This study aims to compare the effectiveness of two popular machine translation systems (Google Translate and Babylon machine translation system) used to translate English sentences into Arabic relative to the effectiveness of English to Arabic human translation. There are many automatic methods used to evaluate different machine translators, one of these methods; Bilingual Evaluation Understud...

This article aims to investigate and evaluate the translation of verb-noun collocation in English into Arabic Google and Bing online translation engines. A number of sentences were used as a testing dataset to evaluate both engines. Human translations by three bilingual speakers were used as a gold standard. A simple evaluation metric was proposed to calculate the translation accuracy of verb-n...

Authors of that paper proposed a prototype machine translator system to translate scientific English sentences into Arabic sentences. This system is based on natural language processing and machine learning. This proposed system is applied in statistical field, which is very important on a mathematical sub field in Math department. The system is analyzed, designed and developed. Author tested t...

This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. No parallel text is needed after the training phase. Monolingual, unannotated text can be used to further improve the stemmer by ...

This paper describes the development of an intelligent computer-assisted language learning (ICALL) system for learning Arabic. This system could be used for learning Arabic by students at primary schools or by learners of Arabic as a second or foreign language. It explores the use of Natural Language Processing (NLP) techniques for learning Arabic. The learners are encouraged to produce sentenc...

Feature-based approaches play an important role and are widely applied in extractive summarization. In this paper, we use particle swarm optimization (PSO) to evaluate the effectiveness of different state-of-the-art features used to summarize Arabic text. The PSO is trained on the Essex Arabic summaries corpus data to determine the best particle that represents the most appropriate simple/combi...

This article describes our proposed system named LIM-LIG. This system is designed for SemEval 2017 Task1: Semantic Textual Similarity (Track1). LIM-LIG proposes an innovative enhancement to word embedding-based model devoted to measure the semantic similarity in Arabic sentences. The main idea is to exploit the word representations as vectors in a multidimensional space to capture the semantic ...

We examined how letter position coding is achieved in a script (Arabic) in which the different letter forms (i.e., allographs) may vary depending on their position within the letter string (e.g., compare the same-ligation pair [see text] and [see text] vs. the different-ligation pair [see text] and [see text]. To that end, we conducted an experiment in Uyghur, an agglutinative language from the...

The aim of the current work is to see how well existing techniques for textual entailment work when applied to Arabic, and to propose extensions which deal with the specific problems posed by the language. Arabic has a number of characteristics, described below, which make it particularly challenging to determine the relations between sentences. In particular, the lack of diacritics means that ...

In Arabic-to-English phrase-based statistical machine translation, a large number of syntactic disfluencies are due to wrong long-range reordering of the verb in VSO sentences, where the verb is anticipated with respect to the English word order. In this paper, we propose a chunk-based reordering technique to automatically detect and displace clause-initial verbs in the Arabic side of a word-al...