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Учет структуры текста при решении задач определения пола автора текста глубокими нейронными сетями с эволюционным подбором их конфигурации

https://doi.org/10.1134/S2304487X20050132

Об авторах

А. Г. Сбоев
НИЦ “Курчатовский институт”; Национальный исследовательский ядерный университет “МИФИ”
Россия

123182

115409

Москва



А. А. Селиванов
НИЦ “Курчатовский институт”
Россия

123182

Москва



Р. Б. Рыбка
НИЦ “Курчатовский институт”
Россия

123182

Москва



И. А. Молошников
НИЦ “Курчатовский институт”
Россия

123182

Москва



Список литературы

1. Sboev A., Moloshnikov I., Gudovskikh D., Selivanov A., Rybka R., Litvinova T. Deep learning neural nets versus traditional machine learning in gender identification of authors of RusProfiling texts // Procedia Comput. Sci. 2018, V. 123. P. 424–431.

2. Sboev A., Moloshnikov I., Gudovskikh D., Selivanov A., Rybka R., Litvinova T. Automatic gender identification of author of Russian text by machine learning and neural net algorithms in case of gender deception // Procedia Computer Science. 2018. V. 123. P. 417–423, ISSN 1877-0509. doi: 10.1016/j.procs.2018.01.064

3. Sboev A., Moloshnikov I., Gudovskikh D., Rybka R. A comparison of Data Driven models of solving the task of gender identification of author in Russian language texts for cases without and with the gender deception // IOP Publishing, Journal of Physics: Conf. Ser. 2017. V. 937. P. 1742–6588.

4. Bergstra J. S., Yamins D., Cox D. Algorithms for hyperparameter optimization // Advances in Neural Information Processing Systems. 2011.

5. Bergstra J. S., Yamins D., Cox D. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures / Proceedings of the 30th International Conference on Machine Learning. 2013.

6. Tai K. S., Socher R., Manning C. D. Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075, 2015.

7. Miyazaki R., Komachi M. Japanese sentiment classification using a tree-structured long short-term memory with attention. arXiv preprint arXiv:1704.00924. 2017.

8. Sboev A., Selivanov A., Rybka R., Moloshnikov I., Bogachev D. A Neural Network Model to Include Textual Dependency Tree Structure in Gender Classification of Russian Text Author. In: Advanced Technologies in Robotics and Intelligent Systems. Mechanisms and Machine Science. Springer, Cham, 2020. V. 80. doi: 10.1007/978-3-030-33491-8_48

9. Sboev A., Bogachev D., Selivanov A., Moloshnikov I., Rybka R. Graph Convolution Network with Attention to Include Syntax Trees into Text Author’s Gender Identification Task (in press).

10. Veličković P., Cucurull G., Casanova A., Romero A., Lio P., Bengio Y. Graph Attention Networks. 2018. Published as a conference paper at ICLR 2018.

11. Haifeng Jin, Qingquan Song, Xia Hu. Auto-keras: An efficient neural architecture search system / Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2019.

12. Weill C., Gonzalvo J., Kuznetsov V., Yang S., Yak S., Mazzawi H., Hotaj E., Jerfel G., Macko V., Adlam B., Mohri M. AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles. arXiv preprint arXiv:1905.00080. 2019.

13. Miikkulainen R., Liang J., Meyerson E., Rawal A., Fink D., Francon O., Raju B., Shahrzad H., Navruzyan A., Duffy N., Hodjat B. Evolving deep neural networks. In-Artificial Intelligence in the Age of Neural Networks and Brain Computing. Academic Press, 2019. P. 293–312.

14. James B., Yamins D., Cox D. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures / Proceedings of the 30th International Conference on Machine Learning. 2013.

15. Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A. N., Kaiser Ł., Polosukhin I. Attention is all you need / In: Advances in Neural Information Processing systems. 2017. P. 5998–6008.

16. Devlin J., Chang M. W., Lee K., Toutanova K. Bert: Pretraining of deep bidirectional transformers for language understanding. arXiv preprint arXiv: 1810.04805. 2018.

17. Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting // Journal of Machine Learning Research. 2014. V. 15. № 1. P. 1929–1958.

18. Mosella-Montoro A., Ruiz-Hidalgo J. Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification / In: Proceedings of the IEEE International Conference on Computer Vision Work-shops, 2019.

19. Straka M., Straková J. Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe / Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, 2017. P. 88–99.

20. Vig J. A multiscale visualization of attention in the transformer model. arXiv preprint arXiv: 1906.05714. 2019. no. 12.


Рецензия

Для цитирования:


Сбоев А.Г., Селиванов А.А., Рыбка Р.Б., Молошников И.А. Учет структуры текста при решении задач определения пола автора текста глубокими нейронными сетями с эволюционным подбором их конфигурации. Вестник НИЯУ МИФИ. 2020;9(6):554-560. https://doi.org/10.1134/S2304487X20050132

For citation:


Sboev A.G., Selivanov A.A., Moloshnikov I.A., Rybka R.B. Evolutionary Configuration Selection of Deep Neural Networks Accounting Text Structures in a Task of Author’s Gender Profiling. Vestnik natsional'nogo issledovatel'skogo yadernogo universiteta "MIFI". 2020;9(6):554-560. (In Russ.) https://doi.org/10.1134/S2304487X20050132

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