The rise of social media platforms makes it a valuable information source of recent events and users’ perspective towards them. Twitter has been one of the most important communication platforms in recent years. Event detection, one of the information extraction aspects, involves identifying specified types of events in the text. Detecting events from tweets can help to predict real-world events precisely. A serious challenge that faces Arabic event detection is the lack of Arabic datasets that can be exploited in detecting events. This paper will describe FloDusTA, which is a dataset of tweets that we have built for the purpose of developing an event detection system. The dataset contains tweets written in both Modern Standard Arabic and Saudi dialect. The process of building the dataset starting from tweets collection to annotation by human annotators will be present. The tweets are labeled with four labels: flood, dust storm, traffic accident, and non-event. The dataset was tested for classification and the result was strongly encouraging.

L’objectif de cet article est la présentation d’un système de génération automatique de dictionnaires électroniques de la langue arabe classique, développé au sein de laboratoire UTIC (unité de Monastir). Dans cet article, nous présenterons, les différentes étapes de réalisation, et notamment la génération automatique de ces dictionnaires se basant sur une théorie originale : les Conditions de Structures Morphomatiques (CSM), et les matrices lexicales. Ce système rentre dans le cadre des deux projets MIRTO et OREILLODULE réalisés dans les deux laboratoires LIDILEM de Grenoble et UTIC Monastir de Tunisie