图书情报工作

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2013 , Vol. 57 >Issue 01: 130 - 135

DOI: https://doi.org/10.7536/j.issn.0252-3116.2013.01.023

知识组织

面向信息分析的专利术语抽取研究

  • 屈鹏 ,
  • 王惠临
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  • 中国科学技术信息研究所
屈鹏,中国科学技术信息研究所博士后,博士,E-mail:pqu@pku.edu.cn;王惠临,中国科学技术信息研究所研究员,博士。

收稿日期: 2012-08-27

  修回日期: 2012-11-23

  网络出版日期: 2013-01-05

基金资助

本文系第51批中国博士后科学基金面上资助一等资助项目"科技文本信息资源中术语抽取与基于术语的分类与聚类"(项目编号:2012M510040)和中国科学技术信息研究所学科建设项目"自然语言处理"(项目编号:XK2012-6)研究成果之一。

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Patent Term Extraction for Information Analysis

  • Qu Peng ,
  • Wang Huilin
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  • Institute for Scientific and Technical Information of China, Beijing 100038

Received date: 2012-08-27

  Revised date: 2012-11-23

  Online published: 2013-01-05

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摘要

从信息分析的实际需求出发,对与电动汽车相关的5 405条专利数据进行术语抽取、生僻术语识别和字段比较研究。结果显示关键短语抽取的方法可行,互信息抽取的术语所在文档的平均文档长度更接近集合的平均文档长度;摘要和First Claim字段的术语存在一定差别,但对分类或聚类同等重要;生僻术语识别算法能够发现生僻词和高频词的对应关系。研究结论可以为专利文本挖掘和专利信息分析提供结果和方法,并为信息分析工作提供所需的参考术语。

关键词： 术语抽取; 文本挖掘; 专利; 信息分析

本文引用格式

屈鹏 , 王惠临 . 面向信息分析的专利术语抽取研究[J]. 图书情报工作, 2013 , 57(01) : 130 -135 . DOI: 10.7536/j.issn.0252-3116.2013.01.023

Abstract

Based on the actual need of information analysis, this paper studies 5405 pieces of patent data about electric vehicle by term extraction, rare term recognition and field comparison. The result reveals that key-phrase extraction is feasible; that average length of the documents containing terms ranked by mutual information is closer to one of collection; that terms in abstract and those in first claim are different to a certain extent, but of equal importance to text categorization/clustering; the algorithm of rare term recognition can find the corresponding relationship between rare words and high frequency words. This paper provides results and methods for patent text mining and patent information analysis, and provides reference for information analysis.

Key words： term extraction; text mining; patent; information analysis

参考文献

[1] Tseng Yuenhsien, Lin Chijen, Lin Yui. Text mining techniques for patent analysis[J]. Information Processing and Management, 2007, 43(5): 1216-1247.



[2] 曲军伟, 乔晓东, 桂婕. 自组织映射在专利文本聚类中的应用研究[J]. 数字图书馆论坛, 2010(9): 13-19.



[3] 刘玉琴, 朱东华, 吕琳. 基于文本挖掘技术的产品技术成熟度预测[J]. 计算机集成制造系统, 2008, 14(3): 506-510.



[4] Yoon B, Park Y. A text-mining-based patent network: Analytical tool for high-technology trend[J]. Journal of High Technology Management Research, 2004, 15(1): 37-50.



[5] 韩红旗, 朱东华, 汪雪峰. 专利技术术语的抽取方法[J]. 情报学报, 2011, 30(12): 1280-1285.



[6] 韩红旗, 安小米, 朱东华, 等. 专利技术术语共现的战略图分析方法[J]. 计算机应用研究, 2011, 28(2): 576-579.



[7] Witten I H, Paynter G W, Frank E, et al. KEA: Practical automatic keyphrase extraction[C]//Proceedings of the 4th ACM Conference on Digital Libraries (DL’99). New York: ACM Press, 1999: 254-255.



[8] Kelleher D, Luz S. Automatic hypertext keyphrase detection[C]// Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI’05). San Francisco: Morgan Kaufmann Publishers Inc., 2005: 1608-1609.



[9] El-Beltagy S R, Rafea A. KP-Miner: A keyphrase extraction system for English and Arabic documents[J]. Information Systems, 2009, 34(1): 132-144.



[10] Mihalcea R, Tarau P. TextRank: Bringing order in text[C/OL]//Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP’04).[2011-10-18]. http://www.aclweb.org/anthology-new/W/W04/ W04-3252.pdf.



[11] Liu Zhiyuan, Huang Wenyi, Zheng Yabin, et al. Automatic keyphrase extraction via topic decomposition[C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP’10). Stroudsburg: Association for Computational Linguistics, 2010: 366-376.



[12] Huang Chong, Tian Yonghong, Zhou Zhi, et al. Keyphrase extraction using semantic networks structure analysis[C]//Proceedings of the 6th IEEE International Conference on Data Mining (ICDM’06). Washington D.C.: IEEE Computer Society, 2006: 275-284

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