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Разработка алгоритма для генерации текста на основе нейросетевой модели
Разработка алгоритма для генерации текста на основе нейросетевой модели
Объектом исследования являются нейросетевые лингвистические модели. Цель работы: разработка алгоритма для генерации текста на основе нейросетевой модели. В ходе данной работы рассмотрена одна из задач обработки естественного языка - лингвистическое моделирование, произведен обзор архитектуры различных нейросетевых моделей, разработан алгоритм для генерации текста, произведено сравнение данного алгоритма с использованием различных нейросетевых моделей. Данная разработка позволит автоматизировать процесс создания различного текстового контента для интернет ресурсов. The object of study are neural network language modeling. Purpose of work: development an algorithm for generating text based on a neural network model. In the course of this work, one of the tasks of natural language processing, language modeling, was reviewed, the architecture of various neural network models was reviewed, a text generation algorithm was developed, and a comparison was made with this algorithm using various neural network models. This development will automate the process of creating various text content for Internet resources.
- Tomsk Polytechnic University Russian Federation
обработка естественного языка, synonymization, language modeling, transformer, text generation, синонимизация, генерация текста, natural language processing, лингвистическое моделирование, 004.738.5:339 [004.421.5.032.26], BERT, 01.03.02
обработка естественного языка, synonymization, language modeling, transformer, text generation, синонимизация, генерация текста, natural language processing, лингвистическое моделирование, 004.738.5:339 [004.421.5.032.26], BERT, 01.03.02
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Popularity provided by BIP!- Tomsk Polytechnic University Russian Federation
Объектом исследования являются нейросетевые лингвистические модели. Цель работы: разработка алгоритма для генерации текста на основе нейросетевой модели. В ходе данной работы рассмотрена одна из задач обработки естественного языка - лингвистическое моделирование, произведен обзор архитектуры различных нейросетевых моделей, разработан алгоритм для генерации текста, произведено сравнение данного алгоритма с использованием различных нейросетевых моделей. Данная разработка позволит автоматизировать процесс создания различного текстового контента для интернет ресурсов. The object of study are neural network language modeling. Purpose of work: development an algorithm for generating text based on a neural network model. In the course of this work, one of the tasks of natural language processing, language modeling, was reviewed, the architecture of various neural network models was reviewed, a text generation algorithm was developed, and a comparison was made with this algorithm using various neural network models. This development will automate the process of creating various text content for Internet resources.