  The work presented in this master thesis consists of extracting a set of
events from texts written in natural language. For this purpose, we have based
ourselves on the basic notions of the information extraction as well as the
open information extraction. First, we applied an open information
extraction(OIE) system for the relationship extraction, to highlight the
importance of OIEs in event extraction, and we used the ontology to the event
modeling. We tested the results of our approach with test metrics. As a result,
the two-level event extraction approach has shown good performance results but
requires a lot of expert intervention in the construction of classifiers and
this will take time. In this context we have proposed an approach that reduces
the expert intervention in the relation extraction, the recognition of entities
and the reasoning which are automatic and based on techniques of adaptation and
correspondence. Finally, to prove the relevance of the extracted results, we
conducted a set of experiments using different test metrics as well as a
comparative study.
