Leveraging Multi-task Learning for Biomedical Named Entity Recognition

2019 ◽  
pp. 431-444 ◽  
Author(s):  
Tahir Mehmood ◽  
Alfonso Gerevini ◽  
Alberto Lavelli ◽  
Ivan Serina
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Task Learning ◽  
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 Cross-type biomedical named entity recognition with deep multi-task learning

2018 ◽  
Vol 35 (10) ◽  
pp. 1745-1752 ◽  
Author(s):  
Xuan Wang ◽  
Yu Zhang ◽  
Xiang Ren ◽  
Yuhao Zhang ◽  
Marinka Zitnik ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Task Learning ◽  
Biomedical Named Entity Recognition ◽  
Cross Type

scholarly journals Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

2018 ◽  
Author(s):  
Xuan Wang ◽  
Yu Zhang ◽  
Xiang Ren ◽  
Yuhao Zhang ◽  
Marinka Zitnik ◽  
...  
Keyword(s):  
State Of The Art ◽  
Named Entity Recognition ◽  
Network Models ◽  
Training Data ◽  
Entity Recognition ◽  
Neural Network Models ◽  
Named Entity ◽  
Task Learning ◽  
Level Information ◽  
Biomedical Named Entity Recognition

AbstractMotivationState-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains limited by the available training data for each entity type.ResultsWe propose a multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them. In experiments on 15 benchmark BioNER datasets, our multi-task model achieves substantially better performance compared with state-of-the-art BioNER systems and baseline neural sequence labeling models. Further analysis shows that the large performance gains come from sharing character- and word-level information among relevant biomedical entities across differently labeled corpora.AvailabilityOur source code is available at https://github.com/yuzhimanhua/[email protected], [email protected].

scholarly journals Hierarchical shared transfer learning for biomedical named entity recognition

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Zhaoying Chai ◽  
Han Jin ◽  
Shenghui Shi ◽  
Siyan Zhan ◽  
Lin Zhuo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Medical Information ◽  
Named Entity Recognition ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Single Task ◽  
Named Entity ◽  
Task Learning ◽  
Biomedical Named Entity Recognition

Abstract Background Biomedical named entity recognition (BioNER) is a basic and important medical information extraction task to extract medical entities with special meaning from medical texts. In recent years, deep learning has become the main research direction of BioNER due to its excellent data-driven context coding ability. However, in BioNER task, deep learning has the problem of poor generalization and instability. Results we propose the hierarchical shared transfer learning, which combines multi-task learning and fine-tuning, and realizes the multi-level information fusion between the underlying entity features and the upper data features. We select 14 datasets containing 4 types of entities for training and evaluate the model. The experimental results showed that the F1-scores of the five gold standard datasets BC5CDR-chemical, BC5CDR-disease, BC2GM, BC4CHEMD, NCBI-disease and LINNAEUS were increased by 0.57, 0.90, 0.42, 0.77, 0.98 and − 2.16 compared to the single-task XLNet-CRF model. BC5CDR-chemical, BC5CDR-disease and BC4CHEMD achieved state-of-the-art results.The reasons why LINNAEUS’s multi-task results are lower than single-task results are discussed at the dataset level. Conclusion Compared with using multi-task learning and fine-tuning alone, the model has more accurate recognition ability of medical entities, and has higher generalization and stability.

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