| Challenge: | a multilingual model is not specifically tailored to either the language nor the application domain. |
| Approach: | They propose a CRF-based baseline approach and multilingual BERT to the task . they achieve an F-score of 88% on the development data and 87% on the test set with BERT . |
| Outcome: | The proposed model achieves an F score of 88% on the development data and 87% on the test set with BERT. |
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Transfer Learning in Biomedical Named Entity Recognition: An Evaluation of BERT in the PharmaCoNER task (D19-57)
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| Challenge: | Existing methods for natural language processing are labor-intensive and skill-dependent . Currently, most biomedical natural language tasks focus on English documents . |
| Approach: | They introduce a BERT benchmark to facilitate the research of PharmaCoNER task . they evaluate two baselines based on Multilingual BERT and BioBERT on the corpus . |
| Outcome: | The proposed task is based on multilingual BERT and BioBERT on the PharmaCoNER corpus. |
When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish (D19-57)
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| Challenge: | Existing work on pharmacological entities requires manual annotation of these units. |
| Approach: | They propose an approach to task 1 of the PharmaCoNER Challenge to recognize pharmacological entities on a spanish corpus. |
| Outcome: | The proposed approach achieves 89.76% score on a spanish corpus based on pre-trained embeddings and 90.52% score on domain-specific embeddables. |
OpenBioNER: Lightweight Open-Domain Biomedical Named Entity Recognition Through Entity Type Description (2025.findings-naacl)
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Alessio Cocchieri, Giacomo Frisoni, Marcos Martínez Galindo, Gianluca Moro, Giuseppe Tagliavini, Francesco Candoli
| Challenge: | Biomedical Named Entity Recognition (BioNER) is a computationally expensive and limited tool . specialized 7B NER LLMs and GPT-4o can't match textual spans with entity types . |
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Where do LLMs currently stand on biomedical NER in both clean and noisy settings ? (2026.findings-eacl)
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| Challenge: | despite advances in medicine, many diseases remain without effective treatments . clinical meta-analysis is essential for drug discovery and clinical research . |
| Approach: | They investigate the performance of large language models (LLMs) on biomedical NER tasks . findings suggest LLMs exhibit a notable degree of robustness to noise . |
| Outcome: | The proposed models are closing the performance gap with BERT-based models and demonstrate particular strengths in low-data settings. |
A Benchmark Evaluation of Clinical Named Entity Recognition in French (2024.lrec-main)
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| Challenge: | Masked Language Models (MLMs) have shown strong performance on many NLP tasks. |
| Approach: | They evaluate masked language models for biomedical French on the task of clinical named entity recognition using gold-standard corpora. |
| Outcome: | The proposed model outperforms standard models on the task of clinical named entity recognition in biomedical French while remaining lighter than current models. |
TeluguNER: Leveraging Multi-Domain Named Entity Recognition with Deep Transformers (2022.acl-srw)
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| Challenge: | Named Entity Recognition (NER) is a successful and well-researched problem in English due to the availability of resources. |
| Approach: | They propose to use two annotated NER datasets for the Telugu language . they compare the finetuned Telugus model with the existing model in NER . |
| Outcome: | The proposed models outperform existing models on a large dataset of 38,363 sentences on telugu and other languages. |
Named Entity Recognition for Chinese biomedical patents (2020.coling-main)
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| Challenge: | Existing attempts to address NER for Chinese biomedical texts have been limited due to the amount of Chinese biomedicine discoveries being patented. |
| Approach: | They train and evaluate Chinese biomedical patents NER models based on BERT . their model is optimized for Chinese bio-patent data and scored an F1 . |
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PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track (D19-57)
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Aitor Gonzalez-Agirre, Montserrat Marimon, Ander Intxaurrondo, Obdulia Rabal, Marta Villegas, Martin Krallinger
| Challenge: | Biomedical text mining is one of the most prolific application domains of natural language processing technologies. |
| Approach: | They propose to share a task on detecting drug and chemical entities in medical documents in Spanish with other languages to improve access to biomedical text mining. |
| Outcome: | The first task on detecting drug and chemical entities in Spanish medical documents yielded competitive results with F-measures above 0.91. |
IxaMed at PharmacoNER Challenge 2019 (D19-57)
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| Challenge: | The aim of this paper is to present our approach in the PharmacoNER 2019 task. |
| Approach: | They propose to use a Bi-LSTM with a CRF to identify named entities from clinical case studies written in Spanish. |
| Outcome: | The proposed approach achieves the best score (86.81 F-Score) combining pretrained word embeddings of Wikipedia and Electronic Health Records with contextual string embedds. |
Exploring Cross-sentence Contexts for Named Entity Recognition with BERT (2020.coling-main)
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| Challenge: | Named entity recognition (NER) is often addressed as a sequence classification task with each input consisting of one sentence of text. |
| Approach: | They propose a method to combine different predictions from multiple sentences in input samples to increase NER performance. |
| Outcome: | The proposed method improves on the state-of-the-art NER results on English, Dutch, and Finnish and achieves the best reported BERT-based results on German. |