The wide adoption of media and social media has increased the amount of digital content to an enormous level. Natural language processing (NLP) techniques provide an opportunity to extract and explore meaningful information from a large amount of text. Among natural languages, Urdu is one of the widely used languages worldwide for spoken and written communications. Due to its wide adopt-ability, digital content in the Urdu language is increasing briskly, especially with social media and online NEWS feeds. Government agencies and advertisers must filter and understand the content to analyze the trends and cohorts in their interest and national prerogative. Clustering is considered a baseline and one of the first steps in natural language understanding. There are many state-of-the-art clustering techniques specifically for English, French, and Arabic, but no significant research has been conducted in Urdu language processing. Doing it for short text segments is challenging because of limited features and the absence of meaningful language discourse and nuance. Many rule-based NLP techniques are adopted to overcome these issues, relying on human-designed features and rules. Therefore, these methods do not promise remarkable results. Alongside NLP, deep learning techniques are pretty efficient in capturing contextual information with minimal noise compared to other traditional methods. By taking on this challenging job, we develop a deep learning-based technique for Urdu short text clustering for the very first time without a human-designed feature. In this paper, we propose a method of short text clustering using a deep neural network that automatically learns feature representations and clustering assignments simultaneously. This method learns clustering objectives by converting the high dimensional feature space to a low dimensional feature space. Our experiments on the Urdu NEWS headlines dataset show remarkable results compared to state-of-the-art methods.

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The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients.

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