نجود عبيدالله محمد الجهني

 


      
 
الاسم الاول: 
نجود
اسم العائلة: 
الجهني
الدرجة العلمية: 
دكتوراة
مجال الدراسة: 
العلوم والتقنية
المؤسسة التعليمية: 
Bangor University

البحوث المنشورة

 

البحث (1):

 

عنوان البحث:

GRAMMAR-BASED PRE-PROCESSING FOR PPM

رابط إلى البحث:

Click here

تاريخ النشر:

31/10/2017

موجز عن البحث:

 

In this paper, we apply grammar-based pre-processing prior to using the Prediction by Partial Matching (PPM) compression algorithm. This achieves significantly better compression for different natural language texts compared to other well-known compression methods. Our method first generates a grammar based on the most common two-character sequences (bigraphs) or three-character sequences (trigraphs) in the text being compressed and then substitutes these sequences using the respective non-terminal symbols defined by the grammar in a pre-processing phase prior to the compression. This leads to significantly improved results in compression for various natural languages (a 5% improvement for American English, 10% for British English, 29% for Welsh, 10% for Arabic, 3% for Persian and 35% for Chinese). We describe further improvements using a two pass scheme where the grammar-based pre-processing is applied again in a second pass through the text. We then apply the algorithms to the files in the Calgary Corpus and also achieve significantly improved results in compression, between 11% and 20%, when compared with other compression algorithms, including a grammar-based approach, the Sequitur algorithm.

 

 

البحث (2):

 

عنوان البحث:

Word-Based Grammars for PPM

رابط إلى البحث:

Click here

تاريخ النشر:

28/02/2017

موجز عن البحث:

 

The Prediction by Partial Matching (PPM) compression algorithm is considered one of the most efficient methods for compressing natural language text. Despite the advances of the PPM method for the English language to predict upcoming symbols or words, more research is required to devise better compression methods for other languages, such as Arabic due, for example, to the rich morphological nature of the Arabic text, where a word can take many different forms. In this paper, we propose a new method that achieves the best compression rates not only for Arabic text but also for other languages that use Arabic script in their writing system such as Persian. Our word-based method constructs a context-free grammar (CFG) for the text and this grammar is then encoded using PPM to achieve excellent compression rates.