Document-based topic coherence measures for news media text
Document-based topic coherence measures for news media text
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DOI：
10.1016/j.eswa.2018.07.063
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发表时间：
2018-12-30
影响因子：
8.5
通讯作者：
Snajder, Jan
中科院分区：
文献类型：
--
作者：
Korencic, Damir;Ristov, Strahil;Snajder, Jan
对新闻文本的自动分析的需求越来越大，主题模型已被证明是这项任务的有用工具。然而，由于主题模型诱导出的主题质量差异很大，因此对它们的自动评价已经投入了大量的研究工作。最近的研究将话题连贯作为衡量话题质量的一个指标。现有的主题连贯性度量是通过考虑主题词的语义相似性来实现的。这使得它们不适合于检测新闻媒体语篇中大量存在的短暂话题与语义无关的话题词之间的连贯。在本文中，我们引入了基于文档的话题连贯的概念，并提出了基于主题文档而不是主题词来估计话题连贯的新的话题连贯度量。我们在两个包含手动标记为基于文档的一致性的主题的数据集上评估了建议的衡量标准，在这两个数据集上，建议的衡量标准的表现优于强基线以及基于单词的连贯衡量标准。我们还展示了基于文档的连贯措施对于从新闻媒体文本中自动发现主题的有效性。(C)2018爱思唯尔有限公司。保留所有权利。
There is a rising need for automated analysis of news text, and topic models have proven to be useful tools for this task. However, as the quality of the topics induced by topic models greatly varies, much research effort has been devoted to their automated evaluation. Recent research has focused on topic coherence as a measure of a topic's quality. Existing topic coherence measures work by considering the semantic similarity of topic words. This makes them unfit to detect the coherence of transient topics with semantically unrelated topic words, which abound in news media texts. In this paper, we introduce the notion of document-based topic coherence and propose novel topic coherence measures that estimate topic coherence based on topic documents rather than topic words. We evaluate the proposed measures on two datasets containing topics manually labeled for document-based coherence, on which the proposed measures outperform a strong baseline as well as word-based coherence measures. We also demonstrate the usefulness of document-based coherence measures for automated topic discovery from news media texts. (C) 2018 Elsevier Ltd. All rights reserved.