Multitask learning for biomedical named entity recognition with cross-sharing structure

摘要

BackgroundBiomedical named entity recognition (BioNER) is a fundamental and essential task for biomedical literature mining, which affects the performance of downstream tasks. Most BioNER models rely on domain-specific features or hand-crafted rules, but extracting features from massive data requires much time and human efforts. To solve this, neural network models are used to automatically learn features. Recently, multi-task learning has been applied successfully to neural network models of biomedical literature mining. For BioNER models, using multi-task learning makes use of features from multiple datasets and improves the performance of models.