Recurrent Neural Networks with Continuous Learning in Problems of News Streams Multifunctional Processing
Keywords:
recurrent neural networks, intelligent news processing, multifunctionality, continuity of learning, forecastingAbstract
The main task of using neural networks is the prompt and accurate solution of various creative tasks, including the analysis and synthesis of news flows, while maintaining the continuity of learning. The result of such processing can be digests, filtered news streams, as well as event forecasts that allow for proactivity in management decisions. Known methods of news processing by neural networks and technical solutions that implement them do not fully provide a solution to the problems that arise in this area. It is necessary to expand their functionality, and improve the space-time signal binding in recurrent neural networks. When processing news flows, simultaneously with continuous training of recurrent neural networks, selection, recognition, restoration, prediction and synthesis of news should be carried out. To reduce the severity of the problem, a promising method of multifunctional processing of news flows is proposed using recurrent neural networks with a logical organization of layers and continuous learning. The method is based on the development of associative processing of textual information in streaming recurrent neural networks with controlled elements. The key features of this method are the multifunctional processing of information flows with changing laws of news appearance. The method provides for operational selection, recognition, restoration, forecasting and synthesis of news based on deep associative continuous processing of links between text elements. The neural network system that implements the proposed method differs from the known solutions by new elements, connections between them, as well as by the functions performed. The results of the experiments confirmed the extended functionality of the method. New features of processing news texts by streaming RNNs are revealed. The proposed solutions can be used to create a new generation of intelligent systems not only for word processing, but also for other types of information.
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Copyright (c) Василий Юрьевич Осипов, Сергей Викторович Кулешов, Дмитрий Игоревич Милосердов, Александра Алексеевна Зайцева, Алексей Юрьевич Аксенов
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