Academic Articles & Research Papers

Deep Learning for Natural Language Processing

Natural Language Processing for Information Retrieval

Natural language processing (NLP) is the computerized approach to analyzing text that is based on both a set of theories and a set of technologies. Although NLP is a relatively recent area of research and application, compared with other in...

Natural Language Processing

The primary concern of natural language processing is the design and implementation of computational systems for analysing and understanding human languages to automate certain real-life tasks demanding human language abilities. It is typic...

Natural Language Processing of Social Media as Screening for Suicide Risk

Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people's lives are meaningfully affected, and almost any other statistic around suicide deaths i...

Evaluation of natural language processors

Despite a large amount of research on developing natural language understanding programs, little work has been done on evaluating their performance or potential. The evaluations that have been done have been unsystematic and incomplete. Thi...

Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.

Special Thematic Section on Semantic Models for Natural Language Processing (Preface)

With the availability of large language data online, cross-linked lexical resources (such as BabelNet, Predicate Matrix and UBY) and semantically annotated corpora (SemCor, OntoNotes, etc.), more and more applications in Natural Language Pr...

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data

Purpose NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analy...

Empowering text classification with NLP and explainable AI for enhanced interpretability

Abstract Artificial intelligence (AI) models have demonstrated significant success in classifying various types of text. However, the complex nature of these models often complicates the interpretability of their classifications. To address...

Large Language Models in Healthcare and Medical Domain: A Review

The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These models exhibit the remarkable ability to provide proficient responses to free-text queries, demonstrating a nuan...

National Leaders’ Usage of Twitter in Response to COVID-19: A Sentiment Analysis

Twitter is a powerful tool for world leaders to disseminate public health information and to reach citizens. While Twitter, like other platforms, affords world leaders the opportunity to rapidly present information to citizens, the discours...

A rule-based stemmer for Arabic Gulf dialect

Arabic dialects arewidely used from many years ago instead of Modern Standard Arabic language in many fields. The presence of dialects in any language is a big challenge. Dialects add a new set of variational dimensions in some fields like ...

Content Analysis of Medical Research Articles: A corpus-based study

Introduction: Content analysis applied for comparing texts, a writer’s works with others or the same author, the texts written in a natural language with texts in other languages literature and review of specialized field. The present study...

Using 311 data to develop an algorithm to identify urban blight for public health improvement.

The growth of administrative data made available publicly, often in near-real time, offers new opportunities for monitoring conditions that impact community health. Urban blight-manifestations of adverse social processes in the urban enviro...

Weighted DAG Automata for Semantic Graphs

Graphs have a variety of uses in natural language processing, particularly as representations of linguistic meaning. A deficit in this area of research is a formal framework for creating, combining, and using models involving graphs that pa...

Challenges in Natural Language Processing

Although natural language processing has come far, the technology has not achieved a major impact on society. Is this because of some fundamental limitation that cannot be overcome? Or because there has not been enough time to refine and ap...

Statistical Natural Language Processing

An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools

Comparative Study of CNN and RNN for Natural Language Processing

Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle var...

Natural Language Processing

Natural language processing is a fast-evolving field with popular applications in language translation, speech recognition, text classification, and summarization of text, among others. The central aspect of NLP is vectorizing text data. Te...