Back to overview

Deep Joint Entity Disambiguation with Local Neural Attention

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Author Ganea O.E., Hofmann T.,
Project Conversational Agent forInteractive Access to Information
Show all

Proceedings (peer-reviewed)

Title of proceedings Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Open Access

Type of Open Access Website


We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation. Our approach thereby combines benefits of deep learning with more traditional approaches such as graphical models and probabilistic mention-entity maps. Extensive experiments show that we are able to obtain competitive or state-of-the-art accuracy at moderate computational costs.