Sources of Complexity in Semantic Frame Parsing for Information Extraction

Abstract : 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.
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Contributor : Gabriel Marzinotto <>
Submitted on : Thursday, December 20, 2018 - 4:47:35 PM
Last modification on : Sunday, December 23, 2018 - 1:24:03 AM


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  • HAL Id : hal-01731385, version 2
  • ARXIV : 1812.09193



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-01731385v2⟩



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