DTranNER: biomedical named entity recognition with deep learning-based label-label transition model
作者: S. K. HongJae-Gil Lee
作者单位: 1Graduate School of Knowledge Service Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, South Korea
2Department of Industrial & Systems Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, South Korea
刊名: BMC Bioinformatics, 2020, Vol.21 (10), pp.15-52
来源数据库: Springer Nature Journal
DOI: 10.1186/s12859-020-3393-1
关键词: BioinformaticsData miningNamed entity recognitionNeural network
英文摘要: Abstract(#br)Background(#br)Biomedical named-entity recognition (BioNER) is widely modeled with conditional random fields (CRF) by regarding it as a sequence labeling problem. The CRF-based methods yield structured outputs of labels by imposing connectivity between the labels. Recent studies for BioNER have reported state-of-the-art performance by combining deep learning-based models (e.g., bidirectional Long Short-Term Memory) and CRF. The deep learning-based models in the CRF-based methods are dedicated to estimating individual labels, whereas the relationships between connected labels are described as static numbers; thereby, it is not allowed to timely reflect the context in generating the most plausible label-label transitions for a given input sentence. Regardless, correctly...
全文获取路径: Springer Nature  (合作)
影响因子:3.024 (2012)