Dataset-aware multi-task learning approaches for biomedical named entity recognition.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Aug 1, 2020
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.
