Adaptive models of Arabic text

The main aim of this thesis is to build adaptive language models of Arabic text that can achieve the best compression performance over existing models. Prediction by partial matching (PPM) language models has been the best performing over the other adaptive language models through the past three decades in term of compression performance. In order to get such performance for Arabic text, the rich morphological nature of Arabic language should be taken into consideration. In this thesis, two new resources of Arabic language have been introduced for understanding the nature of Arabic language and standardizing the experiments on Arabic text. The first is a new corpus, the Bangor Arabic Compression Corpus (BACC), for standardizing compression experiments and creating a benchmark corpus for future compression experiments on Arabic text. The second is a new corpus, Bangor Balanced Corpus of Contemporary Arabic (BBCCA), The purpose of this corpus is to mirror similar balanced corpora that are available for the English language (Brown and LOB) but instead comprises the Arabic language. Two new adaptive models, BS-PPM and CS-PPM, based on the Prediction by Partial Matching (PPM) compression scheme are then introduced to improve the compression performance of standard PPM model by using preprocessing techniques. The first model works by replacing the most