Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Revisiting Ontology Design: A Methodology Based on Corpus Analysis
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
Modeling a description logic vocabulary for cancer research
Journal of Biomedical Informatics
Extracting Semantic Frames from Thai Medical-Symptom Phrases with Unknown Boundaries
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
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In this paper, we present the results of experiments identifying the drug treatment relation and drug treatment attributes like dosage, treatment frequency and duration from abstracts of medical publications using linguistic patterns. The approach uses an automatic linguistic pattern construction algorithm after the dataset has been semantically annotated. The automatically constructed patterns were able to identify treatment relations and their attributes with varying success. We observe that the simple (or naïve) treatment patterns performs much better than the non-naïve treatment patterns in identifying sentences with drug treatment relationship in both cancer and non-cancer drug therapy domain. However the drug dosage, frequency and duration patterns performed much better in the identification of relationships in the cancer drug therapy domain than the non-cancer drug therapy domain.