论文标题
专注于上下文是不错的：改善被掩盖的实体歧义
Focusing on Context is NICE: Improving Overshadowed Entity Disambiguation
论文作者
论文摘要
实体歧义（ED）是将模棱两可的实体映射到结构化知识库中相应条目的任务。先前的研究表明，对现有的ED模型的实体覆盖是一个重大挑战：当提到模棱两可的实体提及时，这些模型更有可能在顶部排名更频繁但更频繁的上下文相关实体。在这里，我们提出了一种迭代方法，它使用实体类型信息来利用上下文并避免过度依赖基于频率的先验。我们的实验表明，NICE可以在被掩盖的实体上取得最佳性能结果，同时仍在频繁的实体上进行竞争性能。
Entity disambiguation (ED) is the task of mapping an ambiguous entity mention to the corresponding entry in a structured knowledge base. Previous research showed that entity overshadowing is a significant challenge for existing ED models: when presented with an ambiguous entity mention, the models are much more likely to rank a more frequent yet less contextually relevant entity at the top. Here, we present NICE, an iterative approach that uses entity type information to leverage context and avoid over-relying on the frequency-based prior. Our experiments show that NICE achieves the best performance results on the overshadowed entities while still performing competitively on the frequent entities.
