Extraction system of BiLSTM-CRF joint transfer learning

Key Points

• The aim is to develop an effective extraction system using BiLSTM and CRF through joint transfer learning.
• Utilized a BiLSTM architecture for capturing contextual information in sequential data.
• Applied Conditional Random Fields (CRF) for structured output prediction.
• Employed transfer learning techniques to enhance model performance on related tasks.
• Achieved significant improvement in extraction accuracy compared to traditional methods.
• Demonstrated scalability to various datasets, maintaining robust performance.

Abstract