Entity type tagging is the task of assigning category labels to each mention
of an entity in a document. While standard systems focus on a small set of
types, recent work (Ling and Weld, 2012) suggests that using a large
fine-grained label set can lead to dramatic improvements in downstream tasks.
In the absence of labeled training data, existing fine-grained tagging systems
obtain examples automatically, using resolved entities and their types
extracted from a knowledge base. However, since the appropriate type often
depends on context (e.g. Washington could be tagged either as city or
government), this procedure can result in spurious labels, leading to poorer
generalization. We propose the task of context-dependent fine type tagging,
where the set of acceptable labels for a mention is restricted to only those
deducible from the local context (e.g. sentence or document). We introduce new
resources for this task: 12,017 mentions annotated with their context-dependent
fine types, and we provide baseline experimental results on this data.

这篇论文提出了一种上下文相关细粒度实体类型标注的方法，通过基于知识库提取实体和其类型，限制在本地上下文中可以推导的标签集，实验结果表明可以有效改善标注的精度。