ChemicalTagger: A tool for semantic text-mining in chemistry
Authors
Hawizy, Lezan
Jessop, David
Adams, Nico
Murray-Rust, Peter
Publication Date
2011-05-16ISSN
0065-7727
Language
English
Type
Article
Metadata
Show full item recordCitation
Hawizy, L., Jessop, D., Adams, N., & Murray-Rust, P. (2011). ChemicalTagger: A tool for semantic text-mining in chemistry. https://doi.org/10.1186/1758-2946-3-17
Abstract
AbstractBackgroundThe 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 flowing 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.ResultsWe 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).ConclusionsIt 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.
Identifiers
External DOI: https://doi.org/10.1186/1758-2946-3-17
This record's URL: http://www.dspace.cam.ac.uk/handle/1810/238153
Rights
Rights Holder: Hawizy et al.; licensee BioMed Central Ltd.
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