MotifClass：利用高阶元数据信息的弱监督文本分类
MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information
AI总结：
研究弱监督文本分类问题，提出MotifClass框架，通过异构信息网络和motif捕捉高阶元数据组合，生成伪标签训练分类器，性能优于现有方法。
摘要：
我们研究弱监督文本分类问题，旨在仅使用类别表面名称将文本文档分类到一组预定义类别中，且不提供任何标注的训练文档。大多数现有分类器利用每个文档中的文本信息。然而，在许多领域中，文档伴随着各种类型的元数据（例如研究论文的作者、会议和年份）。这些元数据及其组合可作为除文本内容之外的强类别指示。在本文中，我们探索使用元数据辅助弱监督文本分类的潜力。具体而言，我们通过异构信息网络对文档与元数据间的关系进行建模。为有效捕捉网络中的高阶结构，我们使用motif描述元数据组合。我们提出了一个名为MotifClass的新颖框架，该框架（1）选择类别指示性的motif实例，（2）基于类别名称和指示性motif实例检索并生成伪标记训练样本，以及（3）使用伪训练数据训练文本分类器。在真实世界数据集上的大量实验证明了MotifClass相比现有弱监督文本分类方法的优越性能。进一步分析表明，在我们的框架中考虑高阶元数据信息是有益的。
英文摘要：
We study the problem of weakly supervised text classification, which aims to classify text documents into a set of pre-defined categories with category surface names only and without any annotated training document provided. Most existing classifiers leverage textual information in each document. However, in many domains, documents are accompanied by various types of metadata (e.g., authors, venue, and year of a research paper). These metadata and their combinations may serve as strong category indicators in addition to textual contents. In this paper, we explore the potential of using metadata to help weakly supervised text classification. To be specific, we model the relationships between documents and metadata via a heterogeneous information network. To effectively capture higher-order structures in the network, we use motifs to describe metadata combinations. We propose a novel framework, named MotifClass, which (1) selects category-indicative motif instances, (2) retrieves and generates pseudo-labeled training samples based on category names and indicative motif instances, and (3) trains a text classifier using the pseudo training data. Extensive experiments on real-world datasets demonstrate the superior performance of MotifClass to existing weakly supervised text classification approaches. Further analysis shows the benefit of considering higher-order metadata information in our framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
