期刊文献+

基于“为什么”问句的中文问答系统研究 被引量:1

Research of Chinese Question Answering System Based on the"Why"Interrogative Sentences
下载PDF
导出
摘要 目前的中文问答系统主要是针对有关命名实体的问句进行回答,而对"为什么"开头的问句研究并不多。本研究主要是以搜索引擎返回的网络摘要,从摘要中取得因果关系的句子,再根据预先设定好的因果模式进行权重计算,将权重大的句子返回给用户。实验结果表明,该系统效果显著,对测试问句集的MRR值达到了0.52。 At present,the Chinese Question Answering System was mostly aimed at some questions of named entities to give answers,while few researches were aimed at"Why"interrogative Sentences.In this paper,cause-effect relationship sentences from some snippets of the Search Engine were extracted,and then the weight of every sentence was measured according to the causeeffect patterns.The experiment results indicated that the system could get relatively good results,with the MRR of all questions at 0.52.
作者 陈玉
出处 《农业网络信息》 2010年第11期90-91,共2页 Agriculture Network Information
关键词 问答系统 模式 距离 question answering system patterns distance
  • 相关文献

参考文献3

  • 1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:165
  • 2刘开瑛.基于互联网的多层次汉语语料库构建研究[A],中文信息处理若干重要问题[M],北京:科学出版社,2003.136-146.
  • 3David R. Ramamonjisoa. Finding Relevant Answers in Question Answering System Contest: Proceedings of NTCIR-4, April 2003 - June 2004[C]. Tokyo: NIL.

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)

共引文献165

同被引文献2

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部