期刊文献+

基于网络的中文问答系统及信息抽取算法研究 被引量:46

Research on Web-based Chinese Question Answering System and Answer Extraction
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摘要 问答系统 (QuestionAnsweringSystem)能用准确、简洁的答案回答用户用自然语言提出的问题。目前多数问答系统利用大规模文本作为抽取答案的知识库 ,而网络上丰富的资源为问答系统提供了另外一种良好的知识来源 ,对于回答简短、基于事实的问题非常有效。本文对基于网络的问答系统研究现状作了简要的介绍 ,分析了网络信息的特点。我们提出了一种基于语句相似度计算的答案抽取方法 ,在此基础上实现了一个基于网络的中文问答系统。该系统只利用网络搜索引擎返回结果中的摘要部分作为答案抽取的资源 ,从而节省了下载、分析网络源文本的时间。实验结果表明该系统对人名、数量及时间类型的问题效果显著 ,对测试问题集的MRR值达到 0 5 1。 Question Answering System can give users precise answer to the question presented in natural language. Currently, most of question answering systems use large scaled corpus as knowledge base to extract answer. However, the abundant web resource provides another ideal knowledge source for question answering system. The research result shows that using web resource as the information source for question answering system can get good performance for simple and factoid based questions. This paper presents an answer extraction method based on the computation of sentence similarity between the question sentence and the candidate answer sentence. We also developed a web based Chinese QA system. This system only utilizes the 'text snippet' in the feedback of the web search engine as data resource for answer extraction. The experiment result indicates that the system can get relatively good results for the questions of the types of PERSON, TIME and NUMBER; the MRR of all questions is 0 51.
出处 《中文信息学报》 CSCD 北大核心 2004年第3期24-31,共8页 Journal of Chinese Information Processing
关键词 计算机应用 中文信息处理 问答系统 句子相似度 信息抽取 computer application Chinese information processing question answering system sentence similarity information extraction
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参考文献7

  • 1E.Voorhees.Overview of the TREC-9 Question Answering Track [A].In:Proceedings of the 9th Text Retrieval Conference (TREC9)[C],NIST,Gaithersburg,MD,2000,71-80.
  • 2C.Kwok,O.Etzioni,and D.S.Weld.Scaling Question Answering to the Web [A].In:Proceedings of the 10th World Wide Web Conference (WWW2001)[C],Hong Kong,2001,150-161.
  • 3Dell Zhang,Wee Sun Lee.A Web-based Question Answering System[A].In:Proceedings of the SMA Annual Symposium 2003 [C],NUS,Singapore,Jan 2003.
  • 4Long Hao,Cai Dongfeng.A Word-maching-based Method of Computing the Degree of Similarity Between Sentences[A].Advance in Computation of Oriental Languages.20th International Conference on Computer Processing of Oriental Languages Shenyang [C],China,August
  • 5崔桓 蔡东风 苗雪雷.问答系统中疑问句理解的分析研究[A]..中国人工智能进展[C].北京邮电大学出版社,2003,11.1023-1027.
  • 6S.Dumais,M.Banko,E.Brill,J.Lin and A.Ng.Web Question Answering:Is More Always Better? [A].In:proceedings of SIGIR02 [C],Aug 2002,291-298.
  • 7郑实福,刘挺,秦兵,李生.自动问答综述[J].中文信息学报,2002,16(6):46-52. 被引量:167

二级参考文献11

  • 1[8]Ulf Hermjakob. Parsing and Question Classification for Question Answering. Proceeding of the workshop on Open-Domain Question Answering at ACL-2001
  • 2[9]Eugene Agichtein, Steve Lawrence, Luis Gravano. Learning Search Engine Specific Query Transformations for Question Answering. ACM 2001,169- 178
  • 3[10]Soo-Min Kim, ae-Ho Baek, Sang-Beom Kim, Hae-Chang Rim Question Answering Considering Semantic Categories and Co-occurrence Density. Proceedings of the night Text Retrieval Conference (TREC-9)
  • 4[11]Marius Pasca, Sanda Harabagiu. High-Performance Question/Answering. 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ( Sigir-01 ). New Orleans, LA. September 9 - 13,2001
  • 5[1]Ittycheriah,M. Franz,W-J Zhu,A. Ratnaparkhi. IBM's Statistical Question Answering System. Proceedings of the night Text Retrieval Conference (TREC-9)
  • 6[2]D. Elworthy. Question Answering Using a Large NLP System. Proceedings of the night Text Retrieval Conference (TREC-9)
  • 7[3]L. Wu,X-j Huang,Y. Guo,B. Liu,Y. Zhang. FDU at TREC-9:CLIR,Filtering and QA Tasks. Proceedings of the night Text Retrieval Conference(TREC-9)
  • 8[4]R.J. Cooper, S. M. Rüger. A Simple Question Answering System. Proceedings of the night Text Retrieval Conference(TREC-9)
  • 9[5]C.L.A. Clarke, G. V. Cormack, D. I. E. Kisman, T. R. Lynam. Question Answering by Passage Selection. Proceedings of the night Text Retrieval Conference (TREC-9)
  • 10[6]S-M Kim,D-H Baek,S-B Kim,H-C Rim. Question Answering Considering Semantic Categories and CoOccurrence Density. Proceedings of the night Text Retrieval Conference(TREC-9)

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引证文献46

二级引证文献253

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