Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …

Text classification results can be hindered when just the bag-of-words model is used for
representing features, because it ignores word order and senses, which can vary with the …

ICD-9 (the Ninth Revision of International Classification of Diseases) is widely used to
describe a patient's diagnosis. Accurate automated ICD-9 coding is important because …

Web applications are paramount tools for facilitating services providing in the modern world.
Unfortunately, the tremendous growth in the web application usage has resulted in a rise in …

Multi-label text classification is an important yet challenging task in natural language
processing. It is more complex than single-label text classification in that the labels tend to …

This paper presents a systematic review to identify research combining artificial intelligence
(AI) algorithms with Open source intelligence (OSINT) applications and practices. Currently …

Medical applications challenge today's text categorization techniques by demanding both
high accuracy and ease-of-interpretation. Although deep learning has provided a leap …

Deep neural networks (DNNs) have achieved remarkable success in various tasks (eg,
image classification, speech recognition, and natural language processing (NLP)). However …

Microblogging sites contain a huge amount of textual data and their classification is an
imperative task in many applications, such as information filtering, user profiling, topical …

Detecting cybersecurity intelligence (CSI) on social media such as Twitter is crucial because
it allows security experts to respond cyber threats in advance. In this paper, we devise a new …