Parameter tuning Naïve Bayes for automatic patent classification. (June 2020)

• Record Type: Journal Article
• Authors: Cassidy, Caitlin
• Is Part Of: World patent information. Volume 61(2020)
• Publication Date: 2020-06
• Subjects: Machine learning -- Naïve bayes -- Text classification; Patent literature -- Periodicals; Information storage and retrieval systems -- Patent documentation -- Periodicals; 608.05
• DOI: 10.1016/j.wpi.2020.101968
• Languages: English
• ISSNs: 0172-2190

Abstract

Abstract: I present an analysis of feature selection for automatic patent categorization. For a corpus of 7, 309 patent applications from the World Patent Information (WPI) Test Collection (Lupu, 2019), I assign International Patent Classification (IPC) section codes using a modified Naïve Bayes classifier. I compare precision, recall, and f-measure for a variety of meta-parameter settings including data smoothing and acceptance threshold. Finally, I apply the optimized model to IPC class and group codes and compare the results of patent categorization to academic literature.