2016, Чернышов, А. А., Чернышов, Артем Андреевич, Климов, В. В.

2021, Chernyshov, A., Klimov, V., Balandina, A., Shchukin, B., Чернышов, Артем Андреевич, Климов, Валентин Вячеславович, Баландина, Анита Ивановна, Щукин, Борис Алексеевич

© 2020 Elsevier B.V.. All rights reserved.In this paper, authors discover the advantages of the attention-based neural network application to the natural language dependency-parsing task. The authors explain the architecture and show the results of comparison between attention-based neural network and long-short memory neural networks in relation to the dependency-parsing task.

2020, Chernyshov, A., Balandina, A., Klimov, V., Чернышов, Артем Андреевич, Баландина, Анита Ивановна, Климов, Валентин Вячеславович

© 2020, Springer Nature Switzerland AG.This article provides an overview of modern natural language processing and understanding methods. All the monitored technologies are covered in the context of search engines. The authors do not consider any particular implementations of the search engines; however take in consideration some scientific research to show natural language processing techniques application prospects in the informational search industry.

2020, Klimov, V., Balandina, A., Chernyshov, A., Климов, Валентин Вячеславович, Баландина, Анита Ивановна, Чернышов, Артем Андреевич

© 2020 The Authors. Published by Elsevier B.V.This article provides an overview and description of the long-short memory approaches for the neural networks modelling and development. The authors show the possible application of these models and methods and consider its usage during the process of natural language understanding as the part of the semantic search system.