Faculty of Medicine of Sfax, University of Sfax, Tunisia.
Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Tunisia.
J Am Med Inform Assoc. 2021 Mar 1;28(3):668-669. doi: 10.1093/jamia/ocaa314.
This letter discusses the limitations of the use of filters to enhance the accuracy of the extraction of parenthetic abbreviations from scholarly publications and proposes the usage of the parentheses level count algorithm to efficiently extract entities between parentheses from raw texts as well as of machine learning-based supervised classification techniques for the identification of biomedical abbreviations to significantly reduce the removal of acronyms including disallowed punctuations.
这封信讨论了使用过滤器来提高从学术出版物中提取括号缩写的准确性的局限性，并提出使用括号级计数算法从原始文本中有效地提取括号内的实体，以及基于机器学习的监督分类技术来识别生物医学缩写，以显著减少包括不允许的标点符号在内的首字母缩写词的去除。
