Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3782
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dc.contributor.authorTaşkıran, Fatma-
dc.contributor.authorKaya, Ersin-
dc.date.accessioned2023-03-03T13:35:02Z-
dc.date.available2023-03-03T13:35:02Z-
dc.date.issued2022-
dc.identifier.issn2667-8055-
dc.identifier.urihttps://doi.org/10.36306/konjes.1081213-
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1144638-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/3782-
dc.description.abstractAccessing data is very easy nowadays. However, to use these data in an efficient way, it is necessary to get the right information from them. Categorizing these data in order to reach the needed information in a short time provides great convenience. All the more, while doing research in the academic field, text-based data such as articles, papers, or thesis studies are generally used. Natural language processing and machine learning methods are used to get the right information we need from these text-based data. In this study, abstracts of academic papers are clustered. Text data from academic paper abstracts are preprocessed using natural language processing techniques. A vectorized word representation extracted from preprocessed data with Word2Vec and BERT word embeddings and representations are clustered with four clustering algorithms.en_US
dc.language.isoenen_US
dc.relation.ispartofKonya mühendislik bilimleri dergisi (Online)en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectNatural Language Processingen_US
dc.subjectMachine Learningen_US
dc.subjectText Representationen_US
dc.titleACADEMIC TEXT CLUSTERING USING NATURAL LANGUAGE PROCESSINGen_US
dc.typeArticleen_US
dc.identifier.doi10.36306/konjes.1081213-
dc.departmentKATÜNen_US
dc.identifier.volume10en_US
dc.identifier.issue0en_US
dc.identifier.startpage41en_US
dc.identifier.endpage51en_US
dc.institutionauthor-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanen_US
dc.identifier.trdizinid1144638en_US
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.03. Department of Computer Engineering-
Appears in Collections:TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections
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