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.
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Corbett et al. (2007) studied this question.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: