Context-Dependent Fine-Grained Entity Type Tagging
Venue
arXiv.org (2014)
Publication Year
2014
Authors
Dan Gillick, Nevena Lazic, Kuzman Ganchev, Jesse Kirchner, David Huynh
BibTeX
Abstract
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: 11,304 mentions annotated with their
context-dependent fine types, and we provide baseline experimental results on this
data.
