Chinese Clinical Named Entity Recognition With Segmentation Synonym Sentence Synthesis Mechanism: Algorithm...

Area of Science:

• Natural Language Processing
• Machine Learning
• Bioinformatics

Background:

• Clinical Named Entity Recognition (CNER) is crucial for extracting information from electronic medical records.
• Deep learning models are prevalent in CNER but often require large annotated datasets and extensive dictionaries.
• Existing CNER methods face challenges due to text complexity, entity diversity, and boundary ambiguity.

Purpose of the Study:

• To address data scarcity and labeling difficulties in CNER tasks.
• To propose a dataset augmentation algorithm that leverages existing knowledge without manual dictionary expansion.
• To improve the generalization performance of CNER models.

Main Methods:

• Developed the Segmentation Synonym Sentence Synthesis (SSSS) algorithm using proximity word calculation and lexical segmentation.
• Recombined synonymous vocabulary from natural language data to expand the dataset.
• Applied the SSSS algorithm to RoBERTa + CRF and RoBERTa + BiLSTM + CRF models, evaluating on CCKS 2017 and 2019 datasets.

Main Results:

• SSSS + RoBERTa + CRF achieved an F1-score of 91.30% on the CCKS-2017 dataset.
• SSSS + RoBERTa + BiLSTM + CRF achieved an F1-score of 91.35% on the CCKS-2017 dataset.
• Both models achieved F1-scores above 83% on the CCKS-2019 dataset.

Conclusions:

• The SSSS algorithm effectively expands CNER datasets.
• The proposed method significantly improves CNER model performance.
• This approach successfully mitigates challenges related to data acquisition, annotation, and model generalization.