Unsupervised information extraction from italian clinical records

Abstract

This paper discusses the application of an unsupervised text mining technique for the extraction of information from clinical records in Italian. The approach includes two steps. First of all, a metathesaurus is exploited together with natural language processing tools to extract the domain entities. Then, clustering is applied to explore relations between entity pairs. The results of a preliminary experiment, performed on the text extracted from 57 medical records containing more than 20,000 potential relations, show how the clustering should be based on the cosine similarity distance rather than the City Block or Hamming ones.