Using a machine learning model to assess the complexity of stress systems
Liviu Dinu, Alina Maria Ciobanu, Ioana Chitoran, 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.- Anthology ID:
- L14-1140
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 331–336
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/1200_Paper.pdf
- DOI:
- Cite (ACL):
- Liviu Dinu, Alina Maria Ciobanu, Ioana Chitoran, and Vlad Niculae. 2014. Using a machine learning model to assess the complexity of stress systems. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 331–336, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- Using a machine learning model to assess the complexity of stress systems (Dinu et al., LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/1200_Paper.pdf