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Prevalent solution for BioNER involves using representation learning techniques coupled with sequence labeling.
Long short-term memory
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
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The unified medical language system (UMLS): integrating biomedical terminology
O. Bodenreider · 2004
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Introduction to the bio-entity recognition task at jnlpba
N. Collier and J.-D. Kim · 2004
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Overview of biocreative ii gene mention recognition
L. Smith, L. K. Tanabe, C.-J. Kuo, I. Chung, C.-N. Hsu, Y.-S. Lin, R. Klinger, C. M. Friedrich, K. Ganchev, M. Torii, et al · 2008
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Linnaeus: a species name identification system for biomedical literature
M. Gerner, G. Nenadic, and C. M. Bergman · 2010
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The species and organisms resources for fast and accurate identification of taxonomic names in text
E. Pafilis, S. P. Frankild, L. Fanini, S. Faulwetter, C. Pavloudi, A. Vasileiadou, C. Arvanitidis, and L. J. Jensen · 2013
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NCBI disease corpus: a resource for disease name recognition and concept normalization
R. I. Doğan, R. Leaman, and Z. Lu · 2014
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Bidirectional LSTM-CRF models for sequence tagging
Z. Huang, W. Xu, and K. Yu · 2015
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The chemdner corpus of chemicals and drugs and its annotation principles
M. Krallinger, O. Rabal, F. Leitner, M. Vazquez, D. Salgado, Z. Lu, R. Leaman, Y. Lu, D. Ji, D. M. Lowe, et al · 2015
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Biocreative v cdr task corpus: a resource for chemical disease relation extraction
J. Li, Y. Sun, R. J. Johnson, D. Sciaky, C.-H. Wei, R. Leaman, A. P. Davis, C. J. Mattingly, T. C. Wiegers, and Z. Lu · 2016
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The colorado richly annotated full text (craft) corpus: Multi-model annotation in the biomedical domain
K. B. Cohen, K. Verspoor, K. Fort, C. Funk, M. Bada, M. Palmer, and L. E. Hunter · 2017
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Deep learning with word embeddings improves biomedical named entity recognition
M. Habibi, L. Weber, M. Neves, D. L. Wiegandt, and U. Leser · 2017
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2018
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Automated phrase mining from massive text corpora
J. Shang, J. Liu, M. Jiang, X. Ren, C. R. Voss, and J. Han · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. D. M.-W. C. Kenton and L. K. Toutanova · 2019
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ScispaCy: Fast and robust models for biomedical natural language processing
M. Neumann, D. King, I. Beltagy, and W. Ammar · 2019
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Cross-type biomedical named entity recognition with deep multi-task learning
X. Wang, Y. Zhang, X. Ren, Y. Zhang, M. Zitnik, J. Shang, C. Langlotz, and J. Han · 2019
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining
J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, and J. Kang · 2020
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Self-alignment pretraining for biomedical entity representations
F. Liu, E. Shareghi, Z. Meng, M. Basaldella, and N. Collier · 2020
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Inspire the large language model by external knowledge on biomedical named entity recognition
J. Bian, J. Zheng, Y. Zhang, and S. Zhu · 2023
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An extensive benchmark study on biomedical text generation and mining with ChatGPT
Q. Chen, H. Sun, H. Liu, Y. Jiang, T. Ran, X. Jin, X. Xiao, Z. Lin, H. Chen, and Z. Niu · 2023
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Zero-and few-shot machine learning for named entity recognition in biomedical texts
M. Košprdić, N. Prodanović, A. Ljajić, B. Bašaragin, and N. Milošević · 2023
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Label supervised llama finetuning
Z. Li, X. Li, Y. Liu, H. Xie, J. Li, F.-l. Wang, Q. Li, and X. Zhong · 2023
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Universal information extraction as unified semantic matching
J. Lou, Y. Lu, D. Dai, W. Jia, H. Lin, X. Han, L. Sun, and H. Wu · 2023
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HUNER: improving biomedical ner with pretraining
L. Weber, J. Münchmeyer, T. Rocktäschel, M. Habibi, and U. Leser · 2020
Cited alongside, same era.
Biomedical named entity recognition via knowledge guidance and question answering
P. Banerjee, K. K. Pal, M. Devarakonda, and C. Baral · 2021
Cited alongside, same era.
Domain-specific language model pretraining for biomedical natural language processing
Y. Gu, R. Tinn, H. Cheng, M. Lucas, N. Usuyama, X. Liu, T. Naumann, J. Gao, and H. Poon · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2021
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HunFlair: an easy-to-use tool for state-of-the-art biomedical named entity recognition
L. Weber, M. Sänger, J. Münchmeyer, M. Habibi, U. Leser, and A. Akbik · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
J. Wei, M. Bosma, V. Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le · 2021
Cited alongside, same era.
Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al · 2022
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AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning
L. Luo, C.-H. Wei, P.-T. Lai, R. Leaman, Q. Chen, and Z. Lu · 2023
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OpenAI · 2023
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On the effectiveness of compact biomedical transformers
O. Rohanian, M. Nouriborji, S. Kouchaki, and D. A. Clifton · 2023
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Llama 2: Open foundation and fine-tuned chat models
H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, et al · 2023
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UniversalNER: targeted distillation from large language models for open named entity recognition
W. Zhou, S. Zhang, Y. Gu, M. Chen, and H. Poon · 2023
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LLM2Vec: Large language models are secretly powerful text encoders
P. BehnamGhader, V. Adlakha, M. Mosbach, D. Bahdanau, N. Chapados, and S. Reddy · 2024
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Scaling instruction-finetuned language models
H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, Y. Li, X. Wang, M. Dehghani, S. Brahma, et al · 2024
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D. Dukić and J. Šnajder · 2024
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Advancing entity recognition in biomedicine via instruction tuning of large language models
V. K. Keloth, Y. Hu, Q. Xie, X. Peng, Y. Wang, A. Zheng, M. Selek, K. Raja, C. H. Wei, Q. Jin, et al · 2024
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