带有元数据的文本的最小监督分类
Minimally Supervised Categorization of Text with Metadata
AI总结：
针对文档分类中元数据未被利用和标签稀缺的问题，提出最小监督框架MetaCat，通过生成模型将文本与元数据嵌入同一语义空间并合成训练样本，有效提升了分类性能。
摘要：
文档分类旨在为每个文档分配一个主题标签，在各种应用中发挥着基础作用。尽管现有研究在传统监督文档分类中取得了成功，但它们较少关注两个现实问题：（1）元数据的存在：在许多领域，文本伴随着各种附加信息，如作者和标签。这些元数据作为令人信服的主题指示器，应该被利用到分类框架中；（2）标签稀缺：在某些情况下，标注训练样本获取成本高昂，需要仅使用少量标注数据来执行分类。认识到这两个挑战，我们提出了MetaCat，一个最小监督的文本与元数据分类框架。具体而言，我们开发了一个生成过程来描述词、文档、标签和元数据之间的关系。在生成模型的指导下，我们将文本和元数据嵌入到同一语义空间中以编码异构信号。然后，基于相同的生成过程，我们合成训练样本以解决标签稀缺的瓶颈。我们在广泛的数据集上进行了全面评估。实验结果证明了MetaCat相对于许多竞争性基线的有效性。
英文摘要：
Document categorization, which aims to assign a topic label to each document, plays a fundamental role in a wide variety of applications. Despite the success of existing studies in conventional supervised document classification, they are less concerned with two real problems: (1) the presence of metadata: in many domains, text is accompanied by various additional information such as authors and tags. Such metadata serve as compelling topic indicators and should be leveraged into the categorization framework; (2) label scarcity: labeled training samples are expensive to obtain in some cases, where categorization needs to be performed using only a small set of annotated data. In recognition of these two challenges, we propose MetaCat, a minimally supervised framework to categorize text with metadata. Specifically, we develop a generative process describing the relationships between words, documents, labels, and metadata. Guided by the generative model, we embed text and metadata into the same semantic space to encode heterogeneous signals. Then, based on the same generative process, we synthesize training samples to address the bottleneck of label scarcity. We conduct a thorough evaluation on a wide range of datasets. Experimental results prove the effectiveness of MetaCat over many competitive baselines.
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。
