DTranNER: biomedical named entity recognition with deep learning-based label-label transition model [PDF]
BMC Bioinformatics, 2020 Background Biomedical named-entity recognition (BioNER) is widely modeled with conditional random fields (CRF) by regarding it as a sequence labeling problem.
S. K. Hong, Jae-Gil Lee
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CollaboNet: collaboration of deep neural networks for biomedical named entity recognition [PDF]
BMC Bioinformatics, 2019 Background Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results.
Wonjin Yoon +3 more
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Comparison of named entity recognition methodologies in biomedical documents [PDF]
BioMedical Engineering OnLine, 2018 Background Biomedical named entity recognition (Bio-NER) is a fundamental task in handling biomedical text terms, such as RNA, protein, cell type, cell line, and DNA.
Hye-Jeong Song +4 more
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Various criteria in the evaluation of biomedical named entity recognition [PDF]
BMC Bioinformatics, 2006 Background Text mining in the biomedical domain is receiving increasing attention. A key component of this process is named entity recognition (NER). Generally speaking, two annotated corpora, GENIA and GENETAG, are most frequently used for training and ...
Lin Yu-Chun +7 more
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A neural network multi-task learning approach to biomedical named entity recognition [PDF]
BMC Bioinformatics, 2017 Background Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size.
Gamal Crichton +3 more
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Biomedical named entity recognition using deep neural networks with contextual information [PDF]
BMC Bioinformatics, 2019 Background In biomedical text mining, named entity recognition (NER) is an important task used to extract information from biomedical articles. Previously proposed methods for NER are dictionary- or rule-based methods and machine learning approaches ...
Hyejin Cho, Hyunju Lee
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Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
Applied SciencesBiomedical named entity recognition (BioNER) aims to identify and classify biomedical entities (i.e., diseases, chemicals, and genes) from text into predefined classes. This process serves as an important initial step in extracting biomedical information
Yesol Park, Gyujin Son, Mina Rho
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Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model [PDF]
BMC BioinformaticsBackground Biomedical text mining is a technique that extracts essential information from scientific articles using named entity recognition (NER).
Ram Chandra Bhushan +4 more
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On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition
Future Internet, 2023 Biomedical named entity recognition (BioNER) is a preliminary task for many other tasks, e.g., relation extraction and semantic search. Extracting the text of interest from biomedical documents becomes more demanding as the availability of online data is
Tahir Mehmood +4 more
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Improving biomedical named entity recognition with syntactic information. [PDF]
BMC Bioinformatics, 2020 Abstract Background Biomedical named entity recognition (BioNER) is an important task for understanding biomedical texts, which can be challenging due to the lack of large-scale labeled training data and domain knowledge.
Tian Y +5 more
europepmc +4 more sources