Arabic beyond Arabic

Arabic is the best and the most complicated language of all time!” Even though this statement seems like an exaggeration, it is what I grew up hearing; from my Arab parents as a child, my Arab teachers as a student, and my Arab customers as a salesperson. The Arabic language has a significant role in Arabs’ identity, yet most Arabs only scratch the surface and do not fully grasp the embedded meaning of the language. I have long been fascinated by the relationship between language and community as well as how it translates to design - specifically, the relationship between Arabs and Arabic. When it comes to designing with Arabic, most of the spotlight is directed towards Calligraphy; in a lot of cases the Arabic language is not considered a priority but is applied to a design as a secondary element. The inherited pride that Arabs have towards their language is immense, yet the design language does not match it in complexity. The strong connection that most Arabs have towards Arabic results in disagreements amongst each other regarding the linguistics of the language. This thesis aims to uncover these behaviors and connections with the language by taking a critical design approach using interaction design to reveal hidden and apparent features of Arabic. This research encourages questioning current design methods and proposes alternative approaches by taking Arabic beyond its stereotypical aesthetical value and over onto its linguistic and behavioral significance

South Arabian and Yemeni dialects

It has traditionally been assumed that with the Islamic conquests Arabic overwhelmed the original ancient languages of the Peninsula, leaving the language situation in the south-western Arabian Peninsula as one in which dialects of Arabic are tinged, to a greater or lesser degree, with substrate features of the ancient South Arabian languages. The ancient Arab grammarians had clear ideas concerning the difference between the non-Arabic languages of the Peninsula and Arabic, including the -t feminine nominal ending in all states and -n versus the -l definite article.. Today, however, we read about ‘Arabic’ dialects that exhibit large proportions of ‘non-Arabic’ features. Here I compare phonological, morphological, lexical and syntactic data from several contemporary varieties spoken within historical Yemen – within the borders of current Yemen into southern ‘Asīr – with data from Ancient South Arabian, Sabaean, and Modern South Arabian, Mehri, as spoken in the far east of Yemen. On the basis of these comparisons I suggest that Arabic may not have replaced all the ancient languages of the Peninsula, and that we may be witnessing the rediscovery of descendants of the ancient languages. The Yemeni and ‘Asīri dialects considered are: Yemen: Rāziḥīt, Minabbih, Xašir, San‘ani, Ġaylħabbān ‘Asīr: Rijāl Alma‛, Abha, Faif

Unconstrained Scene Text and Video Text Recognition for Arabic Script

Building robust recognizers for Arabic has always been challenging. We demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid architecture in recognizing Arabic text in videos and natural scenes. We outperform previous state-of-the-art on two publicly available video text datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a new Arabic scene text dataset and establish baseline results. For scripts like Arabic, a major challenge in developing robust recognizers is the lack of large quantity of annotated data. We overcome this by synthesising millions of Arabic text images from a large vocabulary of Arabic words and phrases. Our implementation is built on top of the model introduced here [37] which is proven quite effective for English scene text recognition. The model follows a segmentation-free, sequence to sequence transcription approach. The network transcribes a sequence of convolutional features from the input image to a sequence of target labels. This does away with the need for segmenting input image into constituent characters/glyphs, which is often difficult for Arabic script. Further, the ability of RNNs to model contextual dependencies yields superior recognition results.Comment: 5 page

Greek-Arabic-Latin: The transmission of mathematical texts in the Middle Ages

During the Middle Ages many Greek mathematical and astronomical texts were translated from Greek into Arabic (ca. ninth century) and from Arabic into Latin (ca. twelfth century). There were many factors complicating the study of them, such as translation from or into other languages, redactions, multiple translations, and independently transmitted scholia. A literal translation risks less in loss of meaning, but can be clumsy. This article includes lists of translations and a large bibliography, divided into sections

An Evaluation of Arabic Language Learning Websites

As a result of ICT development and the increasingly growing use of the Internet in particular, practices of language teaching and learning are about to evolve significantly. Our study focuses on the Arabic language, and aims to explore and evaluate Arabic language learning websites. To reach these goals, we propose in a first step, to define an evaluation model, based on a set of criteria for assessing the quality of websites dedicated to teaching and learning Arabic. We subsequently apply our model on a set of Arabic sites available on the web and give an assessment of these web sites. We finally discuss their strengths and limitations.Comment: International Conference on Education and E-Learning Innovation

An automatically built named entity lexicon for Arabic

We have successfully adapted and extended the automatic Multilingual, Interoperable Named Entity Lexicon approach to Arabic, using Arabic WordNet (AWN) and Arabic Wikipedia (AWK). First, we extract AWN’s instantiable nouns and identify the corresponding categories and hyponym subcategories in AWK. Then, we exploit Wikipedia inter-lingual links to locate correspondences between articles in ten different languages in order to identify Named Entities (NEs). We apply keyword search on AWK abstracts to provide for Arabic articles that do not have a correspondence in any of the other languages. In addition, we perform a post-processing step to fetch further NEs from AWK not reachable through AWN. Finally, we investigate diacritization using matching with geonames databases, MADA-TOKAN tools and different heuristics for restoring vowel marks of Arabic NEs. Using this methodology, we have extracted approximately 45,000 Arabic NEs and built, to the best of our knowledge, the largest, most mature and well-structured Arabic NE lexical resource to date. We have stored and organised this lexicon following the Lexical Markup Framework (LMF) ISO standard. We conduct a quantitative and qualitative evaluation of the lexicon against a manually annotated gold standard and achieve precision scores from 95.83% (with 66.13% recall) to 99.31% (with 61.45% recall) according to different values of a threshold