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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/W17-4104.pdf