Detection of Similar Text Documents Based on Self-Organizing Maps

Alahmad, Kutiba (2024) Detection of Similar Text Documents Based on Self-Organizing Maps. Other thesis, Universität Rostock.

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Abstract

Plagiarism of text has become a common occurrence today with difficulty in detecting forms such as paraphrasing being frequently practiced. This project presents an approach for detecting plagiarism in academic documents using Self-Organizing Maps (SOMs). The system leverages SOMs to cluster documents based on both word-level and context-level similarities, achieved through advanced text embeddings. Experimental results demonstrate the effectiveness of this approach in accurately detecting textual similarities and distinguishing between original and plagiarized content. Future enhancements include fine-tuning the embedding models and expanding the system’s capabilities to handle multilingual.

Item Type: Thesis (Other)
Subjects: Autorenart > Studentische Arbeiten > Bachelorarbeit
Forschungsthemen > Digitale Bibliotheken
Forschungsthemen > Information Retrieval
Autorenart > Studentische Arbeiten
Depositing User: Dbis Admin
Date Deposited: 26 Nov 2024 15:08
Last Modified: 26 Nov 2024 15:08
URI: https://eprints.dbis.informatik.uni-rostock.de/id/eprint/1120

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