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通过检索增强多阶段培训进行弱监督的科学文档分类.

Ran Xu1, Yue Yu2, Joyce Ho1

  • 1Emory University, Atlanta, GA, USA.

International ACM SIGIR Conference on Research and Development in Information Retrieval. Annual International ACMSIGIR Conference on Research & Development in Information Retrieval
|February 14, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了WanDeR,这是一种新的科学文档分类方法,仅使用标签名称. WanDeR通过利用密集检索和标签扩展显著提高了分类准确性,克服了数据标签成本.

关键词:
检索检索可以检索.科学文件分类科学文件分类监督的弱点 监督的弱点

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科学领域:

  • 计算机科学 计算机科学
  • 信息科学 信息科学 信息科学
  • 人工智能的人工智能

背景情况:

  • 科学文档分类是必不可少的,但由于人类标记数据的高成本而受到阻碍.
  • 当标签名称包含文档库中缺少的域名特定术语时,现有方法会遇到困难.

研究的目的:

  • 开发一种有效的科学文档分类方法,只使用标签名称.
  • 为了应对与含义丰富但潜在稀疏的标签名称匹配文档的挑战.

主要方法:

  • 提出了WanDeR,一种采用密集检索用于嵌入空间中的语义匹配的方法.
  • 整合了标签名称扩展模块,以丰富标签表示.
  • 利用自我训练步骤来完善分类预测.

主要成果:

  • 与现有基线相比,WanDeR表现优越.
  • 在三个实验数据集中,分类准确度提高了11.9%.
  • 该方法有效地捕捉了标签语义,以改善文档匹配.

结论:

  • WanDeR为科学文档分类提供了具有成本效益和准确的解决方案.
  • 利用密集的检索和标签扩展可以提高模型理解标签语义的能力.
  • 拟议的方法显示了科学信息管理中的实际应用的巨大潜力.