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

[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.