Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren
| Challenge: | Existing collective entity linking methods are expensive and often lack local context information. |
| Approach: | They propose a dynamic context-augmented inference model that can be used to make collective inference. |
| Outcome: | The proposed model can cope with different local EL models with different learning settings, base models, decision orders and attention mechanisms. |
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| Challenge: | a new approach to entity linking relies on unlabeled documents and Wikipedia . a supervised approach uses only natural information, such as unlabed documents . |
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Contextual Augmentation for Entity Linking using Large Language Models (2025.coling-main)
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| Challenge: | Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. |
| Approach: | They propose a fine-tuned model that integrates entity recognition and disambiguation in a unified framework. |
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Contextualized End-to-End Neural Entity Linking (2020.aacl-main)
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| Challenge: | a proposed entity linking model that disjointly applies MD and ED from the same contextualized BERT embeddings is able to generalize better. |
| Approach: | They propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED) they propose to use task-specific heads on top of shared BERT contextualized embeddings to learn MD and ED. |
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AELC: Adaptive Entity Linking with LLM-Driven Contextualization (2025.findings-emnlp)
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| Challenge: | Entity linking (EL) focuses on associating ambiguous mentions in text with corresponding entities in a knowledge graph. |
| Approach: | Entity linking (EL) focuses on associating ambiguous mentions in text with corresponding entities in a knowledge graph. |
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Improving Entity Linking by Modeling Latent Relations between Mentions (P18-1)
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| Challenge: | Entity linking systems often exploit relations between textual mentions to decide if the linking decisions are compatible. |
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Neural Collective Entity Linking (C18-1)
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| Challenge: | Entity linking aims to link entity mentions in texts to knowledge bases, but existing methods rely on local contexts to resolve entities independently. |
| Approach: | They propose a neural model for collective entity linking that integrates local contextual features and global coherence information to improve the computation efficiency. |
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Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition (2024.acl-short)
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| Challenge: | specialized fields such as science and biology face significant challenges due to the scarcity of quality data. |
| Approach: | They propose a guidance data augmentation technique that abstracts context and sentence structure and maintains context-entity relationships for DA. |
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Joint Learning of Named Entity Recognition and Entity Linking (P19-2)
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| Challenge: | Named entity recognition and entity linking are two fundamentally related tasks . most approaches focus on the mention detection part, assuming the correct mentions have been detected . |
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The Role of Global and Local Context in Named Entity Recognition (2023.acl-short)
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| Challenge: | Named Entity Recognition (NER) models are usually applied sequentially because of their complexity. |
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Effective Use of Context in Noisy Entity Linking (D18-1)
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| Challenge: | Effectively using an entity mention's context to disambiguate it is the crux of the entity linking task. |
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