Automated Word Stress Detection in Russian
Maria Ponomareva, Kirill Milintsevich, Ekaterina Chernyak, Anatoly Starostin
Abstract
In this study we address the problem of automated word stress detection in Russian using character level models and no part-speech-taggers. We use a simple bidirectional RNN with LSTM nodes and achieve accuracy of 90% or higher. We experiment with two training datasets and show that using the data from an annotated corpus is much more efficient than using only a dictionary, since it allows to retain the context of the word and its morphological features.- Anthology ID:
- W17-4104
- Volume:
- Proceedings of the First Workshop on Subword and Character Level Models in NLP
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Manaal Faruqui, Hinrich Schuetze, Isabel Trancoso, Yadollah Yaghoobzadeh
- Venue:
- SCLeM
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 31–35
- Language:
- URL:
- https://aclanthology.org/W17-4104
- DOI:
- 10.18653/v1/W17-4104
- Cite (ACL):
- Maria Ponomareva, Kirill Milintsevich, Ekaterina Chernyak, and Anatoly Starostin. 2017. Automated Word Stress Detection in Russian. In Proceedings of the First Workshop on Subword and Character Level Models in NLP, pages 31–35, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Automated Word Stress Detection in Russian (Ponomareva et al., SCLeM 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/W17-4104.pdf