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Preprint
MultiSiam: A Multiple Input Siamese Network For Social Media Text Classification And Duplicate Text Detection
arXiv (Cornell University)
06 Jan 2024
Abstract
Social media accounts post increasingly similar content, creating a chaotic
experience across platforms, which makes accessing desired information
difficult. These posts can be organized by categorizing and grouping duplicates
across social handles and accounts. There can be more than one duplicate of a
post, however, a conventional Siamese neural network only considers a pair of
inputs for duplicate text detection. In this paper, we first propose a
multiple-input Siamese network, MultiSiam. This condensed network is then used
to propose another model, SMCD (Social Media Classification and Duplication
Model) to perform both duplicate text grouping and categorization. The
MultiSiam network, just like the Siamese, can be used in multiple applications
by changing the sub-network appropriately.
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Details
- Title
- MultiSiam: A Multiple Input Siamese Network For Social Media Text Classification And Duplicate Text Detection
- Creators
- Sudhanshu BhoiSwapnil MarkhedkarShruti PhadkePrashant Agrawal
- Publication Details
- arXiv (Cornell University)
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Other Identifier
- 991021985096504721