Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event
Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event
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DOI：
10.18653/v1/2020.acl-main.478
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Prafulla Kumar Choubey;A. Lee;Ruihong Huang;Lu Wang
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文献类型：
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作者：
Prafulla Kumar Choubey;A. Lee;Ruihong Huang;Lu Wang
理解新闻语篇的语篇结构对于有效地理解新闻事件的发生具有重要意义。为了使计算建模的新闻结构，我们应用现有的理论功能话语结构的新闻文章，围绕主要事件，并创建一个人的注释语料库的802个文件跨越四个领域和三个媒体来源。接下来，我们提出了几个文档级神经网络模型来自动构建新闻内容结构。最后，我们证明，将系统预测的新闻结构产生新的国家的最先进的性能事件共指解决。我们注释的新闻文档是公开的，注释也是公开发布的，供未来研究使用。
Understanding discourse structures of news articles is vital to effectively contextualize the occurrence of a news event. To enable computational modeling of news structures, we apply an existing theory of functional discourse structure for news articles that revolves around the main event and create a human-annotated corpus of 802 documents spanning over four domains and three media sources. Next, we propose several document-level neural-network models to automatically construct news content structures. Finally, we demonstrate that incorporating system predicted news structures yields new state-of-the-art performance for event coreference resolution. The news documents we annotated are openly available and the annotations are publicly released for future research.