  This paper describes a Semantic Frame parsing System based on sequence
labeling methods, precisely BiLSTM models with highway connections, for
performing information extraction on a corpus of French encyclopedic history
texts annotated according to the Berkeley FrameNet formalism. The approach
proposed in this study 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, to
highlight the factors that make Semantic Frame parsing a difficult task and to
provide detailed evaluations of the performance on different types of frames
and sentences.
