Automated Word Stress Detection in Russian

Ponomareva, Maria; Milintsevich, Kirill; Chernyak, Ekaterina; Starostin, Anatoly

doi:10.18653/v1/w17-4104

Cited by 5 publications

(5 citation statements)

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“…In this project, we approach the word stress detection problem for three East Slavic languages: Russian, Ukrainian and Belarusian, which are said to be mutually intelligible to some extent. Our preliminary experiments along with the results of (Ponomareva et al, 2017) show that using context, i.e., left and right words to the word under consideration, is of great help. Hence, such data sources as dictionaries, including Wiktionary, do not satisfy these requirements, because they provide only single words and do not provide context words.…”

Section: Datasetmentioning

confidence: 90%

“…The global model, which is shown to be the best RNN-based architecture for this setting of the task, was first presented in (Ponomareva et al, 2017), where a simple bidirectional RNN with LSTM nodes was used to achieve the accuracy of 90% or higher. The authors 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 to consider word frequencies and the morphological context of the word.…”

Section: Word Stress Detection In East Slavic Languagesmentioning

confidence: 99%

“…The authors 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 to consider word frequencies and the morphological context of the word. We extend the approach of (Ponomareva et al, 2017) by training on new datasets from additional languages and conducting cross-lingual experiments.…”

Section: Word Stress Detection In East Slavic Languagesmentioning

confidence: 99%

“…Also, the method cannot manage words without special morpheme markers. As shown in (Ponomareva et al, 2017), even simple deep learning methods easily outperform all the approaches described above.…”

Section: Introductionmentioning

confidence: 99%

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Chernyak¹,

Ponomareva²,

Milintsevich³

2019

Proceedings of the Sixth Workshop On

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We explore how well a sequence labeling approach, namely, recurrent neural network, is suited for the task of resource-poor and POS tagging free word stress detection in the Russian, Ukranian, Belarusian languages. We present new datasets, annotated with the word stress, for the three languages and compare several RNN models trained on three languages and explore possible applications of the transfer learning for the task. We show that it is possible to train a model in a cross-lingual setting and that using additional languages improves the quality of the results.

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Section: Datasetmentioning

confidence: 90%

Section: Word Stress Detection In East Slavic Languagesmentioning

confidence: 99%

Section: Word Stress Detection In East Slavic Languagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Chernyak¹,

Ponomareva²,

Milintsevich³

2019

Proceedings of the Sixth Workshop On

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“…The annotation was performed by Russian-language speakers on the crowdsourcing platform Yandex. Toloka, which had already been used in several academic studies about Russian-language texts [10], [29], [32], [44]. As an annotation guideline, we used the annotation instructions for toxicity with sub-attributes from Jigsaw Toxic Comment Classification Challenge.…”

Section: Toxic Comments Datasetmentioning

confidence: 99%

Toxic Comments Detection in Russian

Smetanin¹

2020

Computational Linguistics and Intellectual Technologies

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Currently, social network sites tend to be one of the major communication platforms in both offline and online space. Freedom of expression of various points of view, including toxic, aggressive, and abusive comments, might have a long-term negative impact on people’s opinions and social cohesion. As a consequence, the ability to automatically identify and moderate toxic content on the Internet to eliminate the negative consequences is one of the necessary tasks for modern society. This paper aims at the automatic detection of toxic comments in the Russian language. As a source of data, we utilized anonymously published Kaggle dataset and additionally validated its annotation quality. To build a classification model, we performed fine-tuning of two versions of Multilingual Universal Sentence Encoder, Bidirectional Encoder Representations from Transformers, and ruBERT. Finetuned RuBERT achieved F1 = 92.20%, demonstrating the best classification score. We made trained models and code samples publicly available to the research community.

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The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives

Smetanin

2020

IEEE Access

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Sentiment analysis has become a powerful tool in processing and analysing expressed opinions on a large scale. While the application of sentiment analysis on English-language content has been widely examined, the applications on the Russian language remains not as well-studied. In this survey, we comprehensively reviewed the applications of sentiment analysis of Russian-language content and identified current challenges and future research directions. In contrast with previous surveys, we targeted the applications of sentiment analysis rather than existing sentiment analysis approaches and their classification quality. We synthesised and systematically characterised existing applied sentiment analysis studies by their source of analysed data, purpose, employed sentiment analysis approach, and primary outcomes and limitations. We presented a research agenda to improve the quality of the applied sentiment analysis studies and to expand the existing research base to new directions. Additionally, to help scholars selecting an appropriate training dataset, we performed an additional literature review and identified publicly available sentiment datasets of Russian-language texts. INDEX TERMS Classification, machine learning, computational linguistics, sentiment analysis, applications of sentiment analysis, Russian-language texts, public opinion.

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Automated Word Stress Detection in Russian

Cited by 5 publications

References 5 publications

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Toxic Comments Detection in Russian

The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives

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