Unsupervised Text Mining for Ontology Extraction: An Evaluation of Statistical Measures

Abstract

We report on a comparative evaluation carried out in the field of unsupervised text mining. We have worked on a parsed medical corpus, on which we have used different statistical measures. Using those measures, we rate the verb-object dependencies and we select the most reliable ones according to each measure. We then apply pattern matching and clustering algorithms to the classes of dependencies in order to build sets of semantically related words and establish semantic links between them. Finally, we evaluate the impact of the statistical measures used for the initial selection of the dependencies on the quality of the results.

Resources

Details