收稿日期: 2014-07-24
修回日期: 2014-09-04
网络出版日期: 2014-10-05
基金资助
本文系国家自然科学基金项目“面向专利文献的统计机器翻译语境分析”（项目编号：61303152）和中日国际合作项目“面向科技文献的日汉双向实用型机器翻译合作研究”（项目编号：2014DFA11350）研究成果之一。
Research on Patent Topics Extraction Based on Semantic Role Labeling
Received date: 2014-07-24
Revised date: 2014-09-04
Online published: 2014-10-05
孟令恩 , 李颖 , 何彦青 , 屈鹏 , 王惠临 . 基于语义角色标注的专利主题提取研究[J]. 图书情报工作, 2014 , 58(19) : 19 -24 . DOI: 10.13266/j.issn.0252-3116.2014.19.003
Automatic topics extraction is crucial to mine information of patent literatures. The existing patent text analysis platforms use either manual annotation or templates to find topics. This paper introduces semantic role labeling (SRL) information to help extract patent topics automatically. To improve application effect of SRL to patent literatures, it first introduces the method of automatical sentences implification, then labels semantic roles for the simplified sentences, finally synthesizes semantic information and frequently used words with semantic framework to extract patent topics. The experimental results show that it can s extract beneficial knowledge from patents, and prove the valve of this study.
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