期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

学术文献引文上下文自动识别研究 被引量：20

Research on Automatic Recognition of Academic Citation Context
原文传递
导出
摘要 [目的 /意义]引文内容分析能够帮助揭示文献引用关系的深层语义内涵,而引文上下文识别作为引文内容分析的基础显得尤为重要。[方法 /过程]梳理已有引文上下文研究的现状,总结当前引文上下文识别的不足,在此基础上归纳引文上下文识别的5类特征,并采用文本分类和序列标注两种方法开展引文上下文自动识别实验。[结果 /结论]实验结果表明,本文提出的特征能够很好地提升引文上下文识别效果,且基于文本分类的SVM分类效果要优于基于序列标注的CRF。 [ Purpose/significance] Citation content analysis can help to reveal the deep semantic influence of litera- ture citation relations, and citation context identification as a basis for content analysis is particularly important. [ Meth- od/process] This paper reviews the latest development of researches of citation context and summarizes the deficiencies in citation context identification. Based on which five categories of citation context identification features are proposed. Be- sides, this paper also conducts an automatic identification experiment by utilizing text classification and sequence labeling. [ Result/conclusion] A significant improvement over baseline method shows the effectiveness of our features. Besides, the text classification based SVM method performs better than the sequence labeling based CRF method.
作者 雷声伟 陈海华 黄永 陆伟
机构地区 武汉大学信息管理学院 武汉大学信息检索与知识挖掘研究所
出处 《图书情报工作》 CSSCI 北大核心 2016年第17期78-87,共10页 Library and Information Service
基金 国家自然科学基金面上项目"面向词汇功能的学术文本语义识别与知识图谱构建"(项目编号:71473183)研究成果之一
关键词 引文上下文 引文内容分析 支持向量机 条件随机场 隐式上下文 citation context citation analysis support vector machine condition random field no-explicit context
分类号 G350 [文化科学—情报学]
  • 相关文献

参考文献32

  • 1刘洋,崔雷.引文上下文在文献内容分析中的信息价值研究[J].图书情报工作,2014,58(6):101-104. 被引量：13
  • 2ABU-JBARA A, EZRA J, RADEV D R. Purpose and polarity of citation : towards NLP-based bibliometrics [ C ]//Proceedings of the 2013 conference of the North Americar~ Chapter of the Association for Computational Linguistics: human language technologies. At- lanta: Association for Computational Linguistics, 2013: 596- 606.
  • 3陆伟,孟睿,刘兴帮.面向引用关系的引文内容标注框架研究[J].中国图书馆学报,2014,40(6):93-104. 被引量：72
  • 4COLLINS H M. The TEA set: tacit knowledge and scientific net- works[J]. Social studies of science, 1974, 4(2) : 165 -185.
  • 5CANO V. Citation behavior: classification, utility, and location [ J]. Journal of the American Society for Information Science, 1989, 40(4) : 284 -290.
  • 6CHUBIN D E, MOITRA S D. Content analysis of references: ad- junct or alternative to citation counting? [ J]. Social studies of sci- ence, 1975, 5(4) :423 -441.
  • 7NANBA H, OKUMURA M. Towards Multi-paper summarization u- sing reference information[ C]// Proceedings of The 1999 Interna- tional Joint Conference on Artificial Intelligence. Stockholm: AAAI, 1999 : 926 - 931.
  • 8ABU -JBARA A, RADEV D. Coherent citation-based summariza-tion of scientific papers [ C ]//Proceedings of the 49th annual meet- ing of the Association for Computational Linguistics: human lan- guage technologies-volume I. Portland: Association for Computa- tional Linguistics, 2011 : 500 -509.
  • 9ATHAR A. Sentiment analysis of citations using sentence structure -based features [ C ]//Proceedings of the ACL 2011 student ses- sion. Portland : Association for Computational Linguistics, 2011 : 81 - 87.
  • 10ANGROSH M A, CRANEFIELD S, STANGER N. Context identi- fication of sentences in related work sections using a conditional random field: towards intelligent digital libraries[ C ]//Proceedings of the 10th annual joint conference on digital libraries. Gold Coast : ACM, 2010:293-302.

二级参考文献89

  • 1R. McDonald, K. Crammer and F. Pereira. Online large-margin training of dependency parsers [C]// Proc. of ACL. Ann Arbor, USA: 2005, 91-98.
  • 2R. McDonald and F. Pereira. Online learning of approximate dependency parsing algorithms[C]//Proc. of EACL. Trento, Italy: 2006, 81-88.
  • 3H. Yamada and Y. Matsumoto. Statistical dependency analysis with support vector machines[C]//Proc, of IWPT. Nancy, France: 2003, 195-206.
  • 4J. Nivre and M. Scholz. Deterministic dependency parsing of English text [C]//Proc. of COLING. Switzerland: 2004, 64-70.
  • 5R. McDonald and J. Nivre. Characterizing the errors of data-driven dependency parsing models[C]//Proc. of EMNLP CoNLL. Prague, Czech: 2007, 122-131.
  • 6F. Sha and F. Pereira. Shallow parsing with conditional random fields[C]//Proc, of NAACL. Edmonton, Canada: 2003, 213-220.
  • 7Y. Wu, J. Yang and Y. Lee. Multilingual deterministic dependency parsing framework using modified finite Newton method support vector machines[C]//Proc, of EMNLP-CoNLL. Prague, Czech: 2007, 1175-1181.
  • 8Y. Cheng, M. Asahara and Y Matsumoto. Multi-lingual dependency parsing at NAIST [C]//Proc. of CoNLL. New York City, USA: 2006, 191-195.
  • 9J. Hall, J. Nilsson, J. Nivre, et. al. Single Malt or blended? A study in multilingual parser optimization [C]//Proc. of EMNLP-CoNLL. Prague, Czech: 2007, 933-939.
  • 10M. P. Marcus, B. Santorini and M. A. Marcinkiewicz. Building a large annotated corpus of English: the Penn Treebank [J]. Computational Linguistics, 1993, 19(2): 313-330.

共引文献88

同被引文献206

引证文献20

二级引证文献126

图书情报工作
2016年 第17期

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