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Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
Published on: June 6, 2025
ChemicalTagger: A tool for semantic text-mining in chemistry
Lezan Hawizy1, David M Jessop, Nico Adams
1Unilever Centre for Molecular Science Informatics, Department of Chemistry, Lensfield Road, Cambridge, CB2 1EW, UK. lh359@cam.ac.uk.
Journal of Cheminformatics
|May 18, 2011
Summary
We developed ChemicalTagger, a natural language processing (NLP) tool, to extract data from chemical texts. This method accurately parses chemical experimental language, enabling efficient information retrieval from scientific literature.
Area of Science:
- Chemistry
- Computational Chemistry
- Natural Language Processing
Background:
- Scientific communication relies on unstructured text in publications.
- Data extraction from chemical literature is valuable.
- The formulaic nature of chemistry writing suits NLP approaches.
Purpose of the Study:
- To demonstrate data extraction from unstructured chemical literature using NLP.
- To develop a tool for parsing chemical experimental text.
Main Methods:
- Developed ChemicalTagger, a phrase-based semantic NLP tool.
- Utilized a modular architecture with OSCAR, domain-specific regex, and English taggers.
- Employed ANTLR grammar for structuring phrases into trees.
Main Results:
- Achieved 88.9% agreement for phrase recognition.
- Achieved 91.9% agreement for phrase-type identification (Action names).
- ChemicalTagger achieved >99.5% precision in identifying solvents from linguistic context.
Conclusions:
- Rule-based techniques and formal grammar parsing can parse chemical experimental text.
- ChemicalTagger has been successfully deployed on over 10,000 patents.
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