International Journal of Computer Processing of Languages

This paper presents a novel Arabic morphological generator (AMG) for Modern Standard Arabic (MSA) which is designed and implemented using Prolog. The AMG is used to generate inflected forms of words used for Arabic audio indexing. These words are also the relevant terms in the Arabic authority system (library information retrieval system) used in this study. The AMG generates inflected Arabic words from the root according to pre-specified morphological features that can be extended as needed. The Arabic word is represented as a feature structure which is handled through unification during the morphological generation process. The inflected forms can then be inserted automatically into a speech recognition grammar which is used to identify these words in an audio sequence or utterance.

The performance difference between DSK and Qwerty keyboards is evaluated on the basis of the record of typing contest awards. The resulting estimation with some assumptions is that the expected speed gain of DSK over Qwerty is at least more than 10%. Besides the evaluation, a speed review of some ergonomic studies in the past keyboard performances is also presented and the conclusion is that the design of an optimal human-interface requires the knowledge about human factors.

Research on semantic processing of Arabic language and in particular on deep semantic analysis considering the compositionality of its syntactical structure has so far received very little computational research. This work is therefore attempting to fill some essential aspects of this gap in introducing a logic-based compositional model covering fundamental issues involved in semantic analysis of Arabic sentences. The focus of attention is relying on studying the compositionality of important Arabic syntactical constituents and on extending the concept of the generalized natural language quantification to Generalized Arabic Quantifiers GAQ utilizing lambda-calculus and the type theoretical analysis of Arabic structure. Since semantic representation has to be compositional in natural language understanding systems this approach attempts to propose an element framework for developing more practical and intelligent Arabic natural language processing systems.

Computer processing of off-line Arabic handwriting is very difficult. The cursiveness of the script and the almost variability of the handwritten symbols make the automatic recognition of Arabic a very challenging task. Character segmentation is an important pre-processing stage in any intelligent character recognition system. This paper surveys the field of off-line Arabic character segmentation. Four classes of segmentation approaches are identified based on the used features and on where segmentation points are located. Text curves analysis, outer contour detection and following, stroke scrutiny and singularities vs regularities are the most used techniques. Instructive examples of each category are described and some comments are given.