Academic Articles & Research Papers

Applying Natural Language Processing in Manufacturing

Despite great progress in the digitization of the industrial sector through Industry 4.0 and widely available data, data analysis is typically constrained to numerical data, not the synthesis of knowledge. Although valuable employee knowled...

Inheritance in natural language processing

In this introduction to the special issues, we begin by outlining a concrete example that indicates some of the motivations leading to the widespread use of inheritance networks in computational linguistics. This example allows us to illust...

Systematic Evaluation of Research Progress on Natural Language Processing in Medicine Over the Past 20 Years: Bibliometric Study on PubMed

BackgroundNatural language processing (NLP) is an important traditional field in computer science, but its application in medical research has faced many challenges. With the extensive digitalization of medical information globally and incr...

Improving Web Readability Using Video Content: A Relevance-Based Approach

With the increasing integration of multimedia elements into webpages, videos have emerged as a popular medium for enhancing user engagement and knowledge retention. However, irrelevant or poorly placed videos can hinder readability and dist...

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

BACKGROUND: Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides ...

Natural Language Processing (NLP): An Introduction

Natural language processing in-and-for design research

Abstract We review the scholarly contributions that utilise natural language processing (NLP) techniques to support the design process. Using a heuristic approach, we gathered 223 articles that are published in 32 journals within the period...

Data augmentation techniques in natural language processing

Data Augmentation (DA) methods – a family of techniques designed for synthetic generation of training data – have shown remarkable results in various Deep Learning and Machine Learning tasks. Despite its widespread and successful adoption w...

Large Language Models and the Elliott Wave Principle: A Multi-Agent Deep Learning Approach to Big Data Analysis in Financial Markets

Traditional technical analysis methods face limitations in accurately predicting trends in today’s complex financial markets. Meanwhile, existing AI-driven approaches, while powerful in processing large datasets, often lack interpretability...

Information Extraction From FDA Drug Labeling to Enhance Product-Specific Guidance Assessment Using Natural Language Processing

Towards the objectives of the UnitedStates Food and Drug Administration (FDA) generic drug science and research program, it is of vital importance in developing product-specific guidances (PSGs) with recommendations that can facilitate and ...

Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine

Pre-trained language models (PLM) based on transformer neural networks developed in the field of natural language processing (NLP) offer great opportunities to improve automatic content analysis in communication science, especially for the ...

Machine Reading Comprehension for the Tamil Language With Translated SQuAD

Machine Reading Comprehension (MRC) is a challenging task in Natural Language Processing (NLP), crucial for automated customer support, enabling chatbots and virtual assistants to accurately understand and respond to queries. It also enhanc...

Text Filtering through Multi-Pattern Matching: A Case Study of Wu–Manber–Uy on the Language of Uyghur

Given its generality in applications and its high time-efficiency on big data-sets, in recent years, the technique of text filtering through pattern matching has been attracting increasing attention from the field of information retrieval a...

Applications of Natural Language Processing in Biodiversity Science

Centuries of biological knowledge are contained in the massive body of scientific literature, written for human-readability but too big for any one person to consume. Large-scale mining of information from the literature is necessary if bio...

Natural language processing for information assurance and security

This research paper explores a promising interface between natural language processing (NLP) and information assurance and security (IAS). More specificall~ it is devoted to possible applications to, and further dedicated development of, th...

Natural language processing applied to mental illness detection: a narrative review

Mental illness is highly prevalent nowadays, constituting a major cause of distress in people's life with impact on society's health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors...

BERT applications in natural language processing: a review

BERT (Bidirectional Encoder Representations from Transformers) has revolutionized Natural Language Processing (NLP) by significantly enhancing the capabilities of language models. This review study examines the complex nature of BERT, inclu...

Natural Language Processing

Natural language processing (NLP) is a field of computer science that is a subset of artificial intelligence and that helps computers understand human language as it is written and spoken. Until recently, it was humans who were trying to un...

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

BACKGROUND: Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the seconda...

Multi-Task Learning in Natural Language Processing: An Overview

Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP). However, directly training deep neural models often suffer from overfitting and data scarcity problems that are pervasive in NLP tasks. ...