Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases (2022.coling-1)
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| Challenge: | Existing approaches to identifying and linking funding entities are suboptimal for the funding domain. |
| Approach: | They propose an entity linking model that can perform NIL prediction and overcome data scarcity issues in a time and data-efficient manner. |
| Outcome: | The proposed model outperforms existing baselines and overcomes data scarcity issues in a time and data-efficient manner. |
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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. |
EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing (2022.emnlp-main)
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| Challenge: | Existing work on Entity Linking assumes that the knowledge base is complete and all mentions can be linked. |
| Approach: | They propose a temporally segmented Unknown Entity Discovery and Indexing (EDIN) benchmark where unknown entities have to be integrated into existing entity linking systems. |
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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. |
| Approach: | They treat relations as latent variables while optimizing the neural entity-linking model without supervision. |
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GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction (2021.findings-acl)
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| Challenge: | a funder name refers to an agency, organization, or program providing financial support for the research. |
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Fine-Grained Evaluation for Entity Linking (D19-1)
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| Challenge: | Entity Linking (EL) is an Information Extraction task that identifies entity mentions in a text corpus and associates them with an unambiguous identifier in KBs such as Wikipedia, BabelNet, DBpedia, Wikidata and YAGO. |
| Approach: | They propose a fine-grained categorization of different types of entity mentions and links and propose 'fuzzy recall' metric to address the lack of consensus and compare a selection of online EL systems. |
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Entity Linking via Explicit Mention-Mention Coreference Modeling (2022.naacl-main)
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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. |
| Approach: | They propose a training approach that builds minimum spanning arborescences over mentions and entities to explicitly model mention coreference relationships. |
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Boosting Entity Linking Performance by Leveraging Unlabeled Documents (P19-1)
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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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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. |
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Framing Named Entity Linking Error Types (L18-1)
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| Challenge: | Named Entity Linking (NEL) and relation extraction forms the backbone of Knowledge Base Population tasks. |
| Approach: | They propose a taxonomy to frame common errors and apply it to four well-known Named Entity Linking systems. |
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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. |
| 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. |
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