КФУ. Карточка публикации. Using Generative Adversarial Networks for Relevance Evaluation of Search Engine Results

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ФЕДЕРАЛЬНЫЙ УНИВЕРСИТЕТ

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USING GENERATIVE ADVERSARIAL NETWORKS FOR RELEVANCE EVALUATION OF SEARCH ENGINE RESULTS

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2020

Язык

английский

Авторы, сотрудники КФУ

Бухараев Наиль Раисович, автор

Библиографическое описание на языке оригинала

Dmitry N. Galanin, Nail R. Bukharaev, Alexander M. Gusenkov, Alina R. Sittikova. Using Generative Adversarial Networks for Relevance Evaluation of Search Engine Results / Proceedings of 2020 IEEE East-West Design & Test Symposium (EWDTS) Varna, Bulgaria, September 4 – 7, 2020 pp/ 288-294

Аннотация

Proceedings of 2020 IEEE East-West Design & Test Symposium (EWDTS) Varna, Bulgaria, September 4 – 7, 2020

Ключевые слова

Generative adversarial networks, machine learn- ing, information retrieval

Название журнала

Proceedings of 2020 IEEE East-West Design & Test Symposium (EWDTS) Varna, Bulgaria, September 4 – 7, 2020

URL

https://www.computer.org/csdl/proceedings-article/ewdts/2020/09224840/1nWNUpU1u2Q

Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку

https://repository.kpfu.ru/?p_id=239748

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Бухараев Наиль Раисович	ru_RU
dc.date.accessioned	2020-01-01T00:00:00Z	ru_RU
dc.date.available	2020-01-01T00:00:00Z	ru_RU
dc.date.issued	2020	ru_RU
dc.identifier.citation	Dmitry N. Galanin, Nail R. Bukharaev, Alexander M. Gusenkov, Alina R. Sittikova. Using Generative Adversarial Networks for Relevance Evaluation of Search Engine Results / Proceedings of 2020 IEEE East-West Design & Test Symposium (EWDTS) Varna, Bulgaria, September 4 – 7, 2020 pp/ 288-294	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=239748	ru_RU
dc.description.abstract	Proceedings of 2020 IEEE East-West Design & Test Symposium (EWDTS) Varna, Bulgaria, September 4 – 7, 2020	ru_RU
dc.description.abstract	In the article a new approach to the problem of relevance evaluation of the search engine results, based on generative adversarial networks (GAN), is proposed. To improve the quality of search, the generative adversarial networks are used to distinguish between relevant and irrelevant search results. We used a simplistic model based on fully automated reference results selection and multi-layered generator and discriminator networks with dense layers. The queries needed to generate the reference results were themselves generated by a GPT-2 like network using the same text corpus as a source, to make them potentially relevant to the search space. The results clearly demonstrate the principal possibility and feasibility of using the described approach, despite the fact of used models being simplistic.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Generative adversarial networks	ru_RU
dc.subject	machine learn- ing	ru_RU
dc.subject	information retrieval	ru_RU
dc.title	Using Generative Adversarial Networks for Relevance Evaluation of Search Engine Results	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU