Using a machine learning model to assess the complexity of stress systems

Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014)

Abstract

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.