Patent document categorization based on semantic structural information

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The number of patent documents is currently rising rapidly worldwide, creating the need for an automatic categorization system to replace time-consuming and labor-intensive manual categorization. Because accurate patent classification is crucial to search for relevant existing patents in a certain field, patent categorization is a very important and useful field. As patent documents are structural documents with their own characteristics distinguished from general documents, these unique traits should be considered in the patent categorization process. In this paper, we categorize Japanese patent documents automatically, focusing on their characteristics: patents are structured by claims, purposes, effects, embodiments of the invention, and so on. We propose a patent document categorization method that uses the k-NN (k-Nearest Neighbour) approach. In order to retrieve similar documents from a training document set, some specific components to denote the socalled semantic elements, such as claim, purpose, and application field, are compared instead of the whole texts. Because those specific components are identified by various user-defined tags, first all of the components are clustered into several semantic elements. Such semantically clustered structural components are the basic features of patent categorization. We can achieve a 74% improvement of categorization performance over a baseline system that does not use the structural information of the patent. (c) 2007 Published by Elsevier Ltd.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2007-09
Language
English
Article Type
Article
Description

Received 1 September 2005; accepted 29 May 2006; Available online 26 March 2007

Citation

INFORMATION PROCESSING & MANAGEMENT, v.43, no.5, pp.1200 - 1215

ISSN
0306-4573
DOI
10.1016/j.ipm.2007.02.002
URI
http://hdl.handle.net/10203/3537
Appears in Collection
CS-Journal Papers(저널논문)
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