Confronting the challenges of annotating naturally occurring text into a semantically structured form to facilitate automatic information extraction, current Semantic Role Labeling (SRL) systems have been specifically examining a semantic predicate-argument structure. Based on the Concept Description Language for Natural Language (CDL.nl) which is intended to describe the concept structure of text using a set of pre-defined semantic relations, we develop a parser to add a new layer of semantic annotation of natural language sentences as an extension of SRL. The parsing task is a relation extraction process with two steps: relation detection and relation classification. We advance a hybrid approach using different methods for two steps: first, based on dependency analysis, a rule-based method is presented to detect all entity pairs between each pair for which there exists a relationship; secondly, we use a feature-based method to assign a CDL.nl relation to each detected entity pair using Support Vector Machine. We report the preliminary experimental results carried out on our manual dataset annotated with CDL.nl relations.