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A novel approach for agent ontology and its application in question answering
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作者 郭庆琳 《Journal of Central South University》 SCIE EI CAS 2009年第5期781-788,共8页
The information integration method of semantic web based on agent ontology(SWAO method) was put forward aiming at the problems in current network environment,which integrates,analyzes and processes enormous web inform... The information integration method of semantic web based on agent ontology(SWAO method) was put forward aiming at the problems in current network environment,which integrates,analyzes and processes enormous web information and extracts answers on the basis of semantics. With SWAO method as the clue,the following technologies were studied:the method of concept extraction based on semantic term mining,agent ontology construction method on account of multi-points and the answer extraction in view of semantic inference. Meanwhile,the structural model of the question answering system applying ontology was presented,which adopts OWL language to describe domain knowledge from where QA system infers and extracts answers by Jena inference engine. In the system testing,the precision rate reaches 86%,and the recalling rate is 93%. The experimental results prove that it is feasible to use the method to develop a question answering system,which is valuable for further study in more depth. 展开更多
关键词 agent ontology question answering semantic web concept extraction answer extraction natural language processing
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Answer Ranking with Discourse Structure Feature 被引量:1
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作者 Mao Cunli Chen Fangqiong +2 位作者 Yu Zhengtao Guo Jianyi Zong Huanyun 《China Communications》 SCIE CSCD 2012年第3期110-123,共14页
For the complex questions of Chinese question answering system, we propose an answer extraction method with discourse structure feature combination. This method uses the relevance of questions and answers to learn to ... For the complex questions of Chinese question answering system, we propose an answer extraction method with discourse structure feature combination. This method uses the relevance of questions and answers to learn to rank the answers. Firstly, the method analyses questions to generate the query string, and then submits the query string to search engines to retrieve relevant documents. Sec- ondly, the method makes retrieved documents seg- mentation and identifies the most relevant candidate answers, in addition, it uses the rhetorical relations of rhetorical structure theory to analyze the relationship to determine the inherent relationship between para- graphs or sentences and generate the answer candi- date paragraphs or sentences. Thirdly, we construct the answer ranking model,, and extract five feature groups and adopt Ranking Support Vector Machine (SVM) algorithm to train ranking model. Finally, it re-ranks the answers with the training model and fred the optimal answers. Experiments show that the proposed method combined with discourse structure features can effectively improve the answer extrac- ting accuracy and the quality of non-factoid an- swers. The Mean Reciprocal Rank (MRR) of the an- swer extraction reaches 69.53%. 展开更多
关键词 complex questions discourse structure learning to rank answer extracting
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