Summary of the paper
| Title | Using a Machine Learning Model to Assess the Complexity of Stress Systems |
|---|---|
| Authors | Liviu Dinu, Alina Maria Ciobanu, Ioana Chitoran and Vlad Niculae |
| 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. |
| Topics | Phonetic Databases, Phonology, Evaluation Methodologies |
Full paper  |
Using a Machine Learning Model to Assess the Complexity of Stress Systems |
| Bibtex | @InProceedings{DINU14.1200, author = {Liviu Dinu and Alina Maria Ciobanu and Ioana Chitoran and Vlad Niculae}, title = {Using a Machine Learning Model to Assess the Complexity of Stress Systems}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |