| 融合关系标签和位置信息的中文医疗文本因果关系抽取方法研究 |
| 修订日期：2023-10-27 点此下载全文 |
| 引用本文：张维宁,申喜凤,李美婷,等.融合关系标签和位置信息的中文医疗文本因果关系抽取方法研究[J].医学信息学杂志,2024,45(1):21-26 |
| 摘要点击次数: |
| 全文下载次数: |
|
| 基金项目:科技创新2030——“新一代人工智能”重大项目(项目编号:2020AAA0104905)。 |
|
| 中文摘要:目的/意义 利用因果关系词相对位置辅助深度学习模型,提高因果关系预测能力,挖掘医疗文本增益信息。方法/过程 将医疗文本因果关系词相对位置信息表示为关系特征层嵌入预训练语言模型,融合基线模型进行实体识别及关系抽取。结果/结论 嵌入关系特征层的模型F1值较基线模型BERT-BiLSTM-CRF和CasRel分别提升2.92个百分点和6.41个百分点,因果关系预测能力较好。 |
| 中文关键词:自然语言处理 因果关系抽取 预训练模型 BERT 医疗文本 |
| |
| Study on the Method of Causality Extraction from Chinese Medical Texts by Integrating Relational Label and Location Information |
|
|
| Abstract:Purpose/Significance The relative positions of causality words are utilized to assist deep learning models to improve causality prediction and mine medical text gain information.Method/Process The relative position information of causality words in medical texts is represented as a relational feature layer embedded in a pre-trained language model, and the baseline model is integrated for entity recognition and relationship extraction.Result/Conclusion The F1 value of the model embedded in the relational feature layer is improved by 2.92 percentage points and 6.41 percentage points compared with the baseline models BERT-BiLSTM-CRF and CasRel, respectively, with better causal prediction capacity. |
| keywords:natural language processing causality extraction pre-training model BERT medical text |
| 查看全文 查看/发表评论 下载PDF阅读器 |
