Sources of Complexity in Semantic Frame Parsing for Information Extraction

Gabriel Marzinotto (1) , Frédéric Béchet (2, 3) , Géraldine Damnati (1) , Alexis Nasr (2, 3)

1 Orange Labs [Lannion]
2 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)
3 TALEP - Traitement Automatique du Langage Ecrit et Parlé

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.

Mots clés: Frame Semantic Parsing; LSTM; Information Extraction

Domaines: Traitement du texte et du document

Gabriel Marzinotto, Frédéric Béchet, Géraldine Damnati, Alexis Nasr. Sources of Complexity in Semantic Frame Parsing for Information Extraction. International FrameNet Workshop 2018, May 2018, Miyazaki, Japan. ⟨hal-01731385v1⟩