中文领域专业术语层次关系构建研究  

Study on Construction of Domain Terminology Taxonomic Relation

作　　者：朱惠[1,2] 杨建林[1,2] 王昊[1,2] 

机构地区：[1]南京大学信息管理学院,南京210023 [2]江苏省数据工程与知识服务重点实验室,南京210023

出　　处：《现代图书情报技术》2016年第1期73-80,共8页New Technology of Library and Information Service

基　　金：江苏省自然科学基金项目"面向专利预警的中文本体学习研究"(项目编号:BK20130587);中央高校基本科研业务费专项资金项目"我国图书情报学科知识结构及演化动态研究"(项目编号:20620140645)的研究成果之一

摘　　要：【目的】对如何从中文非结构化文本获取术语的层次关系进行探讨。【方法】从CNKI获取数字图书馆学科领域文献,通过术语抽取、术语向量空间模型构建、BIRCH算法聚类和聚类标签确定构建术语的语义层次结构。【结果】构建数字图书馆领域术语的层次结构,并对构建结果进行验证,聚类正确率达到80.88%,类标签抽取正确率达到89.71%。【局限】对构建效果的验证是通过随机抽样进行的,且仅与一种其他构建方法进行实证比较。【结论】应用BIRCH算法聚类构建术语层次结构,该方法与K-means聚类方法相比具有明显优势,具备较高的执行效率和聚类有效性。[Objective] Discuss how to obtain the terminology taxonomic relation from Chinese domain unstructured text. [Methods] Based on Digital Library domain text from CNKI, construct terminology hierarchy by terminology extraction, terminology Vector Space Model construction, BIRCH clustering and cluster tag distribution. [Results] Obtain the terminology taxonomic relation of Digital Library domain, and evaluate the effectiveness. The accuracy of clustering reaches up to 80.88%, and the accuracy of cluster tag extraction reaches up to 89.71%. [Limitations] Evaluate the effectiveness by random sampling, and in comparison with one method only. [Conclusions] Making use of BIRCH algorithm to construct terminology taxonomic relation, this algorithm has obvious advantage compared with K-means clustering method, and has higher execution and clustering effectiveness.

关 键 词：术语 层次关系 本体 本体学习 聚类 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]