Papers with AIDA-CoNLL
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. |
| 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. |
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem (2022.findings-acl)
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| Challenge: | Existing methods for entity linking do not use a knowledge base or candidate sets. |
| Approach: | They propose an autoregressive entity linking model that is trained with two auxiliary tasks and learns to re-rank generated samples at inference time. |
| Outcome: | The proposed model improves on two biomedical datasets and a news domain dataset without the use of a knowledge base or candidate sets. |
Global Entity Disambiguation with BERT (2022.naacl-main)
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| Challenge: | Entity disambiguation (ED) is a task of assigning mentions to referent entities in a knowledge base. |
| Approach: | They propose a global entity disambiguation (ED) model based on BERT . they train the model using a large entity-annotated corpus obtained from Wikipedia . |
| Outcome: | The proposed model can disambiguate masked entities based on words and non-masked ones at the inference time. |