Context-Dependent Fine-Grained Entity Type Tagging

TL;DR

This paper introduces the task of context-dependent fine-grained entity type tagging, emphasizing the importance of local context in accurately assigning entity types, and provides new annotated resources and baseline results.

Contribution

It proposes a novel context-dependent tagging task, creates new annotated datasets, and establishes baseline results for this approach.

Findings

New dataset with 12,017 mentions annotated with context-dependent types

Baseline experimental results demonstrating the feasibility of the task

Highlighting the importance of local context in entity type disambiguation

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: 12,017 mentions annotated with their context-dependent fine types, and we provide baseline experimental results on this data.