MMBERT:生物医学命名实体识别的统一框架

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Lei Fu¹, Zuquan Weng^2,3, Jiheng Zhang^4,5

¹College of Electromechanical and Information Engineering, PuTian University, PuTian, 351100, Fujian Province, China.

Medical & biological engineering & computing

|October 13, 2023

概括

本研究介绍了MMBERT,这是一种用于生物医学命名实体识别 (NER) 的新框架,可以克服数据稀缺性和实体复杂性. 该模型在基准数据集上实现了最先进的性能.

科学领域:

自然语言处理 (NLP) 是一种自然语言处理.
生物医学信息学生物医学信息学
机器学习机器学习

背景情况:

生物医学命名实体识别 (NER) 是至关重要的,但由于有限的数据集和复杂的实体,具有挑战性.
现有的方法与生物医学文本的独特特征作斗争.
有注释的生物医学NER数据集的稀缺性阻碍了模型开发.

研究的目的:

提出一个新的框架,MMBERT,用于增强生物医学NER.
解决生物医学NER中数据稀缺性和实体复杂性的挑战.
提高生物医学NER模型的概括能力和预测准确度.

主要方法:

介绍了ERNIE-Health,这是一个在生物医疗机构中预先训练的中文语言表示模型.
使用BERT和CW-LSTM用于单词对关系的联合特征向量.
采用多颗粒度的2D卷积来改进单词对表示和关系.
设计了一个卷积神经网络 (CNN) 和共同预测器,以提高概括性和准确性.

主要成果:

与几个基线模型相比,拟议的MMBERT框架实现了更高的性能.
在三个基准数据集上进行了实验,证明了模型的有效性.
该模型成功地解决了数据稀缺性和实体复杂性的挑战.

结论:

MMBERT代表了生物医学NER的重大进步.
整合ERNIE-Health和先进的深度学习技术可以提高模型的性能.
该框架为从生物医学文本中提取有价值的信息提供了一个强大的解决方案.

关键词:

卷积神经网络是一种卷积神经网络.命名实体认可命名实体认可自然语言处理自然语言处理.变压器变压器变压器

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