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

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-vowe...

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Bibliographic Details
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
Description
Summary: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.

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