Please wait a minute...
New Technology of Library and Information Service  2011, Vol. 27 Issue (12): 58-63    DOI: 10.11925/infotech.1003-3513.2011.12.09
Current Issue | Archive | Adv Search |
Research of Patent Automatic Classification Based on RBFNN
Ma Fang
Library of Yantai Project Occupation and Technology College, Yantai 264006, China
Download: PDF(733 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  In order to reduce the poor consistency and the errors in manual patent classification, this article introduces text classification technology into patent auto-classification system. It uses the radial basis function neural network algorithm to realize the automatic classification of patent text, and analyses the test samples.The experiment results show that this new system has a better classification results,and the average F1 value is higher than 70%.
Key wordsPatent automatic classification      Text categorization      Radial basis function neural network     
Received: 13 September 2011      Published: 02 February 2012
: 

G250

 

Cite this article:

Ma Fang. Research of Patent Automatic Classification Based on RBFNN. New Technology of Library and Information Service, 2011, 27(12): 58-63.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2011.12.09     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2011/V27/I12/58

[1] Camus C,Brancaleon R. Intellectual Assets Management:From Patents to Knowledge[J]. World Patent Information,2003,25(2):155-159.

[2] 暴海龙,李金林.专利检索中的IPC和主题词识别方法研究[J]. 北京理工大学学报:社会科学版, 2003, 5(5): 74-76.

[3] Iwayama M,Fujii A,Kando N, et al. Overview of Patent Retrieval-Task at NTCIR-3[C]. In: Proceedings of the ACL-2003 Workshop on Patent Corpus Processing,Sapporo,Japan.2003:24-32.

[4] Li Y,Bontcheva K,Cunningham H. SVM Based Learning System for F-term Patent Classification[C].In: Proceedings of the 6th NTCIR Workshop Meeting,Tokyo, Japan.2007:15-18.

[5] Li Y,Bontcheva K,Cunningham H.Cost Sensitive Evaluation Measures for F-term Patent Classification[C]. In: Proceedings of the 1st International Workshop on Evaluating Information Access(EVIA),Tokyo, Japan.2007:44-53.

[6] Doi H,Seki Y,Aono M.A Patent Retrieval Method Using a Hierarchy of Clusters at TUT[C]. In: Proceedings of the 5th NTCIR Workshop Meeting,Tokyo,Japan.2005:403-406.

[7] Mase H,Iwayama M.NTCIR-6 Patent Retrieval Experiments at Hitachi[C].In: Proceedings of the 6th NTCIR Workshop Meeting,Tokyo, Japan.2007:403-406.

[8] 李生珍,王建新,齐建东,等.基于BP神经网络的专利自动分类方法[J].计算机工程与设计,2010,31(23):5075-5078.

[9] 季铎,蔡云雷,蔡东风,等.基于共享最邻近的专利自动分类技术研究[J].沈阳航空工业学院学报,2010,8(4):41-45.

[10] 孔旗. 基于并行机器学习的大规模专利分类 [D].上海:上海交通大学,2011.

[11] Haykin S S.Neural Networks:A Comprehensive Foundation[M].北京:清华大学出版社,2001:40-42.

[12] 张华平.汉语词法分析系统ICTCLAS[EB/OL].[2010-12-19].http://ictclas.org/index.html.

[13] 孙建军,成颖.信息检索技术[M].北京:科学出版社,2004:88-89.
[1] Zhanglu Tan,Zhaogang Wang,Han Hu. Study on a Method of Feature Classification Selection Based on χ2 Statistics[J]. 数据分析与知识发现, 2019, 3(2): 72-78.
[2] Xiangdong Li,Fan Gao,Youhai Li. Categorizing Documents Automatically within Common Semantic Space[J]. 数据分析与知识发现, 2018, 2(9): 66-73.
[3] Guoming Feng,Xiaodong Zhang,Suhui Liu. Classifying Chinese Texts with CapsNet[J]. 数据分析与知识发现, 2018, 2(12): 68-76.
[4] Xu Dongdong, Wu Shaobo. An Improved TF-IDF Feature Selection Based on Categorical Description[J]. 现代图书情报技术, 2015, 31(3): 39-48.
[5] Tan Xueqing, Zhou Tong, Luo Lin. A Text Classification Algorithm Based on the Average Category Similarity[J]. 现代图书情报技术, 2014, 30(9): 66-73.
[6] Li Xiangdong, He Haihong, Cao Huan, Huang Li. An Algorithm of Digital Resources Text Categorization for Training Sets Skewed Distribution[J]. 现代图书情报技术, 2014, 30(7): 24-33.
[7] Li Xiangdong, Liao Xiangpeng, Huang Li. Research and Implementation of Bibliographic Information Classification System in LDA Model[J]. 现代图书情报技术, 2014, 30(5): 18-25.
[8] Lu Yonghe, Liang Minghui. Improvement of Text Feature Extraction with Genetic Algorithm[J]. 现代图书情报技术, 2014, 30(4): 48-57.
[9] Wang Hao, Ye Peng, Deng Sanhong. The Application of Machine-Learning in the Research on Automatic Categorization of Chinese Periodical Articles[J]. 现代图书情报技术, 2014, 30(3): 80-87.
[10] Hu Bing, Zhang Jianli. Research on Chinese Patent Automatic Classification Method Based on Statistical Distribution[J]. 现代图书情报技术, 2013, 29(7/8): 101-106.
[11] Lu Yonghe, Li Yanfeng. A Feature Selection Based on Consideration of Multiple Factors[J]. 现代图书情报技术, 2013, (5): 34-39.
[12] Qu Peng, Wang Huilin. Fundamental Research Questions in Patent Text Categorization[J]. 现代图书情报技术, 2013, 29(3): 38-44.
[13] Xu Kun, Cao Jindan, Bi Qiang. A Study and Application on Medical Text Categorization Based on FCA[J]. 现代图书情报技术, 2012, 28(3): 23-26.
[14] Lu Yonghe, He Xinyu. An Application of Sharpen Gaussian Template in a Text Feature Weight Adjustment Methodology[J]. 现代图书情报技术, 2012, (12): 39-44.
[15] Lu Yonghe, Cao Lichao. Text Feature Selection Method Based on Particle Swarm Optimization[J]. 现代图书情报技术, 2011, 27(7/8): 76-81.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn