期刊文献+

一种基于文本相似计算的校园智能问答系统设计 被引量:2

Design of a Campus Intelligent Question Answering System Based on Text Similarity Computing
下载PDF
导出
摘要 问答系统是继搜索引擎之后诞生的又一用来帮助用户在海量数据中提高检索效率的系统。目前常见的问答系统主要应用于商业领域,针对在校学生这一特定用户群体的智能问答系统并不多见。本文在分析问答系统现状以及建设难点的基础上,提出了一种面向学校这一特定领域的,用来提升在校学生学习、生活质量的校园智能问答系统建设方法,并从语料库建设方法、问题及答案提取等多个方面进行了详细阐述。 Question Answering System(QAS)is a system that appears after the search engine to improve the retrieval efficiency of users in massive data.At present,the common QAS is mainly used in the business field,but the intelligent QAS for the students in school is rare.Based on the analysis of the current situation and difficulties in the construction of question answering system,this paper puts forward a construction method of campus intelligent question answering system,which is oriented to the specific field of school and is used to improve the quality of students’study and life.It also elaborates on the construction method of corpus,the extraction of questions and answers and so on.
作者 李月 周江 LI Yue;ZHOU Jiang(School of Information,Guangdong Communication Polytechnic,Guangzhou 510650,China)
出处 《现代信息科技》 2019年第22期9-12,17,共5页 Modern Information Technology
基金 中国交通教育研究会2018-2020年度教育科学研究课题(项目编号:交教研1802-141),主持人:李月 教育部职业院校信息化教学指导委员会2018-2020年信息化教学研究课题(项目编号:2018LXB0301),主持人:李月 广东省高职教育信息技术教指委2018年教育教学改革课题(项目编号:XXJZW2018023),主持人:周江
关键词 问答系统 文本处理 相似度计算 语料库 question answering system text processing similarity computing corpus
  • 相关文献

参考文献6

二级参考文献33

  • 1陈银凤.RSS技术的应用和发展趋势探讨[J].内蒙古财经学院学报(综合版),2008,6(1):98-102. 被引量:1
  • 2余战秋.中文分词技术及其应用初探[J].电脑知识与技术(认证考试),2004(11M):81-83. 被引量:11
  • 3唐慧丰,谭松波,程学旗.基于监督学习的中文情感分类技术比较研究[J].中文信息学报,2007,21(6):88-94. 被引量:136
  • 4Blum A, Mitchell T. Combining labeled and unlabeled data with co-training[-C]//Proceedings of the eleventh annual conference on computational, learning theory. ACM, 1998~ 92-100.
  • 5Dietterich T G. Ensemble. methods in machine learning EM~. Multiple classifier systems. Springer Berlin Hei- delberg, 2000:1 15.
  • 6Whitehead M, Yaeger L. Sentiment mining using en- semble classification models~M~. Innovations and Ad- vances in Computer Sciences and Engineering. Spring- er Netherlands, 2010: 509-514.
  • 7Su Y, Zhang Y, Ji D, et al. Ensemble learning for sentiment classification[M]//Chinese Lexieal Seman- tics. Springer Berlin Heidelberg, 2013: 84-93.
  • 8Pang B, Lee L, Vaithyanathan S. Thumbs up?.. sen- timent classifica'don using machine learning teeh- niques~C]//Proceedings of the ACL-02 conference on empirical methods in natural language processing- Volume 10. Association for Computational Linguis- tics, 2002: 79-86.
  • 9Cui H, Mittal V, Datar M. Comparative experiments on sentiment classification for online product reviews [C]//Proceedings of the AAAI. 2006, 6: 1265-1270.
  • 10Pang B, Lee L. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cutsECJ//Proceedings of the 42nd Annual Meeting on Association for Computational Linguis- tics. Association for Computational Linguistics, 2004: 271.

共引文献50

同被引文献13

引证文献2

二级引证文献2

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