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Кластеризация текстовых данных на основе методов машинного обучения
Кластеризация текстовых данных на основе методов машинного обучения
Целью работы является разработка системы кластеризации поисковых запросов семантического ядра. Работа включает в себя определение архитектуры, выбор модели и обучение нейронной сети, выбор оптимального алгоритма кластеризации и оценка получаемых результатов данного алгоритма, разработка клиентской и серверной частей веб-приложения. Система предназначена для сокращения времени анализа поисковых запросов для поисковой оптимизации сайта. The aim of the work is to develop a system for clustering search queries of the semantic core. The work includes determining the architecture, choosing a model and training a neural network, choosing the optimal clustering algorithm and evaluating the results of this algorithm, developing the client and server parts of a web application. Restriction system to reduce the time of analysis of search sources for a search site.
- Tomsk Polytechnic University Russian Federation
обработка естественного языка, semantic core, sentence transformers, search engine optimization, 09.03.02, поисковая оптимизация, преобразователи предложений, семантическое ядро, natural language processing, кластеризация, 004.85 [004.422.6], clustering
обработка естественного языка, semantic core, sentence transformers, search engine optimization, 09.03.02, поисковая оптимизация, преобразователи предложений, семантическое ядро, natural language processing, кластеризация, 004.85 [004.422.6], clustering
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
Citations provided by BIP!Popularity provided by BIP!- Tomsk Polytechnic University Russian Federation
Целью работы является разработка системы кластеризации поисковых запросов семантического ядра. Работа включает в себя определение архитектуры, выбор модели и обучение нейронной сети, выбор оптимального алгоритма кластеризации и оценка получаемых результатов данного алгоритма, разработка клиентской и серверной частей веб-приложения. Система предназначена для сокращения времени анализа поисковых запросов для поисковой оптимизации сайта. The aim of the work is to develop a system for clustering search queries of the semantic core. The work includes determining the architecture, choosing a model and training a neural network, choosing the optimal clustering algorithm and evaluating the results of this algorithm, developing the client and server parts of a web application. Restriction system to reduce the time of analysis of search sources for a search site.