Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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When automatically mining narrative clinical notes to extract meaningful information, such as medical problems, it is essential to take into account the context of this information. For instance, determining whether medical conditions are negated or not is key information for accurately processing medical reports. This article presents an experiment in adapting the state-of-art NegEx algorithm (Chapman et al.) to the French language and evaluating both algorithms (the original English algorithm and the derived French version) on two clinical corpora (English and French, respectively) annotated for medical problems and their negation status. NegEx is a rule-based algorithm which detects negations of medical problems in English-language medical texts, by looking for specific negation trigger phrases in the context of the medical concepts. Our approach has consisted in designing a new list of trigger phrases in French, by studying examples extracted from French clinical notes and relying on the original English list. We performed an evaluation of the negation detection in both corpora. This study show that the two systems achieve comparable results and good performance (respectively 0.839 and 0.867 F-measure for NegEx and its French adaptation).