Vector space models of kyiv city petitions

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dc.contributor.authorShaptala, Roman
dc.contributor.authorKyselov, Gennadiy
dc.date.accessioned2023-04-18T15:35:14Z
dc.date.available2023-04-18T15:35:14Z
dc.date.issued2021
dc.description.abstractIn this study, we explore and compare two ways of vector space model creation for Kyiv city petitions. Both models are built on top of word vectors based on the distributional hypothesis, namely Word2Vec and FastText. We train word vectors on the dataset of Kyiv city petitions, preprocess the documents, and apply averaging to create petition vectors. Visualizations of the vector spaces after dimensionality reduction via UMAP are demonstrated in an attempt to show their overall structure. We show that the resulting models can be used to effectively query semantically related petitions as well as search for clusters of related petitions. The advantages and disadvantages of both models are analyzed.uk
dc.format.pagerangePp. 26-34uk
dc.identifier.citationShaptala, R. Vector space models of kyiv city petitions / Shaptala Roman, Kyselov Gennadiy // Information, Computing and Intelligent systems. – 2021. – No. 2. – Pp. 26–34. – Bibliogr.: 19 ref.uk
dc.identifier.doihttps://doi.org/10.20535/2708-4930.2.2021.244188
dc.identifier.orcid0000-0003-2682-3593uk
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/54696
dc.language.isoukuk
dc.publisherNational Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"uk
dc.publisher.placeKyivuk
dc.relation.ispartofInformation, Computing and Intelligent systems, No. 2uk
dc.subjectvector space modeluk
dc.subjectFastTextuk
dc.subjectWord2Vecuk
dc.subjectpetitions analysisuk
dc.subjectUMAPuk
dc.subject.udc004.855.5uk
dc.titleVector space models of kyiv city petitionsuk
dc.typeArticleuk

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Information, Computing and Intelligent systems, No. 2