Combining syntactic and semantic vector space models in the health domain by using a clustering ensemble

The adoption of services for automatic information management is one of the most interesting open problems in various professional and social fields. We focus on the health domain characterized by the production of huge amount of documents, in which the adoption of innovative systems for information management can significantly improve the tasks performed by the actors involved and the quality of the health services offered. In this work we propose a methodology for automatic documents categorization based on the adoption of unsupervised learning techniques. We extracted both semantic and syntactic features in order to define the vector space models and proposed the use of a clustering ensemble in order to increase the discriminative power of our approach. Results on real medical records, digitalized by means of a state-of-the-art OCR technique, demonstrated the effectiveness of the proposed approach.