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现代图书情报技术  2014, Vol. 30 Issue (11): 53-58    DOI: 10.11925/infotech.1003-3513.2014.11.08
  情报分析与研究 本期目录 | 过刊浏览 | 高级检索 |
半监督的网络科技信息分类模型
李传席, 张智雄, 刘建华, 钱力
中国科学院文献情报中心 北京 100190
A Semi-supervised Web Scientific and Technical Information Classification Model
Li Chuanxi, Zhang Zhixiong, Liu Jianhua, Qian Li
National Science Library, Chinese Academy of Sciences, Beijing 100190, China
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摘要 

[目的] 开放的网络科技信息网页内容之间区分度较小, 传统基于规则和统计学习的方法无法满足网络科技信息网页分类的具体应用需求.[方法] 通过深入分析网络科技信息主题网页的内容和结构, 利用开放本体等资源实现领域特征的学习, 构建半监督的网络科技信息分类模型.[结果] 实验结果表明提出的方法在网络科技信息分类实验中的精度、召回率和F1值分别达到0.9016、0.8756和0.8884, 相比贝叶斯方法具有明显优势.[局限] 该方法在应用到其他类别的网络科技信息分类时, 仍然需要领域专家提供相关领域的核心种子特征.[结论] 该方法可以满足网络科技信息深度加工的需求, 实现有效的网络科技信息网页分类.

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李传席
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刘建华
钱力
关键词 网络科技信息网络科技信息分类模型开放资源    
Abstract

[Objective] Considering the difference of open Web scientific and techical information is minor, general rule-based and statistical learning methods cannot classify the information effectively for the practical application demands. [Methods] By analyzing the content and structure of Web pages, and utilizing the open resources (such as domain Ontology and thesaurus etc.) to perform the self-learning of domain features, this paper proposes a semi-supervised classification model of scientific and technical information. [Results] The experiment results show that the proposed method achieves the precision of 0.9016, recall of 0.8756 and F1 score of 0.8884 respectively, which are superior to Naive Bayes classification. [Limitations] Applying the proposed method to new domain, the domain seed features need be supplied still. [Conclusions] The proposed method can classify the scientific and technical information effectively and satisfy the demand of the information deep analysis and process.

Key wordsWeb scientific and technical information    Scientific and technical information classification model    Open resources
收稿日期: 2014-05-20     
:  TP181  
  G356  
基金资助:

本文系中国科学院文献情报能力建设专项"网络科技信息自动监测系统二期建设"项目(项目编号:院1306)和国家"十二五"科技支撑计划课题"科技知识组织体系共享服务平台建设"(项目编号:2011BAH10B03)的研究成果之一.

通讯作者: 李传席 E-mail: lichuanxi@mail.las.ac.cn     E-mail: lichuanxi@mail.las.ac.cn
作者简介: 作者贡献声明: 李传席, 张智雄: 提出研究问题, 设计研究框架; 李传席: 研究方法的设计和实现, 以及论文的撰写; 刘建华: 提供部分实验数据和研究思路的讨论; 钱力: 参与实验过程的设计与分析.
引用本文:   
李传席, 张智雄, 刘建华, 钱力. 半监督的网络科技信息分类模型[J]. 现代图书情报技术, 2014, 30(11): 53-58.
Li Chuanxi, Zhang Zhixiong, Liu Jianhua, Qian Li. A Semi-supervised Web Scientific and Technical Information Classification Model. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2014.11.08.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2014.11.08

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