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Gujarati is the language used for everyday communication in the state of Gujarat, India. The Gujarati language is also officially recognized by the constitution and the government of India. Gujarati script is based on the Devanagari script. An idiom is an expression, phrase, or word that has a different meaning from the literal meaning of the words in it. Idioms represent the cultural heritage of Gujarati language. Idioms are used in Gujarati language for effective communication and convey of an accurate message. No Machine Translation System does the accurate translation of Gujarati idioms to English or any other language. Different idiom phrases can be generated by adding diacritic(s) as well as suffix to the root or base form of the idiom. Many forms of single idiom make automatic idiom identification as well as machine translation more challenging. This paper focuses on the design and implementation of diacritics and suffix-based rules for dynamic phrase generation and detection of idioms of Gujarati language. This implementation helps in identifying Gujarati idiom present in any possible form in the Gujarati text. The obtained results with the execution of 7050 different Gujarati idiom phrases yield an accuracy of 99.73%. The results are encouraging enough to make the proposed implementation useful for Natural Language processing tasks related to Gujarati language idioms.

Automatic Arabic diacritization is one of the most important and challenging problems in Arabic natural language processing (NLP). Recurrent neural networks (RNNs) have proved recently to achieve state-of-the-art results for sequence transcription problems in general, and Arabic diacritization in specific. In this work, we investigate the effect of varying the size of the training corpus on the accuracy of diacritization. We produce a cleaned corpus of approximately 550k sequences extracted from the full dataset of Tashkeela and use subsets of this corpus in our training experiments. Our base model is a deep bidirectional long short-term memory (BiLSTM) RNN that transcribes undiacritized sequences of Arabic letters with fully diacritized sequences. Our experiments show that error rates improve as the size of training corpus increases. Our best performing model achieves average diacritic and word error rates of 1.45% and 3.89%, respectively. When compared with state-of-the-art diacritization systems, we reduce the word error rate by 12% over the best published results.

Syntax plays a key role in natural language processing, but it does not always occupy an important position in applications. The main objective of this article is to solve the problem of the grammatical case ending errors produced by Arabic learners or certain common errors. Arabic can be considered more complex than English or French. He does not have vowels; diacritic signs (vowels) are placed above or below the letters. These diacritic signs are abandoned in most Arabic texts. This induces both grammatical and lexical ambiguities in Arabic. The present paper describes an automatic correction of this type of errors using “Stanford Parser” with an ontology containing the rules of the Arabic language. We segment the text into sentences, then we extract the annotations of each word with the syntactic relations coming from our parser, then we treat the relations obtained with our ontology. Finally, we compare the original sentence with the corrected one in order to detect the error. The implemented system achieved a total detection of about 94%. It is concluded that the approach is clearly promising by observing the results as compared to the limited number of available Arabic grammar checkers.