Hierarchical multi-instance multi-label learning for Chinese patent text classification

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-01-03 DOI:10.1080/09540091.2023.2295818

Yunduo Liu, Fang Xu, Yushan Zhao, Zichen Ma, Tengke Wang, Shunxiang Zhang, Yuhao Tian

引用次数: 0

Abstract

To further enhance the accuracy of the Chinese patent classification, this paper proposes a model, based on the patent structure and takes the patent claim as subjects, with multi-instance multi-la...

查看原文本刊更多论文

用于中文专利文本分类的分层多实例多标签学习

为进一步提高中国专利分类的准确性，本文提出了一种以专利结构为基础、以专利权利要求书为主体、多实例多类型的中国专利分类模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.