Khaled Shaalan

Arabic is rooted in the Classical or Qur'anical Arabic, but over the centuries, the language has developed to what is now accepted as Modern Standard Arabic (MSA). Arab colloquial dialects are generally only spoken languages, but recently the rate of colloquial written text increases dramatically as a medium of expressing ideas especially across the WWW, usually in the form of blogs and partially colloquial articles. Most of these written colloquial has been in the Egyptian colloquial dialect, which is considered the most widely dialect understood and used throughout the Arab world. We are able to reuse MSA processing tools with colloquial Arabic by transferring colloquial Arabic words into their corresponding MSA words. The advantages of this lexical transfer are to facilitate the communication with colloquial Arabic speakers and restoring it to the standard language in use nowadays. This paper addresses the transfer techniques between colloquial Arabic and MSA, which have not yet been closely studied before. In particular, we present a rule-based lexical transfer approach for converting Egyptian colloquial words into their corresponding MSA words. This process involves morphological analysis and lexical acquisition of colloquial words.

Arabic is a strongly structured and highly derivational language. Arabic morphology and syntax provide the ability to add a large number of affixes to each word which makes combinatorial increment of possible words. In Arabic, passive voice is used as a writing style when: 1) the subject is unknown, 2) the subject is unimportant enough to be mentioned, or 3) the author wants to highlight the object. In this paper, the issues related to the recognition of the Arabic passive verbs which impact the automated understanding of Arabic sentences were addressed. An experiment using the Buckwalter Arabic morphological analyzers, one of the mature Arabic morphological analyzer, were conducted in order to highlight the limitations in the analysis of Arabic passive verbs. Results indicated that there exists a need for handling the problems related to the morphological analysis of passive verbs in order to improve the recognition accuracy of Arabic words.

In this paper, we describe a grammar-based generation approach for task-oriented interlingua-based spoken dialogue that transforms a shallow semantic interlingua representation called Interchange Format (IF) into Arabic Text that corresponds to the intentions underlying the speakers' utterances. The generation approach is developed primarily within the framework of the NESPOLE! (NEgotiating through SPOken Language in E-commerce) multilingual speech-to-speech MT project. The IF-to-Arabic generator is implemented in SICStus Prolog. We conducted an evaluation experiment using the output from the English analyzer provided by Carnegie Mellon University (CMU). The results of this experiment were promising and assured the ability of the generation approach in generating Arabic text form the interlingua taken from the travel and tourism domain.

This paper addresses the development of an automated lexical error diagnosis system, which helps Arabic second language learners to learn well-formed weak verbs. The learners are encouraged to produce input freely in various situations and contexts and guided to recognize by themselves the erroneous or inappropriate functions of their misused expressions. In this system, we successfully used constraint relaxation and edit-distance techniques to provide error-specific diagnosis and feedback to second language learners of Arabic. We demonstrated the capabilities of these techniques to diagnose errors related to the Arabic weak verb which is formed using complex morphological rules. Furthermore, the developed system allows for individualization of the learning process by providing feedback that conforms to the learner’s expertise. Inexperienced learners might require detailed instruction while experienced learners benefit from higher level reminders and explanations.