Improving biomedical named entity recognition with syntactic information

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. To address the challenge, in addition to using powerful encoders (e.g., biLSTM and BioB...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 21; no. 1; p. 539
Main Authors:	Tian, Yuanhe, Shen, Wang, Song, Yan, Xia, Fei, He, Min, Li, Kenli
Format:	Journal Article
Language:	English
Published:	London BioMed Central 25.11.2020 BioMed Central Ltd Springer Nature B.V
Subjects:	Algorithms Annotations Benchmarking Bioinformatics Biomedical and Life Sciences Biomedical Research Biosensors Coders Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data Mining Databases as Topic Datasets Deep Learning Disease Information processing Life Sciences Machine Learning and Artificial Intelligence in Bioinformatics Methods Microarrays Recognition Research Article Semantics Statistics as Topic Texts United Kingdom Text mining Syntactic information Key-value memory networks Neural networks Named entity recognition
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
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