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Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

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arxiv 1604.00734 v1 pith:TX3DXCX4 submitted 2016-04-04 cs.CL

Matthew Francis-Landau , Greg Durrett , Dan Klein This is my paper · ORCID
classification cs.CL
keywords entitynetworksconvolutionaldifferentlinkinginformationmodelmultiple
verification ladder T0 review T1 audit T2 compute T3 formal

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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation

    cs.CL 2019-08 conditional novelty 7.0 of 10

    An entity-aware extension of ELMo, E-ELMo, predicts gold entities at mention positions and powers a local entity disambiguation model that achieves state-of-the-art results on AIDA and TAC 2010.

  2. Learning Dynamic Context Augmentation for Global Entity Linking

    cs.CL 2019-09 conditional novelty 6.0 of 10

    Sequentially accumulating attention-weighted context from previously linked entities, one pass per document, improves entity-linking accuracy over joint global inference and reduces inference cost from roughly quadrat...

  3. JEL: A Novel Model Linking Knowledge Graph entities to News Mentions

    cs.LG 2025-09 reject novelty 3.0 of 10

    JEL, a surface-plus-semantic entity linking model, reportedly beats BLINK by 15% F1 on an internal fuzzy-filtered news dataset, with no public benchmark or code.

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