Related papers: Natural Language Processing in-and-for Design Rese…
Objective: to provide a scoping review of papers on clinical natural language processing (NLP) tasks that use publicly available electronic health record data from a cohort of patients. Materials and Methods: We searched six databases,…
Incorporating linguistic, world and common sense knowledge into AI/NLP systems is currently an important research area, with several open problems and challenges. At the same time, processing and storing this knowledge in lexical resources…
This paper discusses the use of `ontologies' in Natural Language Processing. It classifies various kinds of ontologies that have been employed in NLP and discusses various benefits and problems with those designs. Particular focus is then…
Recent developments in large language models (LLMs) have been accompanied by rapidly growing public interest in natural language processing (NLP). This attention is reflected by major news venues, which sometimes invite NLP researchers to…
Natural Language Processing (NLP) has transformed various fields beyond linguistics by applying techniques originally developed for human language to the analysis of biological sequences. This review explores the application of NLP methods…
The use of natural language processing (NLP) techniques in engineering education can provide valuable insights into the underlying processes involved in generating text. While accessing these insights can be labor-intensive if done…
Language processing is at the heart of current developments in artificial intelligence, and quantum computers are becoming available at the same time. This has led to great interest in quantum natural language processing, and several early…
In recent years, the field of Legal Tech has risen in prevalence, as the Natural Language Processing (NLP) and legal disciplines have combined forces to digitalize legal processes. Amidst the steady flow of research solutions stemming from…
The design of complex engineering systems is an often long and articulated process that highly relies on engineers' expertise and professional judgment. As such, the typical pitfalls of activities involving the human factor often manifest…
Many search systems work with large amounts of natural language data, e.g., search queries, user profiles and documents, where deep learning based natural language processing techniques (deep NLP) can be of great help. In this paper, we…
Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by automating traditional labor-intensive tasks and consequently accelerated the development of computer-aided applications. As researchers…
This book provides a broad overview of Natural Language Generation (NLG), including technology, user requirements, evaluation, and real-world applications. The focus is on concepts and insights which hopefully will remain relevant for many…
Extremism research has grown as an open problem for several countries during recent years, especially due to the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as…
Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their…
It is a long-standing desire of industry and research to automate the software development and testing process as much as possible. In this process, requirements engineering (RE) plays a fundamental role for all other steps that build on…
Analogical reasoning is an essential aspect of human cognition. In this paper, we summarize key theory about the processes underlying analogical reasoning from the cognitive science literature and relate it to current research in natural…
The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque…
Large language models (LLMs) are a class of language models that have demonstrated outstanding performance across a range of natural language processing (NLP) tasks and have become a highly sought-after research area, because of their…
With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intelligence. As artificial intelligence continues to…
Dialogue systems are a popular natural language processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on…