Patent classification by fine-tuning BERT language model

Jieh-Sheng Lee*, Jieh Hsiang

*此作品的通信作者

研究成果: Article同行評審

101 引文 斯高帕斯(Scopus)

摘要

In this work we focus on fine-tuning a pre-trained BERT model and applying it to patent classification. When applied to large datasets of over two million patents, our approach outperforms the state of the art by an approach using CNN with word embeddings. Besides, we focus on patent claims without other parts in patent documents. Our contributions include: (1) a new state-of-the-art result based on pre-trained BERT model and fine-tuning for patent classification, (2) a large dataset USPTO-3M at the CPC subclass level with SQL statements that can be used by future researchers, (3) showing that patent claims alone are sufficient to achieve state-of-the-art results for classification task, in contrast to conventional wisdom.

原文English
文章編號101965
頁(從 - 到)1-4
頁數4
期刊World Patent Information
61
DOIs
出版狀態Published - 6月 2020

指紋

深入研究「Patent classification by fine-tuning BERT language model」主題。共同形成了獨特的指紋。

引用此