A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention's context and a proposed target entity. These convolutional networks operate at multiple granularities to exploit various kinds of topic information, and their rich parameterization gives them the capacity to learn which n-grams characterize different topics. We combine these networks with a sparse linear model to achieve state-of-the-art performance on multiple entity linking datasets, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).
@article{arxiv.1604.00734,
title = {Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks},
author = {Matthew Francis-Landau and Greg Durrett and Dan Klein},
journal= {arXiv preprint arXiv:1604.00734},
year = {2016}
}