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In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
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Snowball 2.0: Generic Material Data Parser for ChemDataExtractor.

Qingyang Dong1, Jacqueline M Cole1,2

  • 1Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge CB3 0HE, U.K.

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|November 7, 2023
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Summary
This summary is machine-generated.

Snowball 2.0 is a new machine learning sentence parser that improves automated chemical data extraction from scientific literature. It enhances text mining for data-driven materials discovery with better performance and user-friendliness.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Natural Language Processing

Background:

  • The increasing volume of chemical data in scientific literature necessitates automated extraction methods.
  • Software toolkits like ChemDataExtractor facilitate data extraction, creating a need for efficient text mining parsers.

Purpose of the Study:

  • To introduce Snowball 2.0, a semisupervised machine learning sentence parser.
  • To enhance automated chemical information extraction for data-driven materials discovery.

Main Methods:

  • Developed Snowball 2.0, a semisupervised machine learning algorithm for sentence parsing.
  • Validated the parser's performance using semiconductor band gap data from journal articles.
  • Compared Snowball 2.0 against previous versions and integrated it into ChemDataExtractor 2.0.

Main Results:

  • Snowball 2.0 demonstrates a 15-20% increase in recall compared to previous versions.
  • Achieved improved performance in most configurations when integrated into ChemDataExtractor 2.0.
  • Showcased better generalizability, learning efficiencies, and user-friendliness.

Conclusions:

  • Snowball 2.0 offers advanced parsing capabilities for ChemDataExtractor, improving automated data extraction pipelines.
  • The parser enables efficient extraction of chemical properties without additional training.
  • Snowball 2.0 represents a significant advancement in text mining for materials science research.