Semantic Classification of Consumer Health Content
نویسنده
چکیده
Background: While the Semantic Web concept holds considerable promise, it requires that “machine-readable meaning” of Web content be explicitly marked up. Yet accurate and consistent manual annotation of large quantities of consumer health content is infeasible. Objectives: We set out to develop computerized methods for the task. Methods: First, we created a partial taxonomy of consumer health information retrieval needs, based on their question, reason, or purpose for doing health information retrieval. Text-based content materials were then processed to extract words, phrases and concepts, which served as features for classification algorithms. Results: Through 10-fold cross validation, classifiers were successfully trained and evaluated on 3 levels of the taxonomy with accuracy of 92% to 95%. Conclusions: Automated semantic classification of consumer health content is a promising approach to annotate the genre of content for Semantic Web.
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