Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Information extraction for enhanced access to disease outbreak reports
Journal of Biomedical Informatics - Special issue: Sublanguage
Complexity of event structure in IE scenarios
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Confidence estimation for information extraction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Building support tools for Russian-language information extraction
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
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This paper presents ongoing work on application of Information Extraction (IE) technology to domain of Public Health, in a real-world scenario. A central issue in IE is the quality of the results. We present two novel points. First, we distinguish the criteria for quality: the objective criteria that measure correctness of the system's analysis in traditional terms (F-measure, recall and precision), and, on the other hand, subjective criteria that measure the utility of the results to the end-user. Second, to obtain measures of utility, we build an environment that allows users to interact with the system by rating the analyzed content. We then build and compare several classifiers that learn from the user's responses to predict the relevance scores for new events. We conduct experiments with learning to predict relevance, and discuss the results and their implications for text mining in the domain of Public Health.