收稿日期: 2016-10-11
修回日期: 2016-12-11
网络出版日期: 2017-01-05
基金资助
本文系武汉大学自主科研项目（人文社会科学）研究成果之一，并得到“中央高校基本科研业务费专项资金”和中国科技信息研究所与武汉大学合作项目“科学文献的语义功能识别与深度利用”资助。
Design and Implementation of Scientific Literature Analysis System Based on Term Function Recognition
Received date: 2016-10-11
Revised date: 2016-12-11
Online published: 2017-01-05
[目的/意义] 从学术文本词汇功能的角度出发，考虑科研文献中词汇的语义功能，设计和实现一个基于词汇功能识别的科研文献分析系统，在一定程度上弥补现有科研文献分析系统的不足之处。[方法/过程] 首先阐述学术文本词汇功能的定义及其识别研究的现状进展；在此基础上，对系统思路、功能模块进行设计；最后，选取1994-2013年CNKI中计算机领域的文献作为数据来源，实现一个基于词汇功能识别的科研文献分析系统CS-LAS。[结果/结论] CS-LAS可以满足科研工作者更为细粒度的信息需求，对于传统学术数据库的检索结果有一定的优化，同时实现对某一学科的研究热点和研究趋势的合理把握和可视化呈现。
李信 , 程齐凯 , 刘兴帮 . 基于词汇功能识别的科研文献分析系统设计与实现[J]. 图书情报工作, 2017 , 61(1) : 109 -116 . DOI: 10.13266/j.issn.0252-3116.2017.01.013
[Purpose/significance] From term function in scientific text perspective, we took the semantic function of words in scientific literatures, designed and implemented a scientific literature analysis system based on term function recognition, to make up for the deficiency of existing literature analysis system at a certain extent.[Method/process] This article firstly expounded the definition and recognition of term function, on the basis of which, itdesigned the system thinking and function modules. Finally, literatures were chosen in computer science in CNKI from 1994-2013, to implement a literature analysis system called CS-LAS.[Result/conclusion] CS-LAS can satisfy researchers' information needs form a more fine-grained perspective, which have optimized the results of traditional academic database. At the meantime, this system can also grasp the hot spot and research trend of a topic and visualize the results.
[1] SPANGLER S, WILKINS A D, BACHMAN B J, et al. Automated hypothesis generation based on mining scientific literature[C]//Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. New York:ACM, 2014:1877-1886.
[2] 陈超美. CiteSpaceⅡ:科学文献中新趋势与新动态的识别与可视化[J]. 陈悦, 侯剑华,译.情报学报, 2009, 28(3):401-421.
[3] ECK N J, WALTMAN L. Software survey:VOSviewer, a computer program for bibliometric mapping[J]. Scientometrics, 2010, 84(2):523-538.
[4] 崔雷, 刘伟, 闫雷,等. 文献数据库中书目信息共现挖掘系统的开发[J]. 现代图书情报技术, 2008(8):70-75.
[5] WANG X, CHENG Q, LU W. Analyzing evolution of research topics with NEViewer:a new method based on dynamic co-word networks[J]. Scientometrics, 2014, 101(2):1253-1271.
[6] 崔雷, 胡海荣, 李纪宾, 等. 文献计量学共引分析系统设计与开发[J]. 情报学报, 2000, 19(4):308-312.
[7] 程齐凯. 学术文献词汇功能识别[D]. 武汉:武汉大学,2015.
[8] 陈卫东, 范艳峰. 基于三次样条插值的支持向量机研究[J]. 计算机工程与设计, 2009, 30(7):1722-1724.
[9] 张敏贵, 潘泉, 张洪才,等. 基于支持向量机的人脸分类[J]. 计算机工程, 2004, 30(11):110-112.
[10] KONDO T, NANBA H, TAKEZAWA T, et al. Technical trend analysis by analyzing research papers' titles[M]//Human language technology:challenges for computer science and linguistics.Berlin:Springer, 2009:512-521.
[11] NANBA H, KONDO T, TAKEZAWA T. Automatic creation of a technical trend map from research papers and patents[C]//International workshop on patent information retrieval. New York:ACM, 2010:11-16.
[12] TSAI C T, KUNDU G, ROTH D. Concept-based analysis of scientific literature[C]//ACM international conference on information & knowledge management. New York:ACM, 2013:1733-1738.
[13] GUPTA S, MANNING C D. Analyzing the dynamics of research by extracting key aspects of scientific papers[C]//Proceedings of ijcnlp. Thailand:The Association for Computer Linguistics, 2011:1-9.
[14] HUANG S, WAN X. AKMiner:domain-specific knowledge graph mining from academic literatures[M]//Web information systems engineering-WISE 2013. Berlin:Springer, 2013:241-255.
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