Research on entity relation extraction for Chinese medical text.

Area of Science:

• Natural Language Processing
• Information Extraction
• Computational Linguistics

Background:

• Overlapping relations and cascading errors are key challenges in entity relation extraction.
• CasRel and TPLinker are competitive models for addressing these challenges.
• Chinese medical text presents unique characteristics for relation extraction.

Purpose of the Study:

• To evaluate CasRel and TPLinker for entity relation extraction in Chinese medical text.
• To enhance model performance using domain-specific pre-trained models.
• To identify the optimal model combination for this task.

Main Methods:

• Utilized the CMeIE dataset for evaluating CasRel and TPLinker.
• Integrated advanced pre-trained models tailored for Chinese medical text.
• Compared performance against benchmark and state-of-the-art models.

Main Results:

• TPLinker demonstrated a more significant and consistent performance boost than CasRel.
• Advanced pre-trained models notably improved TPLinker's performance.
• The MacBERT + TPLinker combination achieved state-of-the-art results, outperforming ERNIE-Health 3.0.

Conclusions:

• The MacBERT + TPLinker model combination is highly effective for entity relation extraction in Chinese medical text.
• Domain-specific pre-trained models are crucial for advancing performance in specialized NLP tasks.
• This approach offers a robust solution for handling complex relations in medical corpora.