基于机器学习的论文作者名消歧方法研究
DOI:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

TP391.1

基金项目:


RResearch on author name disambiguation method based on machine learning
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    本文提出了一种基于规则匹配和机器学习的论文作者名自动化消歧方法:首先基于人工构建的人名匹配规则确定候选作者,对于存在多个候选人的情况,基于论文的属性信息(例如合作者、标题、摘要、关键词和出版物名称等)提取特征,然后选取合适的机器学习算法进行消歧.实验效果表明K近邻和Softmax分类器较适合于论文作者名消歧任务;此外,将作者信息与论文的其他信息分开提取特征能够有效提高作者名消歧的准确性.

    Abstract:

    This paper proposes an automatic article author name disambiguation method based on rule matching and machine learning. For each article, the candidate authors are determined based on artificial constructed name matching rules firstly. For the cases of multiple candidates, features are extracted from the attribute information of the article, such as collaborators, title, abstract, key words and publication name, and then selected machine learning models are applied to author name disambiguating. The experimental results show that the K-nearest neighbor and Softmax classifier are more suitable for the author name disambiguation task than other models. In addition, extracting features of the authors information from other information separately can effectively improve the accuracy of the author name disambiguation.

    参考文献
    相似文献
    引证文献
引用本文

引用本文格式: 邓可君,华凯,邓昌明,姜宁,袁玲,彭一明,张治坤. 基于机器学习的论文作者名消歧方法研究[J]. 四川大学学报: 自然科学版, 2019, 56: 241.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2018-10-17
  • 最后修改日期:2018-12-10
  • 录用日期:2018-12-10
  • 在线发布日期: 2019-04-01
  • 出版日期: