文章摘要

李纲,巴志超.共词分析过程中的若干问题研究[J].中国图书馆学报,2017,43(4):93~113
共词分析过程中的若干问题研究
Co-word Analysis: Limitations and Solutions
投稿时间:2016-11-28  
DOI:
中文关键词: 共词分析,词源选择,术语规范化,高频词选定,语义关联,多元统计分析
英文关键词: Co-word analysis  Term source selection  Vocabulary standardization  High-frequency words selectionSemantic association  Multi-statistical analysis
基金项目:本文系国家自然科学基金项目“科研团队动态演化规律研究”(编号:71273196)的研究成果之一
作者单位E-mail
李纲 武汉大学信息资源研究中心 湖北 武汉430072  
巴志超 武汉大学信息资源研究中心 湖北 武汉430072 bazhichaoty@126.com,bazhichaoty@126.com 
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中文摘要:
      为完善和优化共词分析方法,本文从共词分析过程中概念术语的词源选择、高频词的选定、术语相关性计算以及多元统计分析四个方面系统地总结共词分析存在的局限性。在词源选择方面,论述不同类型的文献分析单元、术语的规范化以及术语表征差异性对共词分析的影响;在高频词选定方面,分析国内外相关研究在设定高频词阈值、考虑术语语义类型特征以及低频
英文摘要:

    Co-word analysis is a content analysis technique based on the assumption that the subject of a paper can be summarized in a limited number of key terms. If two terms co-occur within one paper,the two research topics they represent are related,and the higher frequency of the co-word means stronger correlation in terms pairs. However,the basic work of co-word analysis is still words and extremely sensitive to the selection of terms,and the quality of co-word analysis depends on a variety of factors,such as the quality of terms and indexes,the high-frequency terms extraction,and the adequacy of statistical methods. Therefore it is necessary to delve into the limitations of co-word analysis at different stages to improve and optimize it.

    The co-word analysis conducted in the present study involved six sequential steps: determination of problem analysis,term source selection,high-frequency terms extraction,relevance calculation of terms,multivariate statistical analysis,and visual presentation of results. This paper focuses on those six key issues to analyze and demonstrate the main problems based on the induction and summarization of the existing relevant research. Results indicate the following conclusions. 1) In the term source selection,solely making use of keywords and index words,which is called “indexer effect” by researchers,is the biggest problem of early co-word analysis. Keywords are uncontrolled words,and problems of homonyms and synonyms will be brought out. Meanwhile,terms expression differences exist among different parts of analysis units,and some errors of co-word analysis will be induced if those differences are ignored. In order to solve the above problems,the textual semantic structure and the phenomenon of different quality with different quantity of terms can be considered. 2) Researchers engaged in co-word analysis have never been out of the pattern that adopts high-frequency term to develop the multivariate statistical analysis. The extraction of high-frequency terms not only makes low-frequency terms more marginalized,but also causes isolation of high-frequency terms that have low correlation with clusters. Considering the discipline and multi-semantic types of terms to distinguish the representation capabilities of subject areas,we can have a comprehensive and in-depth understanding of the research characteristics of this field. 3) Two co-occurrence terms may correlate each other directly or indirectly,but these semantic relationships between co-occurrence terms are not considered at all,which may affect the soundness of the results of co-word analysis ultimately. Thus we summarize the existing calculation methods of semantic correlation and point out the limitations of each method. 4) Finally,in the multivariate statistical analysis,taking the co-word clustering and co-word association analysis method as example,we discuss the problems of their application in the new data environment and put forward the improvement method and suggestion.

    Co-word analysis has been most commonly utilized in mapping or tracing patterns and trends in term association. Although co-word analysis has been improved in many aspects,it still has some limitations. This paper tries to provide theoretical and operational references for co-word analysis researchers and enhancing the reliability and effectiveness of co-word analysis,and it is of great significance to jump out of the traditional theory and practice strategy of co-word analysis. 1 fig. 82 refs.

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