Enhanced Arabic disaster data classification using domain adaptation.

Natural disasters, like pandemics and earthquakes, are some of the main causes of distress and casualties. Governmental crisis management processes are crucial when dealing with these types of problems. Social media platforms are among the main sources of information regarding current events and public opinion. So, they have been used extensively to aid disaster detection and prevention efforts. Therefore, there is always a need for better automatic systems that can detect and classify disaster data of social media. In this work, we propose enhanced Arabic disaster data classification models. The suggested models utilize domain adaptation to provide state-of-the-art accuracy. We used a standard dataset of Arabic disaster data collected from Twitter for testing the proposed models. Experimental results show that the provided models significantly outperform the previous state-of-the-art results.

Improving neural machine translation for low resource languages through non-parallel corpora: a case study of Egyptian dialect to modern standard Arabic translation.

Machine translation for low-resource languages poses significant challenges, primarily due to the limited availability of data. In recent years, unsupervised learning has emerged as a promising approach to overcome this issue by aiming to learn translations between languages without depending on parallel data. A wide range of methods have been proposed in the literature to address this complex problem. This paper presents an in-depth investigation of semi-supervised neural machine translation specifically focusing on translating Arabic dialects, particularly Egyptian, to Modern Standard Arabic. The study employs two distinct datasets: one parallel dataset containing aligned sentences in both dialects, and a monolingual dataset where the source dialect is not directly connected to the target language in the training data. Three different translation systems are explored in this study. The first is an attention-based sequence-to-sequence model that benefits from the shared vocabulary between the Egyptian dialect and Modern Arabic to learn word embeddings. The second is an unsupervised transformer model that depends solely on monolingual data, without any parallel data. The third system starts with the parallel dataset for an initial supervised learning phase and then incorporates the monolingual data during the training process.

Semantic textual similarity for modern standard and dialectal Arabic using transfer learning.

Semantic Textual Similarity (STS) is the task of identifying the semantic correlation between two sentences of the same or different languages. STS is an important task in natural language processing because it has many applications in different domains such as information retrieval, machine translation, plagiarism detection, document categorization, semantic search, and conversational systems. The availability of STS training and evaluation data resources for some languages such as English has led to good performance systems that achieve above 80% correlation with human judgment. Unfortunately, such required STS data resources are not available for many languages like Arabic. To overcome this challenge, this paper proposes three different approaches to generate effective STS Arabic models. The first one is based on evaluating the use of automatic machine translation for English STS data to Arabic to be used in fine-tuning. The second approach is based on the interleaving of Arabic models with English data resources. The third approach is based on fine-tuning the knowledge distillation-based models to boost their performance in Arabic using a proposed translated dataset. With very limited resources consisting of just a few hundred Arabic STS sentence pairs, we managed to achieve a score of 81% correlation, evaluated using the standard STS 2017 Arabic evaluation set. Also, we managed to extend the Arabic models to process two local dialects, Egyptian (EG) and Saudi Arabian (SA), with a correlation score of 77.5% for EG dialect and 76% for the SA dialect evaluated using dialectal conversion from the same standard STS 2017 Arabic set.

Integrated clinical and specialty pharmacy practice model for management of patients with multiple sclerosis.

PURPOSE: An integrated clinical and specialty pharmacy practice model for the management of patients with multiple sclerosis (MS) is described. SUMMARY: Specialty medications, such as disease-modifying therapies (DMTs) used to treat MS, are costly and typically require special administration, handling, and storage. DMTs are associated with high rates of nonadherence and may have associated safety risks. The University of Illinois Hospital and Health Sciences System developed an MS pharmacy practice model that sought to address the many challenges of coordinating care with multiple entities outside the health system. Several key features of the integrated model include a dedicated clinical pharmacist on the MS specialty team, an integrated specialty pharmacy service, direct access to the electronic medical record, and face-to-face interaction with patients. Through the active involvement of the neurology clinical pharmacist and an onsite specialty pharmacy service, targeted assessments and medication and disease education are provided to the patient before DMT initiation and maintained throughout therapy. In addition, the regular point of contact and refill coordination encourages improved compliance, appropriate medication use, ongoing safety monitoring, and improved communication with the provider for quicker interventions. This fosters increased accessibility, convenience, and patient confidence. Improving patient outcomes--the priority goal of this service model--will be assessed in future planned studies. Through this new practice model, providers are empowered to incorporate specialty medication management into transitions in care, admission and discharge quality indicators, readmissions, and other core measures. CONCLUSION: An integrated pharmacy practice model that includes an interdisciplinary team of physicians, nurses, and pharmacists improved patient compliance with MS therapies.