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 Naive 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.

中文翻译：

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用于自动专利分类的参数调整朴素贝叶斯

摘要 我对自动专利分类的特征选择进行了分析。对于来自世界专利信息 (WPI) 测试集 (Lupu, 2019) 的 7,309 项专利申请的语料库，我使用改进的朴素贝叶斯分类器分配了国际专利分类 (IPC) 部分代码。我比较了各种元参数设置的精度、召回率和 f 度量，包括数据平滑和接受阈值。最后，我将优化后的模型应用于 IPC 类和组代码，并将专利分类结果与学术文献进行比较。