  This article presents an automatic frame analysis system evaluated on a
corpus of French encyclopedic history texts annotated according to the FrameNet
formalism. The chosen approach relies on an integrated sequence labeling model
which jointly optimizes frame identification and semantic role segmentation and
identification. The purpose of this study is to analyze the task complexity
from several dimensions. Hence we provide detailed evaluations from a feature
selection point of view and from the data point of view.
