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Title: Measuring semantic similarity between Arabic sentences
Authors: Ali, Siba
Advisors: Mokbel, Chafic 
Keywords: Machine learning, Natural language processing, Similarity Analysis
Issue Date: 2023
This work discussed approaches of measuring Semantic Similarity between Arabic sentence, various techniques are described, then the results of similarity measurement using DSSM applied on the ASSD dataset was compared with results of HBMP model applied on the XNLI Arabic dataset. Another comparison is made between the results of the same HBMP model for the XNLI English and Arabic datasets. Our results show the higher accuracy achieved by HBMP on Arabic compared to DSSM, but this accuracy is still considered low compared to what the same model achieves for English, our conclusion focuses on the importance of a well curated dataset on the achievement of any model, especially in the context of Arabic which is morphologically complex and holds larger vocabulary set than English. Finally, it is suggested that Stemming can be utilized to enhance model accuracy for the purpose of semantic similarity between Arabic sentences.
Includes bibliographical references (p. 34-40)
Rights: This object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the personal and educational use exceptions must be obtained from the copyright holder
Ezproxy URL: Link to full text
Type: Thesis
Appears in Collections:UOB Theses and Projects

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