Neural Collective Entity Linking

TL;DR

This paper introduces NCEL, a neural collective entity linking model that uses Graph Convolutional Networks to incorporate local and global information, improving accuracy and efficiency in linking entities in texts.

Contribution

The paper proposes a novel neural model, NCEL, that integrates local and global features via graph convolution and employs attention for robustness, with efficient subgraph-based computation.

Findings

NCEL outperforms existing methods on five datasets.

The model demonstrates strong generalization and robustness.

Efficiency is improved through subgraph-based graph convolution.

Abstract

Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of those in the entire text. We further introduce an attention scheme to improve the robustness of NCEL to data noise and train the model on Wikipedia hyperlinks to avoid overfitting and domain bias. In experiments, we evaluate NCEL on five publicly available datasets to verify the linking performance as well as generalization ability. We also conduct an extensive analysis of time complexity, the impact of key modules, and qualitative results, which demonstrate the effectiveness and efficiency of our proposed method.