Automated Word Stress Detection in Russian
Computation and Language
2019-07-15 v1
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 the 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 a dictionary, since it allows us to take into account word frequencies and the morphological context of the word.
Cite
@article{arxiv.1907.05757,
title = {Automated Word Stress Detection in Russian},
author = {Maria Ponomareva and Kirill Milintsevich and Ekaterina Chernyak and Anatoly Starostin},
journal= {arXiv preprint arXiv:1907.05757},
year = {2019}
}
Comments
SCLeM 2017