# Learning Dynamic Context Augmentation for Global Entity Linking

arXiv: https://arxiv.org/abs/1909.02117 · HTML: https://mlsanity.com/paper/1909.02117/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2019.

## Built on by (most cited first, 19 of 19)

- [CM3: A Causal Masked Multimodal Model of the Internet](https://mlsanity.com/paper/2201.07520/) (2022)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata](https://mlsanity.com/paper/2101.09969/) (2021)
- [Multi-Grained Multimodal Interaction Network for Entity Linking](https://mlsanity.com/paper/2307.09721/) (2023)
- [Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge](https://mlsanity.com/paper/2211.10997/) (2022)
- [Improving Entity Linking through Semantic Reinforced Entity Embeddings](https://mlsanity.com/paper/2106.08495/) (2021)
- [Generative Entity Typing with Curriculum Learning](https://mlsanity.com/paper/2210.02914/) (2022)
- [Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia](https://mlsanity.com/paper/2211.00732/) (2022)
- [AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes](https://mlsanity.com/paper/2305.14725/) (2023)
- [Reddit Entity Linking Dataset](https://mlsanity.com/paper/2101.01228/) (2021)
- [Improving Entity Disambiguation by Reasoning over a Knowledge Base](https://mlsanity.com/paper/2207.04106/) (2022)
- [Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution](https://mlsanity.com/paper/2108.13530/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [Evaluating Design Decisions for Dual Encoder-based Entity Disambiguation](https://mlsanity.com/paper/2505.11683/) (2025)
- [Better Call the Plumber: Orchestrating Dynamic Information Extraction Pipelines](https://mlsanity.com/paper/2102.10966/) (2021)
- [Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings](https://mlsanity.com/paper/2305.12027/) (2023)
- [Multi-level Matching Network for Multimodal Entity Linking](https://mlsanity.com/paper/2412.10440/) (2024)
- [Learning to Select the Next Reasonable Mention for Entity Linking](https://mlsanity.com/paper/2112.04104/) (2021)

## Abstract (arXiv)

Despite of the recent success of collective entity linking (EL) methods, these "global" inference methods may yield sub-optimal results when the "all-mention coherence" assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space. In this paper, we propose a simple yet effective solution, called Dynamic Context Augmentation (DCA), for collective EL, which requires only one pass through the mentions in a document. DCA sequentially accumulates context information to make efficient, collective inference, and can cope with different local EL models as a plug-and-enhance module. We explore both supervised and reinforcement learning strategies for learning the DCA model. Extensive experiments show the effectiveness of our model with different learning settings, base models, decision orders and attention mechanisms.