Academic Journal
LSRM: A New Method for Turkish Text Classification.
| Τίτλος: | LSRM: A New Method for Turkish Text Classification. |
|---|---|
| Συγγραφείς: | Borandağ, Emin |
| Πηγή: | Applied Sciences (2076-3417); Dec2024, Vol. 14 Issue 23, p11143, 18p |
| Θεματικοί όροι: | Machine learning, Turkish language, Text mining, Test methods, Deep learning, Classification |
| Περίληψη: | The text classification method is one of the most frequently used approaches in text mining studies. Text classification requires a model generation using a predefined dataset, and this model aims to assign uncategorized data to a correct category. In line with this purpose, this study used machine learning algorithms, deep learning algorithms, word embedding algorithms, and transfer-learning algorithms to classify Turkish texts using three diverse datasets, one of which is new, to analyze text classification performances for the Turkish language. The preparation process of the newly added dataset involved the variations in Turkish word usage patterns over the years, since it consisted of timestamp-enabled data. The study also developed a novel method named LSRM to increase the text classification performance for agglutinative languages such as Turkish. After testing the new method on datasets, the statistical ANOVA method revealed that applying the proposed LSRM method increased the classification performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20763417&ISBN=&volume=14&issue=23&date=20241201&spage=11143&pages=11143-11160&title=Applied Sciences (2076-3417)&atitle=LSRM%3A%20A%20New%20Method%20for%20Turkish%20Text%20Classification.&aulast=Boranda%C4%9F%2C%20Emin&id=DOI:10.3390/app142311143 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 181655449 RelevancyScore: 983 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 983.441223144531 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: LSRM: A New Method for Turkish Text Classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Borandağ%2C+Emin%22">Borandağ, Emin</searchLink> – Name: TitleSource Label: Source Group: Src Data: Applied Sciences (2076-3417); Dec2024, Vol. 14 Issue 23, p11143, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Turkish+language%22">Turkish language</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink><br /><searchLink fieldCode="DE" term="%22Test+methods%22">Test methods</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The text classification method is one of the most frequently used approaches in text mining studies. Text classification requires a model generation using a predefined dataset, and this model aims to assign uncategorized data to a correct category. In line with this purpose, this study used machine learning algorithms, deep learning algorithms, word embedding algorithms, and transfer-learning algorithms to classify Turkish texts using three diverse datasets, one of which is new, to analyze text classification performances for the Turkish language. The preparation process of the newly added dataset involved the variations in Turkish word usage patterns over the years, since it consisted of timestamp-enabled data. The study also developed a novel method named LSRM to increase the text classification performance for agglutinative languages such as Turkish. After testing the new method on datasets, the statistical ANOVA method revealed that applying the proposed LSRM method increased the classification performance. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=181655449 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/app142311143 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 11143 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Turkish language Type: general – SubjectFull: Text mining Type: general – SubjectFull: Test methods Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Classification Type: general Titles: – TitleFull: LSRM: A New Method for Turkish Text Classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Borandağ, Emin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 20763417 Numbering: – Type: volume Value: 14 – Type: issue Value: 23 Titles: – TitleFull: Applied Sciences (2076-3417) Type: main |
| ResultId | 1 |