Toward arabic text mining / Talal Saeed Saleh Kaid ; Supervised Ali Ali Fahmy , Ahmed Hussain Kamal

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Talal Saeed Saleh Kaid

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Material type: Text

TextLanguage: eng Publication details: Cairo : Talal Saeed Saleh Kaid , 2009Description: 151Leaves : charts ; 30cmOther title:

نحو التنقيب فى النصوص العربية [Added title page title]

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Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Computers and information - Department of Computer Science Summary: This resarch mines Arabic douments and classify them according to their subjects to examine the classifier and selects the best one to apply it to very interesting problem which is the free- text answers assessment . We used the SVM , NB , LSA and SVM - SVD classifiers on automated essay scoring on different type of essays (long written and short written) using 2000 essays

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Holdings
Item type	Current library	Home library	Call number	Copy number	Status	Date due	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.20.03.Ph.D.2009.Ta.T (Browse shelf(Opens below))		Not for loan		01010110052273000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.20.03.Ph.D.2009.Ta.T (Browse shelf(Opens below))	52273.CD	Not for loan		01020110052273000

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Previous	Cai01.20.03.Ph.D.2009.Ma.K. Knowledge discovery from the Web /	Cai01.20.03.Ph.D.2009.Ta.C Cross language information retrieval (CLIR) for digital libraries with Arabic ocr-degraded text /	Cai01.20.03.Ph.D.2009.Ta.C Cross language information retrieval (CLIR) for digital libraries with Arabic ocr-degraded text /	Cai01.20.03.Ph.D.2009.Ta.T Toward arabic text mining /	Cai01.20.03.Ph.D.2009.Ta.T Toward arabic text mining /	Cai01.20.03.Ph.D.2010.Ab.D A data miner for protein interactions /	Cai01.20.03.Ph.D.2010.Ab.D A data miner for protein interactions /	Next

Thesis (Ph.D.) - Cairo University - Faculty of Computers and information - Department of Computer Science

This resarch mines Arabic douments and classify them according to their subjects to examine the classifier and selects the best one to apply it to very interesting problem which is the free- text answers assessment . We used the SVM , NB , LSA and SVM - SVD classifiers on automated essay scoring on different type of essays (long written and short written) using 2000 essays

Issued also as CD

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