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A prefix and attention map discrimination fusion guided attention for biomedical named entity recognition
BackgroundThe biomedical literature is growing rapidly, and it is increasingly important to extract meaningful information from the vast amount of...
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MLM-based typographical error correction of unstructured medical texts for named entity recognition
BackgroundUnstructured text in medical records, such as Electronic Health Records, contain an enormous amount of valuable information for research;...
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BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework
BackgroundAutomatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining,...
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Biomedical named entity recognition with the combined feature attention and fully-shared multi-task learning
BackgroundBiomedical named entity recognition (BioNER) is a basic and important task for biomedical text mining with the purpose of automatically...
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Improving deep learning method for biomedical named entity recognition by using entity definition information
BackgroundBiomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in...
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Hierarchical shared transfer learning for biomedical named entity recognition
BackgroundBiomedical named entity recognition (BioNER) is a basic and important medical information extraction task to extract medical entities with...
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Analyzing transfer learning impact in biomedical cross-lingual named entity recognition and normalization
BackgroundThe volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in...
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NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding
Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades
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Deep learning with language models improves named entity recognition for PharmaCoNER
BackgroundThe recognition of pharmacological substances, compounds and proteins is essential for biomedical relation extraction, knowledge graph...
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Improving the recall of biomedical named entity recognition with label re-correction and knowledge distillation
BackgroundBiomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from...
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Fusing Label Relations for Chinese EMR Named Entity Recognition with Machine Reading Comprehension
Chinese electronic medical records named entity recognition (NER) is a core task in medical knowledge mining, which is usually viewed as a sequence...
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Improving biomedical named entity recognition with syntactic information
BackgroundBiomedical named entity recognition (BioNER) is an important task for understanding biomedical texts, which can be challenging due to the...
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Knowledge-enhanced biomedical named entity recognition and normalization: application to proteins and genes
BackgroundAutomated biomedical named entity recognition and normalization serves as the basis for many downstream applications in information...
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A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature
We report a combined manual annotation and deep-learning natural language processing study to make accurate entity extraction in hereditary disease...
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An analysis of entity normalization evaluation biases in specialized domains
BackgroundEntity normalization is an important information extraction task which has recently gained attention, particularly in the...
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A hybrid deep learning framework for bacterial named entity recognition with domain features
BackgroundMicrobes have been shown to play a crucial role in various ecosystems. Many human diseases have been proved to be associated with bacteria,...
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pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms
BackgroundMany disease causing genes have been identified through different methods, but there have been no uniform annotations of biomedical named...
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Context-aware multi-token concept recognition of biological entities
BackgroundConcept recognition is a term that corresponds to the two sequential steps of named entity recognition and named entity normalization, and...
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Flat and Nested Protein Name Recognition Based on BioBERT and Biaffine Decoder
With the rapid growth in the volume of literature in the biomedical field, it has become increasingly important to extract key information from it in...
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GPDminer: a tool for extracting named entities and analyzing relations in biological literature
PurposeThe expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the...
