Likelihood corpus distribution: an efficient topic modelling scheme for Bengali document class identification.
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| Title: | Likelihood corpus distribution: an efficient topic modelling scheme for Bengali document class identification. |
|---|---|
| Authors: | Das Dawn, Debapratim1 (AUTHOR) debapratimdd@gmail.com, Khan, Abhinandan1,2 (AUTHOR), Shaikh, Soharab Hossain3 (AUTHOR), Pal, Rajat Kumar1 (AUTHOR) |
| Source: | Sādhanā: Academy Proceedings in Engineering Sciences. Sep2024, Vol. 49 Issue 3, p1-19. 19p. |
| Subjects: | Identification documents, Latent semantic analysis, Artificial intelligence, Corpora, Library science, Document clustering, Sports sciences |
| Abstract: | The learning quality of humans depends on the sense of contemplation. Textual documents are a huge part of the literature on contemplation which effortlessly creates perception. Automatic document class identification or organisation is a machine learning function to understand the psychological and emotional content of the text in a concise way. The problem of identification of documents falls in the field of library science, information science and artificial intelligence. The research progress of class identification of documents has been made in various most spoken languages. Numerous research works have been published in European and Asian languages. However, there is a gap in the literature when it comes to any less resource language, especially Bengali. Consequently, this work portrays an efficient topic modelling approach for Bengali document class identification. It proposes a Dirichlet-polynomial clustering model likelihood corpus distribution (LCD), which is based on a Bayesian numerical prototype. Experiments are done to prove the efficiency of LCD over various topic modelling algorithms, such as latent Dirichlet allocation (LDA), LDA with bag-of-words (LDA-BOW), latent semantic indexing (LSI), and hierarchical Dirichlet process (HDP). For performance evaluation, we considered five real-world datasets of Bengali corpora, such as science, sports, computer, season, and epic in this work. The coherence score of different modelling algorithms is compared to find the best model for each dataset separately. [ABSTRACT FROM AUTHOR] |
| Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature 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.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 178527531 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Likelihood corpus distribution: an efficient topic modelling scheme for Bengali document class identification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Das+Dawn%2C+Debapratim%22">Das Dawn, Debapratim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> debapratimdd@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Khan%2C+Abhinandan%22">Khan, Abhinandan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shaikh%2C+Soharab+Hossain%22">Shaikh, Soharab Hossain</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pal%2C+Rajat+Kumar%22">Pal, Rajat Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sādhanā%3A+Academy+Proceedings+in+Engineering+Sciences%22">Sādhanā: Academy Proceedings in Engineering Sciences</searchLink>. Sep2024, Vol. 49 Issue 3, p1-19. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Identification+documents%22">Identification documents</searchLink><br /><searchLink fieldCode="DE" term="%22Latent+semantic+analysis%22">Latent semantic analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Corpora%22">Corpora</searchLink><br /><searchLink fieldCode="DE" term="%22Library+science%22">Library science</searchLink><br /><searchLink fieldCode="DE" term="%22Document+clustering%22">Document clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Sports+sciences%22">Sports sciences</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The learning quality of humans depends on the sense of contemplation. Textual documents are a huge part of the literature on contemplation which effortlessly creates perception. Automatic document class identification or organisation is a machine learning function to understand the psychological and emotional content of the text in a concise way. The problem of identification of documents falls in the field of library science, information science and artificial intelligence. The research progress of class identification of documents has been made in various most spoken languages. Numerous research works have been published in European and Asian languages. However, there is a gap in the literature when it comes to any less resource language, especially Bengali. Consequently, this work portrays an efficient topic modelling approach for Bengali document class identification. It proposes a Dirichlet-polynomial clustering model likelihood corpus distribution (LCD), which is based on a Bayesian numerical prototype. Experiments are done to prove the efficiency of LCD over various topic modelling algorithms, such as latent Dirichlet allocation (LDA), LDA with bag-of-words (LDA-BOW), latent semantic indexing (LSI), and hierarchical Dirichlet process (HDP). For performance evaluation, we considered five real-world datasets of Bengali corpora, such as science, sports, computer, season, and epic in this work. The coherence score of different modelling algorithms is compared to find the best model for each dataset separately. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12046-024-02470-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: Identification documents Type: general – SubjectFull: Latent semantic analysis Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Corpora Type: general – SubjectFull: Library science Type: general – SubjectFull: Document clustering Type: general – SubjectFull: Sports sciences Type: general Titles: – TitleFull: Likelihood corpus distribution: an efficient topic modelling scheme for Bengali document class identification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Das Dawn, Debapratim – PersonEntity: Name: NameFull: Khan, Abhinandan – PersonEntity: Name: NameFull: Shaikh, Soharab Hossain – PersonEntity: Name: NameFull: Pal, Rajat Kumar IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 02562499 Numbering: – Type: volume Value: 49 – Type: issue Value: 3 Titles: – TitleFull: Sādhanā: Academy Proceedings in Engineering Sciences Type: main |
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