Search

Over the last two decades, many efforts have been made to provide resources to support the Arabic Natural Language Processing (NLP). Some of these resources target specific NLP tasks such as word tokenization, parsing, or sentiment analysis, while others attempt to tackle numerous tasks at once. In this paper, we present ¡¡TTK¿¿, a toolkit for Tunisian linguistic analysis. It consists of a collection of linguistic analysis tools for orthographic normalization, sentence boundaries detection, word tokenization, morphological analysis, parsing and named entity recognition. This paper focuses on the design and implementation of TTK tools. • NLP tools give us a better understanding of how the language may work in specific situations. • TTK contains text processing tools from orthographic normalization to comprehension. • All the proposed and used tools showed encouraging results. • All the proposed and used tools trait all the three forms of Tunisian dialect. [ABSTRACT FROM AUTHOR]

In English Medium Instruction (EMI), one of the biggest challenges is reportedly the teachers’ own lack of English language proficiency. Helping teachers to improve their proficiency while learning about pedagogy is critical to achieving reasonable success in EMI. This article is contextualized in an English language teacher education program conducted in Tunisia. Specifically, I zoom in on a training task that had trainees reading an academic textbook and posting takeaways on an asynchronous platform over a four-week period. The corpus, comprising 50 journal entries produced by five teacher trainees, was analyzed, first using automated tools for natural language processing and then through human coding, for a combination of quantitative and qualitative perspectives, and with a view to deriving a comprehensive understanding of learning as manifested on multiple levels - psychological, cognitive, and linguistic. Results show impressive learning gains both in content and language. I conclude by discussing the findings and implications for EMI. [ABSTRACT FROM AUTHOR]

For many years, mental health has been hidden behind a veil of shame and prejudice. In 2017, studies claimed that 10.7% of the global population suffered from mental health disorders. Recently, people started seeking relaxing treatment through technology, which enhanced and expanded mental health care, especially during the COVID-19 pandemic, where the use of mental health forums, websites, and applications has increased by 95%. However, these solutions still have many limits, as existing mental health technologies are not meant for everyone. In this work, an up-to-date literature review on state-of-the-art of mental health and healthcare solutions is provided. Then, we focus on Arab-speaking patients and propose an intelligent tool for mental health intent recognition. The proposed system uses the concepts of intent recognition to make mental health diagnoses based on a bidirectional encoder representations from transformers (BERT) model and the International Neuropsychiatric Interview (MINI). Experiments are conducted using a dataset collected at the Military Hospital of Tunis in Tunisia. Results show excellent performance of the proposed system (the accuracy is over 92%, the precision, recall, and F1 scores are over 94%) in mental health patient diagnosis for five aspects (depression, suicidality, panic disorder, social phobia, and adjustment disorder). In addition, the tool was tested and evaluated by medical staff at the Military Hospital of Tunis, who found it very interesting to help decision-making and prioritizing patient appointment scheduling, especially with a high number of treated patients every day. [ABSTRACT FROM AUTHOR]