Research Area:  Machine Learning
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).
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Author(s) Name:  Matthew Francis-Landau, Greg Durrett, Dan Klein
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Conferrence name:  Computer Science
Publisher name:  arXiv:1604.00734
DOI:  10.48550/arXiv.1604.00734
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Paper Link:   https://arxiv.org/abs/1604.00734