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Snowball 2.0: Generic Material Data Parser for ChemDataExtractor.

Published version
Peer-reviewed

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Abstract

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.

Description

Publication status: Published

Keywords

Software, Algorithms, Language, Data Mining, Supervised Machine Learning

Journal Title

J Chem Inf Model

Conference Name

Journal ISSN

1549-9596
1549-960X

Volume Title

63

Publisher

American Chemical Society (ACS)
Sponsorship
BASF (NA)
Royal Academy of Engineering (RCSRF1819\7\10)
ISIS Neutron and Muon Source (NA)