@inproceedings{ponomareva-etal-2017-automated,
title = "Automated Word Stress Detection in {R}ussian",
author = "Ponomareva, Maria and
Milintsevich, Kirill and
Chernyak, Ekaterina and
Starostin, Anatoly",
editor = "Faruqui, Manaal and
Schuetze, Hinrich and
Trancoso, Isabel and
Yaghoobzadeh, Yadollah",
booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4104",
doi = "10.18653/v1/W17-4104",
pages = "31--35",
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.",
}
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%0 Conference Proceedings
%T Automated Word Stress Detection in Russian
%A Ponomareva, Maria
%A Milintsevich, Kirill
%A Chernyak, Ekaterina
%A Starostin, Anatoly
%Y Faruqui, Manaal
%Y Schuetze, Hinrich
%Y Trancoso, Isabel
%Y Yaghoobzadeh, Yadollah
%S Proceedings of the First Workshop on Subword and Character Level Models in NLP
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F ponomareva-etal-2017-automated
%X 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.
%R 10.18653/v1/W17-4104
%U https://aclanthology.org/W17-4104
%U https://doi.org/10.18653/v1/W17-4104
%P 31-35
Markdown (Informal)
[Automated Word Stress Detection in Russian](https://aclanthology.org/W17-4104) (Ponomareva et al., SCLeM 2017)
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.