Distinguishing nominal and verbal Arabic sentences : a machine learning approach

Other Title(s)

تمييز الجمل الاسمية و الفعلية للغة العربية : نهج التعلم الآلي

Dissertant

Abd al-Raziq, Duaa Jamal Mahmud

Thesis advisor

Ujan, Arafat
Abu Suud, Salih

University

Princess Sumaya University for Technology

Faculty

King Hussein Faculty for Computing Sciences

Department

Department of Computer Sciences

University Country

Jordan

Degree

Master

Degree Date

2014

English Abstract

The analysis of human language in which computers can understand the languages as humans, is one of the oldest and most difficult problems in the field of artificial intelligence.

Although this goal is still some way off, Natural Language Processing (NLP) can perform some types of analysis with a high degree of success. Arabic language is a very rich language with complex morphology, which is more different and more difficult structure than other languages.

" اعراب " < ErAb is the primary constructing tool that helps users to write a sentence by analyzing each word and then only accepting the sentence if it is grammatically correct.

Since the first step in

The uses of inductive machine learning approach in association with Natural language processing is a young and an interdisciplinary collaboration field, specifically in Arabic Language.

The successful results of this research prove that implementing inductive machine learning in Arabic complex structure will gain a promising contribution in the field of Arabic natural language processing (ANLP).

This research, can be applied and used to guide the implementation of ML in Arabic Grammar structure.

Main Subjects

Information Technology and Computer Science

Topics

• Data processing
• Arabic language

No. of Pages

110

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-414175