A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Feature Engineering for Text Classification
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
XRules: an effective structural classifier for XML data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Term identification in the biomedical literature
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Ontological analysis of taxonomic relationships
ER'00 Proceedings of the 19th international conference on Conceptual modeling
Towards role-based filtering of disease outbreak reports
Journal of Biomedical Informatics
An exploratory study of news article clustering for web-based bio-surveillance
Proceedings of the 1st ACM International Health Informatics Symposium
Classifying Vietnamese disease outbreak reports with important sentences and rich features
Proceedings of the Third Symposium on Information and Communication Technology
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This paper investigates the roles of named entities (NE's) in annotated biomedical text classification. In the annotation schema of BioCaster, a text mining system for public health protection, important concepts that reflect information about infectious diseases were conceptually analyzed with a formal ontological methodology. Concepts were classified as Types, while others were identified as being Roles. Types are specified as NE classes and Roles are integrated into NEs as attributes. We focus on the Roles of NEs by extracting and using them in different ways as features in the classifier. Experimental results show that: 1) Roles for each NE greatly helped improve performance of the system, 2) combining information about NE classes with their Roles contribute significantly to the improvement of performance. We discuss in detail the effect of each Role on the accuracy of text classification.