联合事件检测和实体解析：一个良性循环
Joint Event Detection and Entity Resolution: a Virtuous Cycle
摘要：
聚类web文档有许多应用，例如将新闻文章聚合成有意义的事件，检测Web上的趋势和热点话题，保持搜索结果的多样性等。同时，命名实体的重要性，特别是识别它们和解决相关共指解决问题的能力，被广泛认为是挖掘时的关键使能因素，聚合和比较网络上的内容。而不是单独考虑这两个问题，我们在本文中提出了一种方法，联合处理的问题，聚类新闻文章到事件和跨文档共引用决议的命名实体。命名实体在相同聚类中的同现被用作决定两个引用是否应该合并到一个实体中的附加信号。这些细化的实体可以反过来作为增强的功能来重新聚类文档，然后再次细化，进入一个良性循环，同时提高两个任务的性能。我们实现了一个原型系统和报告结果使用TDT5收集的新闻文章，展示了我们的方法的潜力。
英文摘要：
Clustering web documents has numerous applications, such as aggregating news articles into meaningful events, detecting trends and hot topics on the Web, preserving diversity in search results, etc. At the same time, the importance of named entities and, in particular, the ability to recognize them and to solve the associated co-reference resolution problem are widely recognized as key enabling factors when mining, aggregating and comparing content on the Web. Instead of considering these two problems separately, we propose in this paper a method that tackles jointly the problem of clustering news articles into events and cross-document co-reference resolution of named entities. The co-occurrence of named entities in the same clusters is used as an additional signal to decide whether two referents should be merged into one entity. These refined entities can in turn be used as enhanced features to re-cluster the documents and then be refined again, entering into a virtuous cycle that improves simultaneously the performances of both tasks. We implemented a prototype system and report results using the TDT5 collection of news articles, demonstrating the potential of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
