Deep Joint Entity Disambiguation with Local Neural Attention
Octavian-Eugen Ganea, Thomas Frank Hofmann · 2017
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 stateof-the-art accuracy at moderate computational costs.