基于引文内容分析的高被引论文主题识别研究  

Topic Identification of Highly Cited Papers Based on Citation Content Analysis

作　　者：祝清松[1] 冷伏海[1] 

机构地区：[1]中国科学院国家科学图书馆

出　　处：《中国图书馆学报》2014年第1期39-49,共11页Journal of Library Science in China

摘　　要：基于被引次数的引文分析无法直接揭示论文的研究内容,利用关键词或从标题、摘要和全文中抽取的主题词很难客观反映论文的被引原因。本文以碳纳米管纤维研究领域的高被引论文为研究对象进行引文内容抽取和主题识别,经人工判读验证:基于引文内容分析的高被引论文识别的核心主题能够较好地揭示高被引论文的被引原因(引用动机),而且与论文的研究内容相符合;与基于全文、基于标题和摘要的主题识别相比,在引文内容分析基础上识别的主题具有更好的主题代表性,能够有效揭示被引文献的研究内容,是对原文相关信息的重要补充。本文的实验表明基于引文内容分析的高被引论文主题识别是可行而且有效的。Citation analysis based on citation frequency fails to directly reveal research contents of papers, neither can it ob- jectively reflect the reason for citation with keywords or topic words extracted from titles, abstracts and full-texts. Taking highly- cited papers of carbon nanotube fiber field as examples, this paper extracts citation contents and identifies the topics. Through human interpretation, it verifies that the core topics of idemifying highly-cited papers based on citation content analysis can bet- ter reveal the reason for citation （i. e., motivation for citation） of highly-cited papers and accord with research contents of pa- pers. Compared with the topic identification based on titles, abstracts and full-texts, the topics identified on the basis of citation content analysis have better representativeness and can effectively reveal research contents of cited papers, and are important supplement to related information in original texts. The experiment results of this paper prove the feasibility and validity of the topic identification through citation content analysis on highly cited papers. 4 figs. 4 tabs. 31 refs.

关 键 词：引文内容分析 主题识别 高被引论文 引用动机 

分 类 号：G250[文化科学—图书馆学]