Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Getting the most out of transition-based dependency parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
DTMBIO 2012: international workshop on data and text mining in biomedical informatics
Proceedings of the 21st ACM international conference on Information and knowledge management
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We explore an information extraction task where the goal is to determine the correct values for fields which are relevant to prescription drug administration such as dosage amount, frequency and route. The data set is a collection of prescriptions from a long-term health-care facility, a small subset of which we have manually annotated with values for these fields. We first examine a rule-based approach to the task, which uses a dependency parse of the prescription, achieving accuracies of 60-95% over various different fields, and 67.5% when all fields of the prescription are considered together. The outputs of such a system have potential applications in detecting irregularities in dosage delivery.