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Readability is a measure that associates a written text to a reader's skill or grade level. Readability assessment is very important in the field of second or foreign language (L2) learning. The complexity of reading in some languages, such as Arabic, presents a great challenge to overcome and increases the need for such a measure before giving texts to a learner. In order to develop powerful tools that automate this choice, we must first choose the optimal feature set to be used in the development of such a tool. In this paper, we present an approach to automatically measure the readability of Arabic as a foreign language through a series of experiments. We begin by using a wide range of features plausibly relevant to readability, as found in the literature, and reduce them in subsequent experiments by eliminating features that appear to have little significance in readability prediction. Our objective is to keep the smallest set of features that gives good readability prediction accuracy based on three different corpora for learners of Arabic as a foreign language, annotated with five difficulty levels. Our best L2 readability accuracy result is 86.15%. [ABSTRACT FROM AUTHOR]

Recently, more data-driven approaches are demanding multilingual parallel resources primarily in the crosslanguage studies. To meet these demands, building multilingual parallel corpora are becoming the focus of many Natural Language Processing (NLP) scientific groups. Unlike monolingual corpora, the number of available multilingual parallel corpora is limited. In this paper, the MulTed, a corpus of subtitles extracted from TEDx talks is introduced. It is multilingual, Part of Speech (PoS) tagged, and bilingually sentence-aligned with English as a pivot language. This corpus is designed for many NLP applications, where the sentencealignment, the PoS tagging, and the size of corpora are influential such as statistical machine translation, language recognition, and bilingual dictionary generation. Currently, the corpus has subtitles that cover 1100 talks available in over 100 languages. The subtitles are classified based on a variety of topics such as Business, Education, and Sport. Regarding the PoS tagging, the Treetagger, a language-independent PoS tagger, is used; then, to make the PoS tagging maximally useful, a mapping process to a universal common tagset is performed. Finally, we believe that making the MulTed corpus available for a public use can be a significant contribution to the literature of NLP and corpus linguistics, especially for under-resourced languages. [ABSTRACT FROM AUTHOR]

The focus of data scientists is essentially divided into three areas: collecting data, analyzing data, and inferring information from data. Each one of these tasks requires special personnel, takes time, and costs money. Yet, the next and the fastidious step is how to turn data into products. Therefore, this field grabs the attention of many research groups in academia as well as industry. In the last decades, data-driven approaches came into existence and gained more popularity because they require much less human effort. Natural Language Processing (NLP) is strongly among the fields influenced by data. The growth of data is behind the performance improvement of most NLP applications such as machine translation and automatic speech recognition. Consequently, many NLP applications are frequently moving from rule-based systems and knowledge-based methods to data-driven approaches. However, collected data that are based on undefined design criteria or on technically unsuitable forms will be useless. Also, they will be neglected if the size is not enough to perform the required analysis and to infer the accurate information. The chief purpose of this overview is to shed some lights on the vital role of data in various fields and give a better understanding of data in light of NLP. Expressly, it describes what happen to data during its life-cycle: building, processing, analyzing, and exploring phases. [ABSTRACT FROM AUTHOR]

Part of Speech (PoS) tagging is still not very well investigated with respect to the Arabic language. Determining the PoS tags of a word in a particular context is difficult, primarily because there is no use of diacritics in most of contemporary texts. Consequently, the same word may be spelled in different ways. Further, detecting the difference between Arabic derivatives represents a very challenging issue for the majority of PoS taggers. Hence, the task of tagging the correct PoS tags requires advanced processing and the use of considerable resources. This study aims to design detailed hierarchical levels of the Arabic tagset categories and their relationships. These hierarchical levels allow easier expansion when required and produce more accurate and precise results. They are based on a comparative study and important references in Arabic grammar; they are also validated by experts in this field. In addition, the proposed tagset is implemented in a PoS tagger and tested via various experiments. We believe that our study makes a significant contribution to the literature because this work is an advancement in the direction of achieving a standard, rich, and comprehensive tagset for Arabic. [ABSTRACT FROM AUTHOR]

AlKhalil Morpho Sys is a morphosyntactic analyzer of standard Arabic words taken out of context. The system analyzes either partially vowelized words or totally vowelized ones. In this paper, we present the second version of this analyzer. The correction of errors in the database of the first version, and enrichment of this database by missing data allowed us to develop a more accurate version with very high coverage since the percentage of analyzed words exceeds 99%. In addition, we have enriched the morphological features provided by this new version with the lemma tag of the word and its pattern, which are very useful in many applications of Arabic language processing. Furthermore, with the new organization of this database and the improvements brought to its source code, this new version produces very fast analysis. [ABSTRACT FROM AUTHOR]