scholarly journals Transfer learning for biomedical named entity recognition with neural networks

2018 ◽  
Vol 34 (23) ◽  
pp. 4087-4094 ◽  
Author(s):  
John M Giorgi ◽  
Gary D Bader
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Biomedical Named Entity Recognition

Dataset-aware multi-task learning approaches for biomedical named entity recognition

2020 ◽  
Vol 36 (15) ◽  
pp. 4331-4338
Author(s):  
Mei Zuo ◽  
Yang Zhang
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Quality Data ◽  
Supplementary Information ◽  
Named Entity ◽  
Task Learning ◽  
Biomedical Named Entity Recognition

Abstract Motivation Named entity recognition is a critical and fundamental task for biomedical text mining. Recently, researchers have focused on exploiting deep neural networks for biomedical named entity recognition (Bio-NER). The performance of deep neural networks on a single dataset mostly depends on data quality and quantity while high-quality data tends to be limited in size. To alleviate task-specific data limitation, some studies explored the multi-task learning (MTL) for Bio-NER and achieved state-of-the-art performance. However, these MTL methods did not make full use of information from various datasets of Bio-NER. The performance of state-of-the-art MTL method was significantly limited by the number of training datasets. Results We propose two dataset-aware MTL approaches for Bio-NER which jointly train all models for numerous Bio-NER datasets, thus each of these models could discriminatively exploit information from all of related training datasets. Both of our two approaches achieve substantially better performance compared with the state-of-the-art MTL method on 14 out of 15 Bio-NER datasets. Furthermore, we implemented our approaches by incorporating Bio-NER and biomedical part-of-speech (POS) tagging datasets. The results verify Bio-NER and POS can significantly enhance one another. Availability and implementation Our source code is available at https://github.com/zmmzGitHub/MTL-BC-LBC-BioNER and all datasets are publicly available at https://github.com/cambridgeltl/MTL-Bioinformatics-2016. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Combining Multi-task Learning with Transfer Learning for Biomedical Named Entity Recognition

2020 ◽  
Vol 176 ◽  
pp. 848-857 ◽  
Author(s):  
Tahir Mehmood ◽  
Alfonso E. Gerevini ◽  
Alberto Lavelli ◽  
Ivan Serina
Keyword(s):  
Transfer Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Task Learning ◽  
Biomedical Named Entity Recognition

scholarly journals Transfer Learning for Arabic Named Entity Recognition With Deep Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 37736-37745 ◽  
Author(s):  
Mohammad Al-Smadi ◽  
Saad Al-Zboon ◽  
Yaser Jararweh ◽  
Patrick Juola
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Deep Neural Networks ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity
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