Statistical studies in Arabic linguistics
Computers and the Arabic language
Morphology and syntax of the Arabic language
Computers and the Arabic language
A comprehensive Arabic morphological analyser generator
Computers and the Arabic language
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Artificial Neural Networks
Self-Organizing Maps
Multitiered nonlinear morphology using multitape finite automata: a case study on Syriac and Arabic
Computational Linguistics - Special issue on finite-state methods in NLP
On abstract finite-state morphology
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Nonconcatenative finite-state morphology
EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
Formalisms for morphographemic description
EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
Formalisms for morphographemic description
EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
Multi-tape two-level morphology: a case study in semitic non-linear morphology
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Constructing lexical transducers
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Arabic finite-state morphological analysis and generation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
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Most of the morphological properties of derivational Arabic words are encapsulated in their corresponding morphological patterns. The morphological pattern is a template that shows how the word should be decomposed into its constituent morphemes (prefix + stem + suffix), and at the same time, marks the positions of the radicals comprising the root of the word. The number of morphological patterns in Arabic is finite and is well below 1000. Due to these properties, most of the current analysis algorithms concentrate on discovering the morphological pattern of the input word as a major step in recognizing the type and category of the word. Unfortunately, this process is non-determinitic in the sense that the underlying search process may sometimes associate more than one morphological pattern with the given word, all of them satisfying the major lexical constraints. One solution to this problem is to use a collection of connectionist pattern associaters that uniquely associate each word with its corresponding morphological pattern. This paper describes an LVQ-based learning pattern association system that uniquely maps a given Arabic word to its corresponding morphological pattern, and therefore deduces its morphological properties. The system consists of a collection of hetroassociative models that are trained using the LVQ algorithm plus a collection of autoassociative models that have been trained using backpropagation. Experimental results have shown that the system is fairly accurate and very easy to train. The LVQ algorithm has been chosen because it is very easy to train and the implied training time is very small compared to that of backpropagation.