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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
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Proceedings (peer-reviewed)

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

Open Access

URL https://arxiv.org/abs/1704.04920
Type of Open Access Website

Abstract

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.
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