Neural Collective Entity Linking

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.

12 pages, 3 figures, COLING2018

References in corpus (3)

• Loopy Belief Propagation for Approximate Inference: An Empirical Study
• Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
• Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All

Cited by in corpus (15)

• KGAT: Knowledge Graph Attention Network for Recommendation
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• Neural Entity Linking: A Survey of Models Based on Deep Learning
• Falcon 2.0: An Entity and Relation Linking Tool over Wikidata
• Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
• Improving Neural Relation Extraction with Implicit Mutual Relations
• Scalable Zero-shot Entity Linking with Dense Entity Retrieval
• Multi-Channel Graph Neural Network for Entity Alignment
• Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment
• Knowledge-Enhanced Recommendation with User-Centric Subgraph Network
• Low-Resource Name Tagging Learned with Weakly Labeled Data
• Better Together -- An Ensemble Learner for Combining the Results of Ready-made Entity Linking Systems
• Neural Attentive Bag-of-Entities Model for Text Classification
• Joint Entity Linking with Deep Reinforcement Learning
• Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution