Parameter Tuning Naïve Bayes for Automatic Patent Classification

Caitlin Cassidy of Search Technology, Inc. presents 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), Caitlin assigned International Patent Classification (IPC) section codes using a modified Naïve Bayes classifier. She compare precision, recall, and f-measure for a variety of meta-parameter settings including data smoothing and acceptance threshold. Finally, Ms. Cassidy applied the optimized model to IPC class and group codes and compare the results of patent categorization to academic literature.