面向长尾实体的无监督上下文检索
Unsupervised Context Retrieval for Long-tail Entities
摘要：
监视媒体流中的实体通常依赖于丰富的实体表示，如知识库（KB）中可用的结构化信息。对于长尾实体，由于其在参考知识库中的代表性有限（如果不是完全缺失的话），这种监测是非常具有挑战性的。在本文中，我们解决的问题，检索文本上下文监测长尾实体。我们提出了一种无监督的方法来克服长尾实体的有限表示，利用已建立的实体和它们的上下文作为支持信息。一个专门建立的测试集合上的评估显示了我们的方法的适用性和它的鲁棒性的KB实体。
英文摘要：
Monitoring entities in media streams often relies on rich entity representations, like structured information available in a knowledge base (KB). For long-tail entities, such monitoring is highly challenging, due to their limited, if not entirely missing, representation in the reference KB. In this paper, we address the problem of retrieving textual contexts for monitoring long-tail entities. We propose an unsupervised method to overcome the limited representation of long-tail entities by leveraging established entities and their contexts as support information. Evaluation on a purpose-built test collection shows the suitability of our approach and its robustness for out-of-KB entities.
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。
