中圖分類號： TP181
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2021.12.009
引用格式： 王慶才，劉貴全. 基于混合表征學習的專利分類方法研究[J].信息技術與網(wǎng)絡安全,，2021,，40(12)：51-59.

Hybrid representation based framework for patent classification

Abstract： Patent classification is a fundamental task in patent analysis, and automatic patent classification methods based on deep learning can perform effectively. Most existing studies utilize natural language processing methods to classify patent documents based on the textual contents of individual patents(e.g.,，abstracts and titles), while the macro-relationships between patents and their labels(e.g.，citations between patents and co-occurrence between labels) are largely ignored. To alleviate the problem of single patent information in patent classification, this paper constructed three graphs to represent the macro-relationships between patents and labels, and then proposed a hybrid representation based framework for patent classification to enhance the automated classification of patents. Extensive experiments conducted on real patent datasets show that the proposed classification method achieves the best performance in several evaluation metrics.

Key words : patent classification,；multi-label classification,；Graph Neural Network；deep learning