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N-ARY Tree- CNN for Arabic sentiment analysis / Shimaa Maher Abdallah Baraka ; Supervised Nevin M. Darwish , Mona F. Ahmed

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Shimaa Maher Abdallah Baraka , 2020Description: 60 P. : charts , facimiles ; 30cmOther title:
  • أسلوب جديد لتحليل الرأى في اللغة العربية عن طريق التعليم العميق باستخدام الشبكات العصبية الالتفافية والشجر متعدد الاطراف [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering Summary: Distributed document and sentence representation is an essential step in text classification. Several models have been studied to compose sentences into a fixed length representation.Such models range from simple order-insensitive models, like Bag-of-Words, to sequence based models, like RNN. In this thesis we propose an architecture that takes into account the hierarchal nature of the language, by building on binary Recursive Neural Nets, using CNN as an internal representation building block for N-ary trees. The algorithm is applied on Arabic sentiment analysis as an example text classification task and reduces the error rate by up to 15-20% for several standard datasets
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.M.Sc.2020.Sh.N (Browse shelf(Opens below)) Not for loan 01010110082523000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.M.Sc.2020.Sh.N (Browse shelf(Opens below)) 82523.CD Not for loan 01020110082523000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering

Distributed document and sentence representation is an essential step in text classification. Several models have been studied to compose sentences into a fixed length representation.Such models range from simple order-insensitive models, like Bag-of-Words, to sequence based models, like RNN. In this thesis we propose an architecture that takes into account the hierarchal nature of the language, by building on binary Recursive Neural Nets, using CNN as an internal representation building block for N-ary trees. The algorithm is applied on Arabic sentiment analysis as an example text classification task and reduces the error rate by up to 15-20% for several standard datasets

Issued also as CD

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