论文标题
分析多任务学习对生物医学命名实体识别的影响
Analyzing the Effect of Multi-task Learning for Biomedical Named Entity Recognition
论文作者
论文摘要
开发用于检测生物医学命名实体的高性能系统具有重大影响。实体识别的基于最新的深度学习解决方案通常需要大量注释的数据集，这在生物医学领域不可用。已显示转移学习和多任务学习可以提高低资源域的性能。但是，这些方法的应用在生物医学领域中相对较少，并且对这些方法的理论理解缺乏改善性能。在这项研究中，我们进行了广泛的分析，以了解不同生物医学实体数据集之间的可传递性。我们发现了有用的措施来预测这些数据集之间的可传递性。此外，我们建议将转移学习和多任务学习结合起来，以提高生物医学命名实体识别系统的性能，这是我们最佳知识之前没有应用的。
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.
