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Bengali Slang Detection Using State-of-the-Art Supervised Models From a Given Text

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dc.contributor.author Hamid, Md. Abdul
dc.contributor.author Tumpa, Eteka Sultana
dc.contributor.author Polin, Johora Akter
dc.contributor.author Nahian, Jabir Al
dc.contributor.author Rahman, Atiqur
dc.contributor.author Mim, Nurjahan Akther
dc.date.accessioned 2024-05-04T06:21:10Z
dc.date.available 2024-05-04T06:21:10Z
dc.date.issued 2023-08-15
dc.identifier.issn 2302-9285
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12214
dc.description.abstract Almost all Bengalis who own smartphones also have social media accounts. People from different regions occasionally employ regional Slang that is unfamiliar to outsiders and confuses the meaning of the sentence. Nearly all languages can now be translated thanks to modern technology, but only in very basic ways, which is a concern. Bengali Slang terms are difficult to translate due to a dearth of rich corpora and frequently occurring new Slang terms developed by people, making it impossible for speakers of other languages to understand the context of a sentence in which Slang is used. We developed a solution to this issue. To create models that can detect Bengali Slang terms from social media, we gather various Slang phrases from various regions and develop a modest corpus. Our suggested method nearly always succeeds in extracting Bengali Slang terms from fresh material. We create a total of 7 supervised models and assess which is the most effective for our study. One of them has a 70% accuracy and 86% recall rate for successful identification. Our models may be linked to the social media platform's backend to restrict the use of Bengali Slang in posts, blogs, comments, and other areas. en_US
dc.language.iso en_US en_US
dc.publisher Institute of Advanced Engineering and Science (IAES) en_US
dc.subject Smartphones en_US
dc.subject Social media en_US
dc.subject Modern technology en_US
dc.title Bengali Slang Detection Using State-of-the-Art Supervised Models From a Given Text en_US
dc.type Article en_US


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