We recognize the necessity of having and using manually-constructed semantic-knowledge sources in order to get precise assessments of the semantic similarity of texts, but, in turn, we also consider that it is possible to obtain good estimations of these similarities using less-expensive, and perhaps broader, information sources.
our proposal is to automatically extract the semantic knowledge from large amounts of raw data samples i.e.