| Challenge: | a biomedical entity linking system is available for COVID-19 research. |
| Approach: | They propose a biomedical entity linking system that detects named enti- ties in text and links them to the UMLS knowledge base. |
| Outcome: | The proposed system detects named enti- ties in text and links them to the unified medical language system (UMS) knowledge base entries. |
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Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning (2022.naacl-main)
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| Challenge: | Generative methods for biomedical entity linking (EL) use synonyms knowledge from knowledge bases (KB) this is not trivial to inject into a generative method, but it is cost-effective. |
| Approach: | They propose to inject synonyms knowledge into a generative model of biomedical EL by constructing synthetic samples with synonyms and definitions from KB and requiring the model to recover concept names. |
| Outcome: | The proposed method achieves state-of-the-art results on several biomedical EL tasks without candidate selection. |
COMETA: A Corpus for Medical Entity Linking in the Social Media (2020.emnlp-main)
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| Challenge: | Existing datasets for Entity Linking (EL) fail to address the complex nature of health terminology in layman’s language. |
| Approach: | They propose to use a corpus of 20k English biomedical entity mentions from Reddit expert-annotated with links to a widely-used medical knowledge graph to investigate the ability of these systems to perform complex inference on entities and concepts. |
| Outcome: | The proposed corpus satisfies a combination of desirable properties, from scale and coverage to diversity and quality, that to the best of our knowledge has not been met by existing resources in the field. |
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. |
| Approach: | They propose to exploit Named Entity Recognition (NER) to narrow the gap between EL systems trained on high and low amounts of labeled data. |
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LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)
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Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray
| Challenge: | Existing work deals with EL in the context of longer text, such as a sentence. |
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A Comprehensive Evaluation of Biomedical Entity Linking Models (2023.emnlp-main)
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David Kartchner, Jennifer Deng, Shubham Lohiya, Tejasri Kopparthi, Prasanth Bathala, Daniel Domingo-Fernández, Cassie Mitchell
| Challenge: | Current methods struggle to correctly link genes and proteins and often have difficulty incorporating context into linking decisions. |
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Joint Learning of Named Entity Recognition and Entity Linking (P19-2)
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| Challenge: | Named entity recognition and entity linking are two fundamentally related tasks . most approaches focus on the mention detection part, assuming the correct mentions have been detected . |
| Approach: | They perform joint learning of named entity recognition and entity linking to leverage their relatedness. |
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entity-linkings: A Unified Library for Entity Linking (2026.eacl-demo)
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| Challenge: | Entity linking (EL) is the task of mapping named entities in text to canonical entries in a knowledge base. |
| Approach: | They propose a unified library for using and developing entity linking systems . a strong emphasis is placed on usability, making it highly extensible . |
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NNE: A Dataset for Nested Named Entity Recognition in English Newswire (P19-1)
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| Challenge: | Named entity recognition (NER) is widely used in downstream tasks but most tools focus on flat mention structure over coarse schemas. |
| Approach: | They describe a fine-grained, nested named entity dataset over the Wall Street Journal portion of the Penn Treebank. |
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Neural Modeling for Named Entities and Morphology (NEMO2) (2021.tacl-1)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. |
| Approach: | They develop a morphologically rich-and-ambiguous language with a token-level and morpheme-level NER annotation framework to address Named Entity Recognition (NER) a novel hybrid architecture precedes and prunes morphology and outperforms the standard pipeline for Hebrew NER and Hebrew morphologies. |
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MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation) (2022.findings-naacl)
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| Challenge: | Named Entity Recognition (NER) is a process of identifying named entities in unstructured texts and classifying them through specific semantic categories. |
| Approach: | They propose a method for automatically producing NER annotations and introduce a manually-annotated test set. |
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