  This paper presents a publicly available corpus of French encyclopedic
history texts annotated according to the Berkeley FrameNet formalism. The main
difference in our approach compared to previous works on semantic parsing with
FrameNet is that we are not interested here in full text parsing but rather on
partial parsing. The goal is to select from the FrameNet resources the minimal
set of frames that are going to be useful for the applicative framework
targeted, in our case Information Extraction from encyclopedic documents. Such
an approach leverages the manual annotation of larger corpora than those
obtained through full text parsing and therefore opens the door to alternative
methods for Frame parsing than those used so far on the FrameNet 1.5 benchmark
corpus. The approaches compared in this study rely on an integrated sequence
labeling model which jointly optimizes frame identification and semantic role
segmentation and identification. The models compared are CRFs and multitasks
bi-LSTMs.
