In this article we present an ongoing work for extracting conceptual information from specialized-domain texts. Concepts are forms of dividing the world in classes and they are the fundamental pieces for constructing ontologies. In this sense, ontology learning is the (semi-) automatic support for constructing an ontology. Input data are required for the ontology learning and this data are the basic source from which to learn the relevant concepts for a domain, their definitions as well the relations holding between them. With this necessity in mind, we propose here a methodology that takes into account the level of synthetic judgements and word relevance in a sentence in order to filter out and rank sentences. Sentences with high relevance and low level of synthetic judgements should have at least a predicative verb characteristic of analytical definitions for being good candidates.