解析
计算机科学
管道(软件)
信息抽取
人工智能
雪球取样
概化理论
判决
自动汇总
精确性和召回率
自然语言处理
机器学习
召回
雪球土
数据挖掘
程序设计语言
医学
语言学
统计
哲学
数学
病理
地貌学
冰期
地质学
作者
Qingyang Dong,Jacqueline M. Cole
标识
DOI:10.1021/acs.jcim.3c01281
摘要
The ever-growing amount of chemical data found in the scientific literature has led to the emergence of data-driven materials discovery. The first step in the pipeline, to automatically extract chemical information from plain text, has been driven by the development of software toolkits such as ChemDataExtractor. Such data extraction processes have created a demand for parsers that efficiently enable text mining. Here, we present Snowball 2.0, a sentence parser based on a semisupervised machine-learning algorithm. It can be used to extract any chemical property without additional training. We validate its precision, recall, and F-score by training and testing a model with sentences of semiconductor band gap information curated from journal articles. Snowball 2.0 builds on two previously developed Snowball algorithms. Evaluation of Snowball 2.0 shows a 15–20% increase in recall with marginally reduced precision over the previous version which has been incorporated into ChemDataExtractor 2.0, giving Snowball 2.0 better performance in most configurations. Snowball 2.0 offers more and better parsing options for ChemDataExtractor, and it is more capable in the pipeline of automated data extraction. Snowball 2.0 also features better generalizability, performance, learning efficiencies, and user-friendliness.
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