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组装预训练的语言模型,从文献中提取生物医学知识

Zhao Li¹, Qiang Wei¹, Liang-Chin Huang¹

¹McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, United States.

Journal of the American Medical Informatics Association : JAMIA

|March 23, 2024

概括

一个新的自然语言处理 (NLP) 系统在生物医学命名实体识别 (NER) 和关系提取 (RE) 中取得了顶级排名,超过了大型语言模型. 特定任务模型在生物医学文本挖掘方面表现出卓越的性能.

科学领域:

生物医学信息学生物医学信息学
计算语言学计算语言学
自然语言处理 (NLP) 是一种自然语言处理.

背景情况:

越来越多的生物医学文献需要自动化方法来提取概念之间的关系.
开发强大的NLP技术对于建立知识库和识别研究差距至关重要.

研究的目的:

为LitCoin挑战开发和评估一个NLP系统,专注于命名实体识别 (NER) 和关系提取 (RE).
使用手动注释的语料库对NLP方法进行基准测试.

主要方法:

组合学习结合了BioBERT,PubMedBERT和NER的BioM-ELECTRA. 这是一个非常好的方法.
一种基于规则的细胞系和分类学检测方法.
微调T0pp模型用于关系提取,结合实体位置信息.

主要成果:

在NER中获得第一名,在LitCoin挑战中在关系提取和新性预测中获得第二名.
微调模型在零射击设置中显著优于通用大型语言模型,如ChatGPT 3.5和4.

结论:

开发的NLP系统在生物医学实体的NER和RE中表现出高效率.
针对特定任务的NLP模型与用于专门的生物医学文本分析的通用大型语言模型相比,提供了更高的性能.

关键词:

组合学习组合学习知识基础知识基础大型语言模型命名实体的认可命名实体的认可关系提取关系提取

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Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.