Work

Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

Year: 2016

Type: conference-paper

Abstract: 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). 1

Source: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

DOI: 10.18653/v1/n16-1150

Authors: Matthew Francis-Landau, Greg Durrett, Dan Klein

Institution: University of California, Berkeley

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: TWC: Frontier: Collaborative: Beyond Technical Security: Developing an Empirical Basis for Socio-Economic Perspectives