Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. Then, where can we obtain more knowledge of this particular event in addition to its context? This work defines Event Linking, a new natural language understanding task at the event level. Event linking tries to link an event mention, appearing in a news article for example, to the most appropriate Wikipedia page. This page is expected to provide rich knowledge about what the event refers to. To standardize the research of this new problem, we contribute in three-fold. First, this is the first work in the community that formally defines event linking task. Second, we collect a dataset for this new task. In specific, we first gather training set automatically from Wikipedia, then create two evaluation sets: one from the Wikipedia domain as well, reporting the in-domain performance; the other from the real-world news domain, testing the out-of-domain performance. Third, we propose EveLINK, the first-ever Event Linking approach. Overall, event linking is a considerably challenging task requiring more effort from the community. Data and code are available here: https://github.com/CogComp/event-linking.
翻译:撰写文章需要理解它的组成事件。 但是, 提及事件的背景往往缺乏此事件的细节 。 然后, 我们从何处获得关于此事件的详细信息? 此工作定义了事件链接, 这是在活动层面的一个新的自然语言理解任务 。 连接事件试图将事件提及( 例如在新闻文章中出现) 链接到最合适的维基百科页面 。 此页面预计将提供有关事件所涉内容的丰富知识 。 为了对这一新问题的研究进行标准化, 我们贡献了三重。 首先, 这是社区中首次正式定义事件链接任务。 其次, 我们收集了新任务的数据集。 具体地说, 我们首先从维基百科自动收集培训设置的数据集, 然后创建了两套评价组: 一个来自维基百科域域, 报告内部业绩; 另一个来自真实世界新闻域, 测试外部表现 。 第三, 我们建议Evelinnk, 首个事件链接方式。 总体来说, 将事件链接起来是一项相当艰巨的任务, 需要社区做出更多努力 。 数据和代码在这里是 https:// commus/ commling.