| Challenge: | a new approach to entity linking relies on unlabeled documents and Wikipedia . a supervised approach uses only natural information, such as unlabed documents . |
| Approach: | They propose a method which exploits only naturally occurring information . they construct a high recall list of candidate entities for each mention in an unlabeled document . |
| Outcome: | The proposed model outperforms fully-supervised state-of-the-art systems on standard test sets. |
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| Challenge: | Entity linking systems often exploit relations between textual mentions to decide if the linking decisions are compatible. |
| Approach: | They treat relations as latent variables while optimizing the neural entity-linking model without supervision. |
| Outcome: | The proposed model outperforms its relation-agnostic version and significantly outperformed its relational version. |
Distant Learning for Entity Linking with Automatic Noise Detection (P19-1)
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| Challenge: | Accurate entity linkers have been produced for domains and languages where no or very limited amounts of labeled data are available. |
| Approach: | They propose to use annotated text to learn to link entities without labeling . they frame the task as a multi-instance learning problem and rely on surface matching to create initial noisy labels. |
| Outcome: | The proposed method outperforms the baseline surface matching model for a subset of entities. |
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. |
| Outcome: | The proposed model achieves state-of-the-art on out-of domain datasets and compares with baselines. |
Unified Examination of Entity Linking in Absence of Candidate Sets (2024.naacl-short)
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| Challenge: | Entity linking systems depend on candidate sets for their performance, but a comprehensive comparative analysis of these systems is lacking. |
| Approach: | They propose a black-box benchmark and a method to evaluate all state-of-the-art entity linking methods. |
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Handling Entity Normalization with no Annotated Corpus: Weakly Supervised Methods Based on Distributional Representation and Ontological Information (2020.lrec-1)
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Arnaud Ferré, Robert Bossy, Mouhamadou Ba, Louise Deléger, Thomas Lavergne, Pierre Zweigenbaum, Claire Nédellec
| Challenge: | Entity normalization is an important subtask of information extraction . it links entities mentions in text to categories or concepts in a reference vocabulary . |
| Approach: | They propose a method that uses corpus selection, pre-processing and weak supervision strategies to address the scarcity of training data. |
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Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection (2022.emnlp-main)
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| Challenge: | Entity linking is an important task for language understanding. |
| Approach: | They propose a fully unsupervised model that generates a guided summary of the contexts conditioning on a mention and then casts the task to a multiple-choice problem. |
| Outcome: | The proposed model achieves state-of-the-art performance on existing datasets and exiting datasets. |
Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas (2021.naacl-main)
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| Challenge: | Existing work on entity linking relies on a knowledge base that is not known at training time. |
| Approach: | They propose a method to flexibly convert entities with several attribute-value pairs from arbitrary KBs into flat strings and use it to generalize the model. |
| Outcome: | The proposed model is 12% more accurate than baseline models on English datasets. |
Learning Dynamic Context Augmentation for Global Entity Linking (D19-1)
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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. |
Low-Rank Subspaces for Unsupervised Entity Linking (2021.emnlp-main)
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| Challenge: | Entity linking is an important problem with many applications. |
| Approach: | They propose a method that exploits the fact that entities that are truly mentioned in a document tend to form a semantically dense subset of all candidate entities in the document. |
| Outcome: | a new method that outperforms existing methods on real-world datasets outperformed existing methods. |
Learn to Not Link: Exploring NIL Prediction in Entity Linking (2023.findings-acl)
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| Challenge: | Entity linking models have been successful in capturing semantic features, but the NIL prediction problem has not been addressed. |
| Approach: | They propose an entity linking dataset that categorizes mentions linking to NIL into Missing Entity and Non-Entity Phrases. |
| Outcome: | The proposed dataset categorizes mentions linking to NIL into Missing Entity and Non-Entity Phrase categories and ensures the presence of mentions by human annotation and entity masking. |