Addressing the challenges of capturing the correlation between information extraction tasks and the contextual information in doctor-patient dialogues during electronic medical record construction, we propose a Transformer-based collaborative information extraction method called CT-JIE (Collaborative Transformer for Joint Information Extraction) . Firstly, this method utilizes a sliding window combined with Bi-LSTM to acquire historical information from the dialogues and employs a label-aware module to capture task-related information in the dialogue context. Secondly, the global attention module enhances the model's ability to perceive the context of symptom entities and their status. Finally, the interactive guidance module explicitly models the interaction among intent recognition, slot filling, and status recognition tasks to capture the complex contexts and relationships among multiple tasks. Experiments demonstrate that this method outperforms other baseline and ablation models on the IMCS21 and CMDD datasets, showing strong generalization ability and performance advantages in handling joint information extraction tasks.

[2024-03-01] Accepted Paper /doc/2023.12.0591/v1