Improving Fine-grained Entity Typing with Entity Linking

TL;DR

This paper introduces a deep neural model that leverages entity linking to improve fine-grained entity typing accuracy, achieving significant performance gains on standard datasets.

Contribution

It presents a novel approach combining entity linking with deep learning for enhanced fine-grained entity classification.

Findings

Over 5% absolute accuracy improvement on benchmark datasets

Effective integration of entity linking with neural models

Demonstrated superiority over existing methods

Abstract

Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. In this paper, we use entity linking to help with the fine-grained entity type classification process. We propose a deep neural model that makes predictions based on both the context and the information obtained from entity linking results. Experimental results on two commonly used datasets demonstrates the effectiveness of our approach. On both datasets, it achieves more than 5\% absolute strict accuracy improvement over the state of the art.