Verbs in Arabic are formed from roots consisting of three or four radical letters. The Irregular verbs can be defined as verbs that act differently from the basic patterns in all or some cases; those verbs may act irregularly in the derived forms as well, irregular verbs pose a challenge to Machine Translation (MT), in particular in morphological and syntactical analysis, the definition of irregular verbs involves accounting doubled, hamzated and weak verbs. There are four categories of weak verbs depending on the position of the weak letter/vowels in the root (first, middle, last letter, or more than one letter). In this article, the authors have built a module to detect irregular verbs, ie, doubled, hamzated, mithal, hollow, defective, and enfolding. A set of rules have been conducted based on the tense of the verb, place of the vowel root-letter,(ie, first, second or third person representation), number, gender, tense and mood features

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Journal: International Journal of Advancements in Computing Technology Volume 5 Issue 15 Pages 1

Arabic stemming is a technique to find the stem or lexical root for Arabic words through the process of eliminating affixes (preffixes, infixes and suffixes) attached to their roots. Several approaches have been implemented to generate the stem of Arabic words according to a certain level of analysis, ie, root-based approach, stem-based approach and statistical approach. Arabic language is a Semitic language which means that it is a derivational rather than a concatinative language. In this study we designed and implemented an Arabic triliteral Morphological Analyser that is capable of analysing the classical and Modern Standard Arabic (MSA) effectively with the capability of analysing vowelised, semi-vowelised and nonvowelised text. The system is integratable with other applications so that vast number of people can get benefited from. One shortcomming for the developed system is that the output obtained

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Journal: Journal of Computer Science - Volume 11 / Issue 4 / Pages 627

In attempt to increase the rate of Arabic phonemes recognition, we introduce a novel hybrid recognition algorithm. The algorithm is composed of the learning vector quantization (LVQ) and hidden Markov model (HMM). The hybrid algorithm used to recognizing Arabic phonemes in continuous open-vocabulary speech. A recorded Arabic corpus of different TV news for modern standard Arabic was used for training and testing purposes. We employ a data driven approach to generate the training feature vectors that embed the frame neighboring correlation information. Next, we generate the phonemes codebooks using the K-means splitting algorithm. Then, we trained the generated codebooks using the LVQ algorithm. We achieved a performance of 98.49 % during independent classification training and 90 % during dependent classification training. When using the trained LVQ codebooks in Arabic utterance transcription, the phoneme recognition rate was 72 % using LVQ only. We combined the LVQ codebooks with the single state HMM model using enhanced Viterbi algorithm which includes the phonemes bigrams. We achieved 89 % of Arabic phonemes recognition rate based on the hybrid LVQ/HMM algorithm.

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Journal: International Journal of Speech Technology, Springer - Volume 19, Issue 3, pp 495–508

This paper examines the agreement and word-order tackling in Arabic MT systems. The paper represents an English to Arabic approach for translating well-structured English sentences into well-structured Arabic sentences using a new rules to handle the problems of ordering and agreement. The methodology is flexible and scalable, the main advantages are: first, it is a rule-based approach, and second, it can be applied on some other languages with minor modifications. The database has been designed to be flexible where most of the rules are defined in tables in order to generalize the code. Validation rules have been applied in both the database design and the programming code in order to ensure the integrity of data. A major design goal of this system is that it will be used as a stand-alone tool and can be very well integrated with a general machine translation system for English sentence.

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Journal: IEEE  - DOI: 10.1109/UKSIM.2010.20

Machine Translation has been defined as the process that utilizes computer software to translate text from one natural language to another[2].This definition involves accounting for the grammatical structure of each language and using rules and grammars to transfer the grammatical structure of the source language (SL) into the target language (TL). Agreement and Word-ordering play an important role with this structure, as MT should handle agreement between the subject and verb where the number gender, person and features of the subject are important factors in the derivation of the verb as well as the features of the verb itself. Other agreements are required between the adjective and the noun where Arabic adjectives depend on the number, gender and person as well as the definiteness and indefiniteness of the noun. Some other agreements also exist between the numbers and the countable nouns. This paper examines the agreement and word-order rules in tackling Arabic MT systems. Special attention is given to Tarjim by Sakhr Software Company which pioneered a new generation of Arabic Natural Language Processing (NLP) technologies. The approach adopted is flexible and scalable, and the main advantages are: first, it is a rule-based approach, and second, it can be applied on other languages with minor modifications. A major design goal of this system is that it can be used as a stand-alone tool and can be very well integrated with a general machine translation system for English sentence.

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Journal: IEEE - vol. 1, pp. 1-10, 2008