Analyzing the Effect of Multi-task Learning for Biomedical Named Entity Recognition
This work addresses the challenge of limited annotated data in the biomedical domain, which is incremental as it builds on existing methods.
The study tackled the problem of low-resource biomedical named entity recognition by analyzing transferability between datasets and proposing a combination of transfer and multi-task learning, resulting in improved performance for entity recognition systems.
Developing high-performing systems for detecting biomedical named entities has major implications. State-of-the-art deep-learning based solutions for entity recognition often require large annotated datasets, which is not available in the biomedical domain. Transfer learning and multi-task learning have been shown to improve performance for low-resource domains. However, the applications of these methods are relatively scarce in the biomedical domain, and a theoretical understanding of why these methods improve the performance is lacking. In this study, we performed an extensive analysis to understand the transferability between different biomedical entity datasets. We found useful measures to predict transferability between these datasets. Besides, we propose combining transfer learning and multi-task learning to improve the performance of biomedical named entity recognition systems, which is not applied before to the best of our knowledge.