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Teahan, William J.
- Preprocessing for PPM: Compressing UTF-8 Encoded Natural Language Text
Abstract Views :178 |
PDF Views:123
Authors
Affiliations
1 School of Computer Science, University of Wales, Bangor, GB
2 School of Computes and Information Technology, University of Tabuk, SA
1 School of Computer Science, University of Wales, Bangor, GB
2 School of Computes and Information Technology, University of Tabuk, SA
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 7, No 2 (2015), Pagination: 41-51Abstract
In this paper, several new universal preprocessing techniques are described to improve Prediction by Partial Matching (PPM) compression of UTF-8 encoded natural language text. These methods essentially adjust the alphabet in some manner (for example, by expanding or reducing it) prior to the compression algorithm then being applied to the amended text. Firstly, a simple bigraphs (two-byte) substitution technique is described that leads to significant improvement in compression for many languages when they are encoded by the Unicode scheme (25% for Arabic text, 14% for Armenian, 9% for Persian, 15% for Russian, 1% for Chinese text, and over 5% for both English and Welsh text). Secondly, a new preprocessing technique that outputs separate vocabulary and symbols streams - that are subsequently encoded separately - is also investigated. This also leads to significant improvement in compression for many languages (24% for Arabic text, 30% for Armenian, 32% for Persian and 35% for Russian). Finally, novel preprocessing and postprocessing techniques for lossy and lossless text compression of Arabic text are described for dotted and non-dotted forms of the language.Keywords
Preprocessing, PPM, UTF-8, Encoding.Full Text
- Grammar-based Pre-processing for PPM
Abstract Views :229 |
PDF Views:124
Authors
Affiliations
1 Department of Computer Science, University of Bangor Bangor, GB
2 Department of Computer Science, University of Tabuk, Tabuk, SA
1 Department of Computer Science, University of Bangor Bangor, GB
2 Department of Computer Science, University of Tabuk, Tabuk, SA
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 9, No 1 (2017), Pagination: 1-11Abstract
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.Keywords
CFG, Grammar-Based, Preprocessing, PPM, Encoding.Full Text
References
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- Gender and Authorship Categorisation of Arabic Text from Twitter Using PPM
Abstract Views :258 |
PDF Views:115
Authors
Affiliations
1 Department of Computer Sciences And Engineering, University of Hail, SA
2 Department of Computer Science, University of Bangor, Bangor, GB
1 Department of Computer Sciences And Engineering, University of Hail, SA
2 Department of Computer Science, University of Bangor, Bangor, GB
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 9, No 2 (2017), Pagination: 131-140Abstract
In this paper we present gender and authorship categorisationusing the Prediction by Partial Matching (PPM) compression scheme for text from Twitter written in Arabic. The PPMD variant of the compression scheme with different orders was used to perform the categorisation. We also applied different machine learning algorithms such as Multinational Naïve Bayes (MNB), K-Nearest Neighbours (KNN), and an implementation of Support Vector Machine (LIBSVM), applying the same processing steps for all the algorithms. PPMD shows significantly better accuracy in comparison to all the other machine learning algorithms, with order 11 PPMD working best, achieving 90 % and 96% accuracy for gender and authorship respectively.Keywords
Arabic Text Categorisation, Data Compression, Machine Learning Algorithms.Full Text
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