Semantic Frame Parsing for Information Extraction : the CALOR corpus
This work addresses information extraction from encyclopedic documents, offering an incremental approach by adapting FrameNet for partial parsing in a specific domain.
The paper tackles the problem of semantic frame parsing for information extraction by introducing a French corpus annotated with FrameNet and focusing on partial parsing to select minimal frames, enabling manual annotation of larger corpora and alternative parsing methods.
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.