchical is not good on question classification, this paper proposes a new method for Chinese question hierarchical classification. This method combines the key class features with the question syntactic features to classify questions. Since this method extracts the syntax features and adds syntax information into question classification, at last, the precision of the coarse classes reaches 88.25% and fine classes reaches 73.15%, respectively improves nearly ten percent than the traditional hierarchy classification, proving this method is effective.Candidate answer sentence extraction is an important part of answer extraction, which directly effects question answering system's performance. Meeting the difference of text retrieval and sentence retrieval, a new method using integrating anaphora resolution, improved edit distance and vector space model is proposed in this paper. On factoid question type, the precision of answer sentence retrieval is up to 84.71%. Answer sentence decision overcomes bag of words model by using mapping dependencies trees between query and candidate...