How to be a discourse particle?
| Autor: | Mathilde Dargnat, Katarina Bartkova, Alice Bastien |
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| Přispěvatelé: | Bartkova, Katarina, Analyse et Traitement Informatique de la Langue Française (ATILF), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Université de Lorraine (UL), Boston University |
| Rok vydání: | 2016 |
| Předmět: |
060201 languages & linguistics
Pronoun Computer science business.industry auto-matic classification and clustering 06 humanities and the arts Adverb computer.software_genre [SHS.LANGUE] Humanities and Social Sciences/Linguistics Expression (mathematics) Identification (information) Categorization discourse particles 0602 languages and literature Artificial intelligence [SHS.LANGUE]Humanities and Social Sciences/Linguistics business Cluster analysis Adjective computer Sentence Natural language processing prosodic parameters |
| Zdroj: | Speech Prosody 2016 Speech Prosody 2016, Boston University, May 2016, Boston, MA, United States |
| DOI: | 10.21437/speechprosody.2016-176 |
| Popis: | International audience; Our study analyses some prosodic correlates of nine French words or expressions: alors, quoi, voilà, bon, ben, tu sais, vous savez, tu vois, vous voyez. Besides their general grammatical categorization as adverb, pronoun, preposition, 'introducer', adjective, adverb and sentence, these expressions are very frequently used as discourse particles (DP) in spontaneous speech. Our goal is to determine to what extent intrinsic and contextual prosodic properties are useful and sufficient to characterize their DP and non-DP functions. The expressions under study are extracted from large corpora, than a manual annotation is carried out to distinguish DP and non-DP functions and an automatic processing is applied for prosodic data extraction and labelling. This allows getting fine-grained and systematic pro-sodic information. Automatic classification tests of the DP functions based solely on prosodic parameters are carried out and lead to very encouraging results as correct identification ranges from 73% to more than 90%. Finally an automatic clustering procedure provides prosodically significant DP sub-classes for every studied expression. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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