Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

Understand

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.