使用词嵌入的自动专利分类

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI:10.1109/ICMLA.2017.0-127

Mattyws F. Grawe, C. A. Martins, Andreia Gentil Bonfante

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引用次数: 42

摘要

专利分类是为专利分配特殊代码的任务，分配的代码用于将具有相似主题的专利分组到同一类别中。本文提出了一种基于词嵌入和长短期记忆网络的专利分类方法，将专利分类精确到子组IPC级别。实验结果表明，我们的分类方法在子组水平上达到了63%的准确率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Automated Patent Classification Using Word Embedding

Patent classification is the task of assign a special code to a patent, where the assigned code is used to group patents with similar subject into a same category. This paper presents a patent categorization method based on word embedding and long short term memory network to classify patents down to the subgroup IPC level. The experimental results indicate that our classification method achieve 63\% accuracy at the subgroup level.

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2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)

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