Incorporating Prior Knowledge into Task Decomposition for Large-Scale Patent Classification

Authors:
Chao Ma;Bao-Liang Lu;Masao Utiyama
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University,;Department of Computer Science and Engineering, Shanghai Jiao Tong University, and MOE-Microsoft Key Lab for Intelligent Computing and Intelligent System, Shanghai Jiao Tong University, Shanghai, ...;National Institute of Information and Communications Technology (NICT), Kyoto, Japan 619-0288
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

With the adoption of min-max-modular support vector machines (SVMs) to solve large-scale patent classification problems, a novel, simple method for incorporating prior knowledge into task decomposition is proposed and investigated. Two kinds of prior knowledge described in patent texts are considered: time information, and hierarchical structure information. Through experiments using the NTCIR-5 Japanese patent database, patents are found to have time-varying features that considerably affect classification. The experimental results demonstrate that applying min-max modular SVMs with the proposed method gives performance superior to that of conventional SVMs in terms of training time, generalization accuracy, and scalability.