Please use this identifier to cite or link to this item:
|Title:||Measuring semantic similarity between Arabic sentences||Authors:||Ali, Siba||Advisors:||Mokbel, Chafic||Keywords:||Machine learning, Natural language processing, Similarity Analysis||Issue Date:||2023||Abstract:||
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)
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/7038||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|
Show full item record
checked on Nov 29, 2023
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.