Annotation of chemical named entities

Authors:
Peter Corbett;Colin Batchelor;Simone Teufel
Affiliations:
Cambridge University, Cambridge, UK;Thomas Graham House, Cambridge, UK;University of Cambridge, UK
Venue:
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Year:
2007

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Abstract

We describe the annotation of chemical named entities in scientific text. A set of annotation guidelines defines 5 types of named entities, and provides instructions for the resolution of special cases. A corpus of fulltext chemistry papers was annotated, with an inter-annotator agreement F score of 93%. An investigation of named entity recognition using LingPipe suggests that F scores of 63% are possible without customisation, and scores of 74% are possible with the addition of custom tokenisation and the use of dictionaries.