Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations
arxiv(2024)
摘要
In acupuncture therapy, the accurate location of acupoints is essential for
its effectiveness. The advanced language understanding capabilities of large
language models (LLMs) like Generative Pre-trained Transformers (GPT) present a
significant opportunity for extracting relations related to acupoint locations
from textual knowledge sources. This study aims to compare the performance of
GPT with traditional deep learning models (Long Short-Term Memory (LSTM) and
Bidirectional Encoder Representations from Transformers for Biomedical Text
Mining (BioBERT)) in extracting acupoint-related location relations and assess
the impact of pretraining and fine-tuning on GPT's performance. We utilized the
World Health Organization Standard Acupuncture Point Locations in the Western
Pacific Region (WHO Standard) as our corpus, which consists of descriptions of
361 acupoints. Five types of relations ('direction_of,' 'distance_of,'
'part_of,' 'near_acupoint,' and 'located_near') (n= 3,174) between acupoints
were annotated. Five models were compared: BioBERT, LSTM, pre-trained GPT-3.5,
and fine-tuned GPT-3.5, as well as pre-trained GPT-4. Performance metrics
included micro-average exact match precision, recall, and F1 scores. Our
results demonstrate that fine-tuned GPT-3.5 consistently outperformed other
models in F1 scores across all relation types. Overall, it achieved the highest
micro-average F1 score of 0.92. This study underscores the effectiveness of
LLMs like GPT in extracting relations related to acupoint locations, with
implications for accurately modeling acupuncture knowledge and promoting
standard implementation in acupuncture training and practice. The findings also
contribute to advancing informatics applications in traditional and
complementary medicine, showcasing the potential of LLMs in natural language
processing.
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