Comparative evaluation of boundary-relaxed annotation for Entity Linking performance (2023.acl-long)
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| Challenge: | Entity Linking is a critical step for information extraction, allowing the retrieval and understanding of information from unstructured textual sources. |
| Approach: | They propose to use noisy datasets to generate noisy versions of annotated entity mentions and then train three Entity Linking models on this data. |
| Outcome: | The proposed model can be used to associate NE mentions to a single concept in an ontology, allowing for better indexing and relation extraction. |
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| Challenge: | Existing models achieve F1-scores comparable to or exceed noise level in CoNLL-03 . current models have significant annotation errors, incompleteness, and inconsistencies in the data . |
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| Challenge: | Existing evaluations of entity linking systems often lack detailed error analysis or a closer look at the results. |
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Boundary Smoothing for Named Entity Recognition (2022.acl-long)
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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. |
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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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Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)
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| Challenge: | Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models. |
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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. |
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