# Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1604.00734 · HTML: https://mlsanity.com/paper/1604.00734/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2016.

## Built on by (most cited first, 14 of 14)

- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [Multi-Grained Multimodal Interaction Network for Entity Linking](https://mlsanity.com/paper/2307.09721/) (2023)
- [Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem](https://mlsanity.com/paper/2204.05990/) (2022)
- [DRIN: Dynamic Relation Interactive Network for Multimodal Entity Linking](https://mlsanity.com/paper/2310.05589/) (2023)
- [UniMEL: A Unified Framework for Multimodal Entity Linking with Large Language Models](https://mlsanity.com/paper/2407.16160/) (2024)
- [Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology](https://mlsanity.com/paper/2101.10587/) (2021)
- [Evaluating Pretrained Transformer Models for Entity Linking in Task-Oriented Dialog](https://mlsanity.com/paper/2112.08327/) (2021)
- [Argument-Aware Approach To Event Linking](https://mlsanity.com/paper/2403.15097/) (2024)
- [A First Context-Free Grammar Applied to Nawatl Corpora Augmentation](https://mlsanity.com/paper/2510.04945/) (2025)
- [Two CFG Nahuatl for automatic corpora expansion](https://mlsanity.com/paper/2512.14239/) (2025)
- [Classifying several dialectal Nawatl varieties](https://mlsanity.com/paper/2601.02303/) (2026)
- [Mapping the Past: Geographically Linking an Early 20th Century Swedish Encyclopedia with Wikidata](https://mlsanity.com/paper/2406.17903/) (2024)
- [JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase](https://mlsanity.com/paper/2411.02695/) (2024)
- [Corpora deduplication or duplication in Natural Language Processing of few resourced languages ? A case of study: The Mexico's Nahuatl](https://mlsanity.com/paper/2604.07015/) (2026)

## Abstract (arXiv)

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).