Improvement of Mahārat al-Kalām in Arabic Learning through Total Physical Response Method

The Total Physical Response (TPR) learning method was developed by James J. Asher. He argues that direct pronunciation to children or students contains an order, and then children or students will respond physically before producing verbal or speech responses. The aims of this study were (1) to find out how far speaking skills (mahārat al-kalām) in Arabic have increased through the TPR method in MTsN 4 Jombang, and (2) to find out the supporting and inhibiting factors in increasing mahārat al-kalām through the TPR method in MTsN 4 Jombang. This research method uses a descriptive-qualitative approach with data collection techniques using observation, interviews, documentation, and oral tests. The data analysis technique uses data condensation, data display, and conclusion drawing or verification. The results showed that (1) with the TPR learning method, the final test score of Arabic learning for mahārat al-kalām in cycle 1 was 74.3% (good). After being given referrals again in cycle 2, it reached 87.6% (very good); (2) The supporting factors that influence speaking activities are language (linguistics) and non-linguistics, while the inhibiting factors are more dominant due to a lack of vocabulary (mufradāt) mastery

Named entity recognition for quranic text using rule based approaches

The variety and difference between domains for textual data require customization in the Natural Language Processing component especially in Named Entity Recognition where different domains contain several types of entities. The current NER model is deemed not fit to accurately extract entities from Quranic text due to its unique content. This paper describes the building of a rule-based Named Entity Recognition method to extract the entities that exist in the English translation to the meaning of the Quranic text and its performance evaluation. Named entity tagging, a common task in-text annotation, in which entities (nouns) in the unstructured text are identified and assigned a class. A few rules are built to extract several types of entities such as the name of prophets and people, creation, location, time, and the various names of God. The rules are built mainly using regular expressions and gazetteers. The rules that have been built result in high precision and recall as well as a satisfactory F-score of over 90%. The results from this experiment can be used as annotation in building a machine learning model to extract entities from the same type of domain specifically on the Quranic text or generally in the Islamic domain text

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

Paris dans les récits de voyage d’écrivains arabes : repérage, analyse sémantique et cartographie de toponymes

À la croisée du traitement du langage naturel, des études littéraires et des humanités spatiales, nous présentons dans cet article une approche pour cartographier les modalités sémantiques positives ou négatives associées aux noms de lieux dans des textes en arabe. La chaîne de traitement comprend le repérage des entités nommées de lieu, l’analyse sémantique de leur contexte (opinions, émotions et sentiments), ainsi que la cartographie de leurs instances sur des cartes géographiques. Notre corpus de travail comprend six récits de voyage à Paris de grands écrivains arabes des xixe et xxe siècles. Des approches à base de règles et à base d’apprentissage automatique ont été expérimentées et évaluées pour le repérage des entités nommées de lieu et pour l’analyse sémantique. Les résultats de notre étude permettent de confirmer l’apport de cette méthode automatique pour la recherche littéraire, en contribuant à une étude sémantique de vaste ampleur.We present in this paper an automated method to map out positive or negative semantic modalities associated with place names in Arabic travelogue literature. This research sits at the crossroads of Natural Language Processing, Literary Studies, and Digital Humanities. Our pipeline identifies place named entities, analyzes their semantic context (with regard to opinions, sentiments and emotions), and locates the place names on geographic maps. Our corpus includes six travel writings on Paris from some of the most influential Arab writers of the 19th and 20th centuries. We evaluate rule-based and machine-learning approaches for their efficacy in named entity recognition and semantic analysis. The results of our automated analysis confirm, to a great extent, the judgements and interpretations of traditional critical scholarship on these Arabic literary texts