Document set expansion aims to identify relevant documents from a large collection based on a small set of documents that are on a fine-grained topic. Previous work shows that PU learning is a promising method for this task. However, some serious issues remain unresolved, i.e. typical challenges that PU methods suffer such as unknown class prior and imbalanced data, and the need for transductive experimental settings. In this paper, we propose a novel PU learning framework based on density estimation, called puDE, that can handle the above issues. The advantage of puDE is that it neither constrained to the SCAR assumption and nor require any class prior knowledge. We demonstrate the effectiveness of the proposed method using a series of real-world datasets and conclude that our method is a better alternative for the DSE task.

翻译：文档集扩展旨在根据少量细粒度主题文档，从大规模集合中识别相关文档。先前研究表明，正无标签学习（PU学习）是该任务中颇具前景的方法。然而，仍存在若干未解决的关键问题：PU方法固有的典型挑战（如未知类别先验与数据不平衡），以及需要转导实验设置。本文提出一种基于密度估计的新型PU学习框架puDE，可有效应对上述问题。puDE的优势在于既不受限于SCAR假设，也无需任何类别先验知识。通过一系列真实数据集验证了所提方法的有效性，实验结果表明该方法更适合文档集扩展任务。