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

Understand

Motivation: State-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.
• Results: We 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.