Computational semantics of time/negation interaction
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
MedPost: a part-of-speech tagger for bioMedical text
Bioinformatics
Contrast and variability in gene names
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Specializing for predicting obesity and its co-morbidities
Journal of Biomedical Informatics
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
UCM-I: a rule-based syntactic approach for resolving the scope of negation
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
UMichigan: a conditional random field model for resolving the scope of negation
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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Expressions of negation in the biomedical literature often encode information of contrast as a means for explaining significant differences between the objects that are so contrasted. We show that such information gives additional insights into the nature of the structures and/or biological functions of these objects, leading to valuable knowledge for subcategorization of protein families by the properties that the involved proteins do not have in common. Based on the observation that the expressions of negation employ mostly predictable syntactic structures that can be characterized by subclausal coordination and by clause-level parallelism, we present a system that extracts such contrastive information by identifying those syntactic structures with natural language processing techniques and with additional linguistic resources for semantics. The implemented system shows the performance of 85.7% precision and 61.5% recall, including 7.7% partial recall, or an F score of 76.6. We apply the system to the biological interactions as extracted by our biomedical information-extraction system in order to enrich proteome databases with contrastive information.