Papers with Named Entity Linking
TrainX – Named Entity Linking with Active Sampling and Bi-Encoders (2020.coling-demos)
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Tom Oberhauser, Tim Bischoff, Karl Brendel, Maluna Menke, Tobias Klatt, Amy Siu, Felix Alexander Gers, Alexander Löser
| Challenge: | Existing easyto-use annotation tools do not support entity linking, which leads to additional training costs for medical professionals. |
| Approach: | They propose a system for Named Entity Linking for medical experts . they use Flair and BERT to support annotating training data with active sampling . |
| Outcome: | The proposed system is capable of linking against large knowledge bases and supporting zero-shot cases where the linker has never seen the entity before. |
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. |
| Outcome: | The proposed taxonomy was applied to four well-known Named Entity Linking systems on three gold standards. |
A Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extraction (2026.findings-eacl)
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Marco Martinelli, Stefano Marchesin, Vanessa Bonato, Giorgio Di Nunzio, Nicola Ferro, Ornella Irrera, Laura Menotti, Federica Vezzani, Gianmaria Silvello
| Challenge: | Existing biomedical IE benchmarks are narrow in scope and rely heavily on distantly supervised annotations. |
| Approach: | They propose a benchmark for Information Extraction (IE) that annotates entities, concept-level links, and relations manually from PubMed abstracts. |
| Outcome: | The GutBrainIE benchmark is based on more than 1,600 PubMed abstracts, manually annotated by biomedical and terminological experts with fine-grained entities, concept-level links, and relations. |