Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information (2022.coling-1)
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| Challenge: | Entity linking is a task of assigning entity mentions to referent entities in a knowledge base. |
| Approach: | They propose to use ultra-fine-grained type information to improve the generalization ability of EL models by utilizing a low-level task to extract ultra-finish entity type information. |
| Outcome: | The proposed model achieves state-of-the-art in the zero-shot entity linking task . |
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| Challenge: | Existing methods for fine-grained entity typing require a large tag set and knowledge of the context. |
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| Challenge: | Entity linking is a task of assigning ambiguous mentions in textual input to entities in knowledge bases. |
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| Challenge: | Existing approaches to link entities to unseen entities require in-domain labeled data. |
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| Challenge: | Existing methods for entity linking use manually curated mention tables and incoming Wikipedia link popularity. |
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| Challenge: | Existing systems consider a small set of coarse types, but fine-grained Entity Typing can be used for a variety of tasks. |
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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. |
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| Challenge: | Using a learning approach for entity mentions is a key component of modern entity linking systems for both candidate generation and making linking predictions. |
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| Challenge: | Entity linking is the task of recognising mentions of entities in unstructured text documents and linking them to the corresponding entities in a Knowledge Base (KB) the largest public EL dataset is Wikipedia, which covers just 3% of the entities in Wikidata. |
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Improving Candidate Generation for Low-resource Cross-lingual Entity Linking (2020.tacl-1)
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| Challenge: | Existing approaches to cross-lingual entity linking (XEL) do not extend well to low-resource languages with few Wikipedia pages. |
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