| 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 . |
| Outcome: | The proposed model achieves an F1 score of 0.540.15 for Chinese biomedical patent data. |
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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. |
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. |
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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A Dataset for Named Entity Recognition and Entity Linking in Chinese Historical Newspapers (2024.lrec-main)
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| Challenge: | a novel historical Chinese dataset is used for named entity recognition, entity linking and entity relations. |
| Approach: | They propose a historical Chinese dataset for named entity recognition, entity linking, coreference and entity relations . they use Chinese newspapers from 1872 to 1949 and multilingual bibliographic resources from the same period . |
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A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is an important task in information extraction. |
| Approach: | They construct a labelled NER corpus of Vietnamese academic biomedical text . they annotate documents with five categories of named entities: Organisation, Location, Date and Time, Symptom and Disease, and Diagnostic Procedure. |
| Outcome: | The proposed system could provide answers to questions related to TB in Vietnamese . the system could also be used to identify TB-related diseases in the country . |
Can Synthetic Text Help Clinical Named Entity Recognition? A Study of Electronic Health Records in French (2023.eacl-main)
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| Challenge: | In sensitive domains, the sharing of corpora is restricted due to confidentiality, copyrights or trade secrets. |
| Approach: | They use auto-regressive neural models to generate a clinical case corpus annotated with clinical entities and evaluate it for a named entity recognition task. |
| Outcome: | The proposed model can produce clinical case corpus annotated with clinical entities while maintaining confidentiality. |
Medical Spoken Named Entity Recognition (2025.naacl-industry)
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Khai Le-Duc, David Thulke, Hung-Phong Tran, Long Vo-Dang, Khai-Nguyen Nguyen, Truong-Son Hy, Ralf Schlüter
| Challenge: | Named Entity Recognition (NER) aims to extract named entities from speech and categorise them into types like person, location, organization, etc. |
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| Outcome: | The dataset is the largest spoken NER dataset in the world regarding the number of entity types, featuring 18 distinct types. |
M-CNER: A Corpus for Chinese Named Entity Recognition in Multi-Domains (L18-1)
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| Challenge: | NER is one of the most important natural language processing tasks. |
| Approach: | They propose to annotate sentences from human-computer interaction, social media, and e-commerce using two rounds of annotation. |
| Outcome: | The proposed system performs the best on all the data sets. |
Entity Enhanced BERT Pre-training for Chinese NER (2020.emnlp-main)
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| Challenge: | Character-level BERT pre-trained in Chinese suffers from lacking lexicon information, which shows effectiveness for Chinese NER. |
| Approach: | They propose a semi-supervised method to integrate lexicon into pre-trained LMs in Chinese . they extract an entity lexiconal from raw text and integrate it into BERT . |
| Outcome: | The proposed method is highly effective and achieves the best results on a news dataset and two datasets annotated by the authors. |
Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition (2024.lrec-main)
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Sungjoo Byun, Jiseung Hong, Sumin Park, Dongjun Jang, Jean Seo, Minseok Kim, Chaeyoung Oh, Hyopil Shin
| Challenge: | Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP) yet, there is no open-source medical NER dataset specifically for Korean. |
| Approach: | They used ChatGPT to construct an open-source Korean NER dataset . they found 20% increase in medical NER performance compared to general Korean ner datasets. |
| Outcome: | The KBMC dataset shows an impressive 20% increase in medical NER performance compared to models trained on general Korean NER datasets. |