The Role of Using Islamic English in Solving the Difficulties in Translating Nobel Quran and Unification of Muslims

The problem with translating the Quran can be divided into translating the lingual form and the meaning. The meaning is the biggest problem because the Quran is not just another book; it is a book that is always understood differently by the readers. Several researchers emphasized that translation of the meanings of noble Quran to other languages is impossible in the same accuracy as Arabic. Words could be translated literally, but it is difficult to translate what those words mean deeply. The translation will make the meaning weaker and sometimes it changes it. Because of such difficulties, the translators of holy Quran create Islamic English; they expressed the Islamic nouns in its meanings without distortion, so several vocabularies have been appeared in English. This study aimed at investigating the role of Islamic English in solving the difficulties in translating noble Quran and unification of Muslims, It attempted to answer what is the role of Islamic English in solving the difficulties in translating and what is the role of Islamic English in unification of Muslims. The researcher prepared a questionnaire divided into two sections: the first one talks about the role of Islamic English in solving the difficulties in translating and the second one is about the role of Islamic English in unification of Muslims. The sample was chosen from English Department in the second semester of the academic year 2011. To establish the validity for the questionnaire, the method of content validity was used. It was given to a jury of specialists. The reliability also was established. The results revealed that Islamic English contributes positively in solving the difficulties in translating noble Quran and unification of Muslims. On the basis of the results of the present study, the researcher proposed a number of recommendations and suggestions for future research. Keywords: Quran, Islamic English, unificatio

New approach for Arabic named entity recognition on social media based on feature selection using genetic algorithm

Many features can be extracted from the massive volume of data in different types that are available nowadays on social media. The growing demand for multimedia applications was an essential factor in this regard, particularly in the case of text data. Often, using the full feature set for each of these activities can be time-consuming and can also negatively impact performance. It is challenging to find a subset of features that are useful for a given task due to a large number of features. In this paper, we employed a feature selection approach using the genetic algorithm to identify the optimized feature set. Afterward, the best combination of the optimal feature set is used to identify and classify the Arabic named entities (NEs) based on support vector. Experimental results show that our system reaches a state-of-the-art performance of the Arab NER on social media and significantly outperforms the previous systems

Using Comparable Corpora to Augment Statistical Machine Translation Models in Low Resource Settings

Previously, statistical machine translation (SMT) models have been estimated from parallel corpora, or pairs of translated sentences. In this thesis, we directly incorporate comparable corpora into the estimation of end-to-end SMT models. In contrast to parallel corpora, comparable corpora are pairs of monolingual corpora that have some cross-lingual similarities, for example topic or publication date, but that do not necessarily contain any direct translations. Comparable corpora are more readily available in large quantities than parallel corpora, which require significant human effort to compile. We use comparable corpora to estimate machine translation model parameters and show that doing so improves performance in settings where a limited amount of parallel data is available for training. The major contributions of this thesis are the following: * We release ‘language packs’ for 151 human languages, which include bilingual dictionaries, comparable corpora of Wikipedia document pairs, comparable corpora of time-stamped news text that we harvested from the web, and, for non-roman script languages, dictionaries of name pairs, which are likely to be transliterations. * We present a novel technique for using a small number of example word translations to learn a supervised model for bilingual lexicon induction which takes advantage of a wide variety of signals of translation equivalence that can be estimated over comparable corpora. * We show that using comparable corpora to induce new translations and estimate new phrase table feature functions improves end-to-end statistical machine translation performance for low resource language pairs as well as domains. * We present a novel algorithm for composing multiword phrase translations from multiple unigram translations and then use comparable corpora to prune the large space of hypothesis translations. We show that these induced phrase translations improve machine translation performance beyond that of component unigrams. This thesis focuses on critical low resource machine translation settings, where insufficient parallel corpora exist for training statistical models. We experiment with both low resource language pairs and low resource domains of text. We present results from our novel error analysis methodology, which show that most translation errors in low resource settings are due to unseen source language words and phrases and unseen target language translations. We also find room for fixing errors due to how different translations are weighted, or scored, in the models. We target both error types; we use comparable corpora to induce new word and phrase translations and estimate novel translation feature scores. Our experiments show that augmenting baseline SMT systems with new translations and features estimated over comparable corpora improves translation performance significantly. Additionally, our techniques expand the applicability of statistical machine translation to those language pairs for which zero parallel text is available