ChemicalTagger: A tool for semantic text-mining in chemistry

Background The primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured li...

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Bibliographic Details
Published in:	Journal of cheminformatics 2011-05, Vol.3 (1), p.17-17, Article 17
Main Authors:	Hawizy, Lezan, Jessop, David M, Adams, Nico, Murray-Rust, Peter
Format:	Article
Language:	eng
Subjects:	Chemistry Chemistry and Materials Science Computational Biology/Bioinformatics Computer Applications in Chemistry Data mining Documentation and Information in Chemistry RDF technologies in chemistry Research Article Theoretical and Computational Chemistry
Online Access:	Get full text
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Summary:	Background The primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches. Results We have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification ( Action names). Conclusions It is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision.
ISSN:	1758-2946 1758-2946