Representing topics labels for exploring digital libraries

Nikolaos Aletras, Timothy Baldwin, Jey Han Lau, Mark Stevenson
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引用次数: 29

Abstract

Topic models have been shown to be a useful way of representing the content of large document collections, for example via visualisation interfaces (topic browsers). These systems enable users to explore collections by way of latent topics. A standard way to represent a topic is using a set of keywords, i.e. the top-n words with highest marginal probability within the topic. However, alternative topic representations have been proposed, including textual and image labels. In this paper, we compare different topic representations, i.e. sets of topic words, textual phrases and images, in a document retrieval task. We asked participants to retrieve relevant documents based on pre-defined queries within a fixed time limit, presenting topics in one of the following modalities: (1) sets of keywords, (2) textual labels, and (3) image labels. Our results show that textual labels are easier for users to interpret than keywords and image labels. Moreover, the precision of retrieved documents for textual and image labels is comparable to the precision achieved by representing topics using sets of keywords, demonstrating that labelling methods are an effective alternative topic representation.
表示主题标签,用于探索数字图书馆
主题模型已被证明是表示大型文档集合内容的一种有用方法,例如通过可视化界面(主题浏览器)。这些系统使用户能够通过潜在主题的方式来探索收藏。表示主题的标准方法是使用一组关键字,即该主题中边际概率最高的前n个单词。然而,已经提出了替代主题表示,包括文本和图像标签。在本文中,我们比较了一个文档检索任务中不同的主题表示,即主题词集、文本短语集和图像集。我们要求参与者在固定的时间内根据预定义的查询检索相关文档,并以以下方式之一呈现主题:(1)关键字集,(2)文本标签,(3)图像标签。我们的研究结果表明,文本标签比关键词和图像标签更容易被用户理解。此外,文本和图像标签检索文档的精度与使用关键字集表示主题所获得的精度相当,这表明标记方法是一种有效的替代主题表示方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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