Paper Info
Title
Adapting Support Vector Machines for F-term-based Classification of Patents
Abstract
Support Vector Machines (SVM) have obtained state-of-the-art results on many applications including document classification. However, previous works on applying SVMs to the F-term patent classification task did not obtain as good results as other learning algorithms such as kNN. This is due to the fact that F-term patent classification is different from conventional document classification in several aspects, mainly because it is a multiclass, multilabel classification problem with semi-structured documents and multi-faceted hierarchical categories. This article describes our SVM-based system and several techniques we developed successfully to adapt SVM for the specific features of the F-term patent classification task. We evaluate the techniques using the NTCIR-6 F-term classification terms assigned to Japanese patents. Moreover, our system participated in the NTCIR-6 patent classification evaluation and obtained the best results according to two of the three metrics used for task performance evaluation. Following the NTCIR-6 participation, we developed two new techniques, which achieved even better scores using all three NTCIR-6 metrics, effectively outperforming all participating systems. This article presents this new work and the experimental results that demonstrate the benefits of the latest approach.
Year
DOI
Venue
2008
10.1145/1362782.1362786
ACM Trans. Asian Lang. Inf. Process.
Keywords
Field
DocType
japanese patent,ntcir-6 participation,ntcir-6 patent classification evaluation,ntcir-6 f-term classification term,vector machines,f-term patent classification,document classification,patent processing,adapting support,support vector machines,f-term-based classification,f-term classification,multilabel classification problem,f-term patent classification task,conventional document classification,ntcir-6 metrics,support vector machine
Structured support vector machine,Data mining,One-class classification,Computer science,Web query classification,Artificial intelligence,Multiclass classification,Document classification,Pattern recognition,Patent classification,Relevance vector machine,Linear classifier,Machine learning
Journal
Volume
Issue
Citations 
7
2
4
PageRank 
References 
Authors
0.45
15
2
Name
Order
Citations
PageRank
Yaoyong Li139326.55
Kalina Bontcheva22538211.33