Using a Machine Learning Model to Assess the Complexity of Stress Systems

• Main Authors: Dinu, L., Ciobanu, Alina Maria, Chitoran, Ioana, Niculae, Vlad
• Other Authors: Computational Linguistics, University of Bucharest (UniBuc), Faculty of Mathematics-Informatics, Centre de Linguistique Inter-langues, de Lexicologie, de Linguistique Anglaise et de Corpus (CLILLAC-ARP (URP_3967)), Université Paris Cité (UPCité), Max Planck Institute for Software Systems (MPI-SWS)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2014
• Subjects: stress prediction; Romanian stress; syllabication; sequence tagging; [SCCO.LING]Cognitive science/Linguistics; Iceland
• Online Access: https://hal.science/hal-01095427; https://hal.science/hal-01095427/document; https://hal.science/hal-01095427/file/LREC_2014_paper.pdf

International audience We address the task of stress prediction as a sequence tagging problem. We present sequential models with averaged perceptron training for learning primary stress in Romanian words. We use character n-grams and syllable n-grams as features and we account for the consonant-vowel structure of the words. We show in this paper that Romanian stress is predictable, though not deterministic, by using data-driven machine learning techniques.