半监督命名实体识别用于大量糖尿病数据的SNER
IEEE journal of biomedical and health informatics
|June 11, 2024
概括
本研究介绍了SNER,这是一种用于糖尿病数据中命名实体识别 (NER) 的新型半监督学习方法. SNER有效地解决了诸如大数据量和有限的标记数据集等挑战,改善了糖尿病信息提取.
科学领域:
- 计算语言学计算语言学
- 医疗信息学医学信息学
- 医疗保健中的人工智能
背景情况:
- 糖尿病的预防和治疗依赖于医学文献和记录.
- 从糖尿病文本数据中提取实体至关重要,但具有挑战性.
- 现有的命名实体识别 (NER) 方法对于专门的糖尿病数据是不够的.
研究的目的:
- 提出一种新的半监督学习方法,SNER,以获得糖尿病数据中的有效NER.
- 为了应对大量数据,缺乏标记数据以及糖尿病文本处理中的高手工标签成本的挑战.
主要方法:
- 开发了SNER,一种名为实体识别的糖尿病半监督学习方法.
- 利用大型未标记的数据集来克服标记数据的稀缺性.
- 通过使用信心和不确定性得分来过预测的标签,创建正面和负面的伪标签,并战略性地使用负面的伪标签.
主要成果:
- SNER有效地处理大量与糖尿病相关的文本数据.
- 该方法在两个公共糖尿病数据集上表现出优越的性能,与现有的最先进的模型相比.
- SNER成功地缓解了与有限的标签数据和手动注释成本相关的问题.
结论:
- 在具有挑战性的糖尿病数据领域,SNER为命名实体识别提供了有效的解决方案.
- 半监督学习,特别是拟议的伪标签策略,显著提高了专业医疗文本的NER性能.
- 开发的方法有望改善从糖尿病文献和记录中提取和利用关键信息.
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