Back to the Past: Supporting Interpretations of Forgotten Stories by Time-aware Re-Contextualization
Back to the Past: Supporting Interpretations of Forgotten Stories by Time-aware Re-Contextualization
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DOI：
10.1145/2684822.2685315
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
N. Tran;Andrea Ceroni;Nattiya Kanhabua;C. Niederée
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文献类型：
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作者：
N. Tran;Andrea Ceroni;Nattiya Kanhabua;C. Niederée
充分理解一篇较老的新闻文章需要从文章创作时就了解上下文知识。寻找关于这类背景的信息是一项乏味和耗时的任务，这会分散读者的注意力。在这里，通过维基实现简单的语境化是不够的。检索到的上下文信息必须具有时间意识、简明扼要(而不是完整的维基页面)，并集中于文章主题的连贯性。在本文中，我们提出了一种时间感知的语境化方法，该方法考虑了这些要求，以改善阅读体验。为此，我们提出了(1)不同的查询表达方法来检索上下文候选者和(2)考虑了主题和时间相关性以及相对于原始文本的互补性的排序方法。我们通过使用真实世界数据集和由9,400多个文章/上下文对组成的地面事实的广泛实验来评估我们提出的方法。为此，我们的实验结果表明，我们的方法从纽约时报档案馆检索旧文章的上下文信息具有高精度，并显著优于基线。
Fully understanding an older news article requires context knowledge from the time of article creation. Finding information about such context is a tedious and time-consuming task, which distracts the reader. Simple contextualization via Wikification is not sufficient here. The retrieved context information has to be time-aware, concise (not full Wikipages) and focused on the coherence of the article topic. In this paper, we present an approach for time-aware recontextualization, which takes those requirements into account in order to improve reading experience. For this purpose, we propose (1) different query formulation methods for retrieving contextualization candidates and (2) ranking methods taking into account topical and temporal relevance as well as complementarity with respect to the original text. We evaluate our proposed approaches through extensive experiments using real-world datasets and ground-truth consisting of over 9,400 article/context pairs. To this end, our experimental results show that our approaches retrieve contextualization information for older articles from the New York Times Archive with high precision and outperform baselines significantly.