Academic Articles & Research Papers

Adversarial Attacks on Deep-learning Models in Natural Language Processing

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs are vulne...

Natural-language processing

Processing natural language such as English has always been one of the central research issues of artificial intelligence, both because of the key role language plays in human intelligence and because of the wealth of potential applications...

Connectionist Approaches to Natural Language Processing

Preface, N. Sharkey and R. Reilly connectionist natural language processing semantics, M.D. Dyer, et al distributed symbol discovery through symbol recirculation - toward natural language processing in distributed connectionist networks, R....

USE OF ARTIFICIAL INTELLIGENCE TO IDENTIFY AND CORRECT MISCONCEPTIONS ABOUT RADIATION

The expansion of nuclear technologies in various industries, combined with the constant threat of radiation-related incidents, highlights the urgent need for effective radiation education. This study is devoted to an empirical investigation...

Large Language Models and 3D Vision for Intelligent Robotic Perception and Autonomy

With the rapid advancement of artificial intelligence and robotics, the integration of Large Language Models (LLMs) with 3D vision is emerging as a transformative approach to enhancing robotic sensing technologies. This convergence enables ...

Natural language processing and its future in medicine

If accurate clinical information were available electronically, automated applications could be developed to use this information to improve patient care and lower costs. However, to be fully retrievable, clinical information must be struct...

Handbook of Computational Linguistics and Natural Language Processing

The Handbook of Computational Linguistics and Natural Language Processing provides a comprehensive overview of the concepts, methodologies, and applications being undertaken today in computational linguistics and natural language processing...

GBsim: A Robust GCN-BERT Approach for Cross-Architecture Binary Code Similarity Analysis

Recent advances in graph neural networks have transformed structural pattern learning in domains ranging from social network analysis to biomolecular modeling. Nevertheless, practical deployments in mission-critical scenarios such as binary...

An Introduction Natural Language Processing

The study of the way native human dialects and computer systems interact is known as natural language processing.Cognitive languages require the use of NLP, which has connections to artificial intelligence and computer engineering.In artifi...

Natural language processing

Summary form only given. Natural language processing (NLP) is a major area of artificial intelligence research, which in its turn serves as a field of application and interaction of a number of other traditional AI areas. Until recently, th...

Fast Exploring Literature by Language Machine Learning for Perovskite Solar Cell Materials Design

Making computers automatically extract latent scientific knowledge from literature is highly desired for future materials and chemical research in the artificial intelligence era. Herein, the natural language processing (NLP)‐based machine ...

A diagnostic hierarchy of epistemic betrayal in large language models

Abstract This paper proposes a hierarchy of epistemic betrayal that distinguishes deceptive behaviors in large language models (LLMs) by the depth at which the infrastructure for epistemic correction is compromised. Drawing on Park et al.’s...

Candyflipping and Other Combinations: Identifying Drug–Drug Combinations from an Online Forum

Novel psychoactive substances (NPS) refer to synthetic compounds or derivatives of more widely known substances of abuse that have emerged over the last two decades. Case reports suggest that users combine substances to achieve desired psyc...

Introduction to Natural Language Processing

My daily routine starts with loud knocking on my door. I hear my grandmother giving me a couple of minutes notice to get ready and start my daily chores. It is 8 a.m. and we are having breakfast and drinking black coffee. Every day we sit i...

Neural transfer learning for natural language processing

The current generation of neural network-based natural language processing models excels at learning from large amounts of labelled data. Given these capabilities, natural language processing is increasingly applied to new tasks, new domain...

Deep Learning for Natural Language Processing

Discover the concepts of deep learning used for natural language processing (NLP) in this practical book, with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2...

Knowledge, attitude, and practices towards anthrax among Communities in the Southern and South-western Ethiopia.

A Refutation of Finite-State Language Models through Zipf’s Law for Factual Knowledge

We present a hypothetical argument against finite-state processes in statistical language modeling that is based on semantics rather than syntax. In this theoretical model, we suppose that the semantic properties of texts in a natural langu...

Deep learning for natural language processing: advantages and challenges

Deep learning refers to machine learning technologies for learning and utilizing ‘deep’ artificial neural networks, such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN). Recently, deep ...

Natural Language Processing (almost) from Scratch

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This...