TY - GEN
T1 - IMPROVING BIOMEDICAL NAMED ENTITY RECOGNITION WITH A UNIFIED MULTI-TASK MRC FRAMEWORK
AU - Tong, Yiqi
AU - Zhuang, Fuzhen
AU - Wang, Deqing
AU - Ying, Haochao
AU - Wang, Binling
N1 - Publisher Copyright:
© 2022 IEEE
PY - 2022
Y1 - 2022
N2 - The prior knowledge, such as expert rules and knowledge base, has been proven effective in the traditional Biomedical Named Entity Recognition (BioNER). Most current neural BioNER systems use this external knowledge for pre-processing or post-editing instead of incorporate it into the training process, which cannot be learned by the model. To encode prior knowledge into the model, we present a unified multi-task Machine Reading Comprehension (MRC) framework for BioNER. Specifically, in the MRC task, the question sequences are derived from the standard BioNER dataset. We introduce three kinds of prior knowledge at query sequences, including Wikipedia, annotation scheme, entity dictionary. Then, our model adopts a multi-task learning strategy to joint training the main task BioNER and the auxiliary task MRC. Finally, experimental results on three benchmark datasets validate the superiority of our BioNER model compared with various state-of-the-art baselines.
AB - The prior knowledge, such as expert rules and knowledge base, has been proven effective in the traditional Biomedical Named Entity Recognition (BioNER). Most current neural BioNER systems use this external knowledge for pre-processing or post-editing instead of incorporate it into the training process, which cannot be learned by the model. To encode prior knowledge into the model, we present a unified multi-task Machine Reading Comprehension (MRC) framework for BioNER. Specifically, in the MRC task, the question sequences are derived from the standard BioNER dataset. We introduce three kinds of prior knowledge at query sequences, including Wikipedia, annotation scheme, entity dictionary. Then, our model adopts a multi-task learning strategy to joint training the main task BioNER and the auxiliary task MRC. Finally, experimental results on three benchmark datasets validate the superiority of our BioNER model compared with various state-of-the-art baselines.
KW - Biomedical named entity recognition
KW - Machine reading comprehension
KW - Multi-task learning
KW - Prior knowledge
UR - https://www.scopus.com/pages/publications/85131267795
U2 - 10.1109/ICASSP43922.2022.9746482
DO - 10.1109/ICASSP43922.2022.9746482
M3 - 会议稿件
AN - SCOPUS:85131267795
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 8332
EP - 8336
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022
Y2 - 22 May 2022 through 27 May 2022
ER -