What matters in a transferable neural network model for relation classification in the biomedical domain?

Area of Science:

• Biomedical informatics
• Machine learning
• Natural language processing

Background:

• Limited labeled data hinders advanced machine learning in real-world applications, especially in the biomedical and clinical domains.
• Transfer learning (TL) offers a solution by utilizing knowledge from source tasks with ample data to improve performance on target tasks.

Purpose of the Study:

• To present and evaluate novel recurrent neural network models for transfer learning in relation classification tasks.
• To systematically investigate how source data characteristics (task similarity, data size) affect the effectiveness of different TL frameworks.

Main Methods:

• Developed two unified recurrent neural models.
• Implemented three distinct transfer learning frameworks for relation classification.
• Conducted empirical studies varying source and target task characteristics.

Main Results:

• The proposed transfer learning frameworks generally enhance model performance for relation classification.
• Improvements are contingent upon the specific characteristics and relatedness of the source and target tasks.
• Empirical results demonstrate a clear dependence of TL effectiveness on task-specific factors.

Conclusions:

• The developed transfer learning frameworks show promise for overcoming data scarcity in biomedical NLP.
• The choice of the optimal TL framework is determined by the interplay between source and target task properties.
• Further research can refine TL strategies based on these task-dependent findings.