The dynamic of Arabic academic writing in postgraduate program: linguistic corpus analysis

This descriptive library research describes the active research topics and methods most interested postgraduate students majoring in Arabic in master's and doctoral degrees. It explains the representation of research dynamic map in Arabic learning supported by corpus linguistic analysis. This research applied two research approaches; the quantitative approach was used to describe the frequency of words used by the postgrad students in their academic writings analyzed by Antconc software. A qualitative approach was used to explain the representation of corpus-based findings. The data are taken from abstracts of Arabic academic writings belonging to one of the Indonesian public universities’ digital libraries. The results showed that students' most popular research topics in the last five years are education topics, namely materials and textbooks development; second, linguistics topics such as naḥwu and tarkīb, and language skills is mahārah kalam. This study shows that students tend to research naḥwu and tarkīb based on speaking skills material. The most widely used students research method was Research and Development (R & R&D). Students tend to develop teaching materials with various perspectives, as R&D was a trend of research method. As a result, students are expected to gain an overview of the dynamic discourse map and research trends in Arabic learning as a foundation or preliminary research to select and determine research topics and methods for the final project. To avoid similarity, plagiarism, and saturation, one must choose variations in topics and types of research methods and follow existing trends

Ensemble Morphosyntactic Analyser for Classical Arabic

Classical Arabic (CA) is an influential language for Muslim lives around the world. It is the language of two sources of Islamic laws: the Quran and the Sunnah, the collection of traditions and sayings attributed to the prophet Mohammed. However, classical Arabic in general, and the Sunnah, in particular, is underexplored and under-resourced in the field of computational linguistics. This study examines the possible directions for adapting existing tools, specifically morphological analysers, designed for modern standard Arabic (MSA) to classical Arabic. Morphological analysers of CA are limited, as well as the data for evaluating them. In this study, we adapt existing analysers and create a validation data-set from the Sunnah books. Inspired by the advances in deep learning and the promising results of ensemble methods, we developed a systematic method for transferring morphological analysis that is capable of handling different labelling systems and various sequence lengths. In this study, we handpicked the best four open access MSA morphological analysers. Data generated from these analysers are evaluated before and after adaptation through the existing Quranic Corpus and the Sunnah Arabic Corpus. The findings are as follows: first, it is feasible to analyse under-resourced languages using existing comparable language resources given a small sufficient set of annotated text. Second, analysers typically generate different errors and this could be exploited. Third, an explicit alignment of sequences and the mapping of labels is not necessary to achieve comparable accuracies given a sufficient size of training dataset. Adapting existing tools is easier than creating tools from scratch. The resulting quality is dependent on training data size and number and quality of input taggers. Pipeline architecture performs less well than the End-to-End neural network architecture due to error propagation and limitation on the output format. A valuable tool and data for annotating classical Arabic is made freely available