Macrosyntactic Segmenters of a French spoken corpus

• Main Authors: Wang, Ilaine, Kahane, Sylvain, Tellier, Isabelle
• Other Authors: Modèles, Dynamiques, Corpus (MoDyCo), Université Paris Nanterre (UPN)-Centre National de la Recherche Scientifique (CNRS), Lattice - Langues, Textes, Traitements informatiques, Cognition - UMR 8094 (Lattice), Université Sorbonne Nouvelle - Paris 3-Université Sorbonne Paris Cité (USPC)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Sciences et Lettres (PSL)-Département Littératures et langage - ENS Paris (LILA), École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2014
• Subjects: sentence boundary segmentation; spoken French; machine learning; segmentation en phrase; français parlé; apprentissage automatique; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; Iceland; Reykjavík
• Online Access: https://shs.hal.science/halshs-01004668; https://shs.hal.science/halshs-01004668/document; https://shs.hal.science/halshs-01004668/file/lrec2014wang.pdf

International audience The aim of this paper is to describe an automated process to segment spoken French transcribed data into macrosyntactic units. While sentences are delimited by punctuation marks for written data, there is no obvious hint nor limit to major units for speech. As a reference, we used the manual annotation of macrosyntactic units based on illocutionary as well as syntactic criteria and developed for the Rhapsodie corpus, a 33.000 words prosodic and syntactic treebank. Our segmenters were built using machine learning methods as supervised classifiers~: segmentation is about identifying the boundaries of units, which amounts to classifying each interword space. We trained six different models on Rhapsodie using different sets of features, including prosodic and morphosyntactic cues, on the assumption that their combination would be relevant for the task. Both types of cues could be resulting either from manual annotation/correction or from fully automated processes, which comparison might help determine the cost of manual effort, especially for the 3M words of spoken French of the Orfeo project those experiments are contributing to.