融合论文关键词知识的专利术语抽取方法  

Patent Term Extraction by Integrating Keyword Knowledge From Paper

作　　者：俞琰 陈磊[1] 姜金德 赵乃瑄 Yu Yan;Chen Lei;Jiang Jinde;Zhao Naixuan(Information Service Department,Nanjing Tech University,Nanjing 210009;Computer Science Department,Southeast University Chengxian College,Nanjing 211816;School of Business,Nanjing Xiaozhuang University,Nanjing 211171)

机构地区：[1]南京工业大学信息服务部,南京210009 [2]东南大学成贤学院计算机工程系,南京211816 [3]南京晓庄学院商学院,南京211171

出　　处：《图书情报工作》2020年第14期104-111,共8页Library and Information Service

基　　金：国家社会科学基金一般规划项目"大数据时代支持创新设计的多维度多层次专利文本挖掘研究"(项目编号:17BTQ059)研究成果之一。

摘　　要：[目的/意义]提出利用丰富的论文关键词知识获取专利文本之外的有效特征,以弥补因专利文本集自身信息不足而制约专利术语抽取效果这一缺陷,提高专利术语抽取准确率。[方法/过程]根据相关论文的关键词知识,分别提出领域相关度和首尾度两个特征,以衡量候选术语成为术语的可能性,并将这些特征融入到专利术语抽取的传统方法之中。[结果/结论]实验结果表明,利用论文关键词得到的候选术语领域相关度和首尾度信息,可使结合论文关键词知识的方法比传统的术语抽取方法的准确率有了明显的提升。[Purpose/significance]In order to make up for the shortcomings of the patent text collection itself to limit the effect of patent term extraction,this paper proposes to use the rich keyword knowledge to obtain effective features outside the patent text to improve the patent term extraction effect.[Method/process]According to the keyword knowledge of related papers,two kinds of characteristic,degree of domain relevance and degree of head&tail are proposed to measure the possibility that candidate terms become terminology,and these characteristics are incorporated into the traditional method of patent term extraction.[Result/conclusion]The experimental results show that the degree of domain relevance and the degree of head&tail of the candidate terms obtained by using the keyword information of the papers make the method of combining the keyword knowledge of the papers significantly higher than the accuracy of the traditional term extraction method.

关 键 词：专利术语抽取 论文 关键词 

分 类 号：G202[文化科学—传播学]