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arXiv · 1907.05757

Automated Word Stress Detection in Russian

Maria Ponomareva·Kirill Milintsevich·Ekaterina Chernyak·Anatoly Starostin
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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.

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Maria Ponomareva, Kirill Milintsevich, Ekaterina Chernyak, Anatoly Starostin. 2019-07-12. Automated Word Stress Detection in Russian. https://arxiv.org/abs/1907.05757

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