论文标题
看到森林和树木：探测和跨文档的核心方案解决了军事跨国纠纷
Seeing the Forest and the Trees: Detection and Cross-Document Coreference Resolution of Militarized Interstate Disputes
论文作者
论文摘要
以前的努力自动化文本中社会和政治事件的发现主要集中在识别单一句子或文件中描述的事件。在文档语料库中，这些自动化系统无法链接事件参考 - 识别多个句子或文档的单数事件。有关事件核心分辨率的计算语言学中的单独文献试图将已知事件互相链接到（跨）文档中。我提供了一个数据集，用于评估方法，以确定文本中的某些政治事件并根据共享事件将相关文本互相链接。数据集，战争的头条新闻，建立在军事化州际争端数据集的基础上，并提供标有核心指示器标记的争议状态和标题对的标题。此外，我引入了一个能够完成这两个任务的模型。鉴于头条新闻的文本和出版日期，多任务卷积神经网络被证明能够识别事件和事件核心。
Previous efforts to automate the detection of social and political events in text have primarily focused on identifying events described within single sentences or documents. Within a corpus of documents, these automated systems are unable to link event references -- recognize singular events across multiple sentences or documents. A separate literature in computational linguistics on event coreference resolution attempts to link known events to one another within (and across) documents. I provide a data set for evaluating methods to identify certain political events in text and to link related texts to one another based on shared events. The data set, Headlines of War, is built on the Militarized Interstate Disputes data set and offers headlines classified by dispute status and headline pairs labeled with coreference indicators. Additionally, I introduce a model capable of accomplishing both tasks. The multi-task convolutional neural network is shown to be capable of recognizing events and event coreferences given the headlines' texts and publication dates.
