Academic Articles & Research Papers

Natural Language Processing

Understanding and processing human language has always been a complicated task in computational theory. Natural language processing, a combination of artificial intelligence and computational linguistics, employs computational techniques to...

Novel hepatic function-based prognostic prediction model for pulmonary arterial hypertension.

Development and External Validation of a Machine Learning Model for Automated Feedback Quality Assessment in Chinese Anesthesiology Residency Training

Lifeng Yao, Yijun Chen, Jing Shen, Junge Zhang, Yiwei Zhang, Guojin Liang, Yiqin Ji Department of Anesthesiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, People’s Republic of ChinaCorrespondence: Yijun Chen, De...

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 ...

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Abstract Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an ...

A Model-Driven Approach Employing DSL and Generation Templates to Accelerate the Development of Conversational Agents for Smart Tourism

Conversational agents are intelligent, ubiquitous software applications widely used across various sectors, such as commerce, tourism, and more. Their key benefits include automating tasks, improving customer service, and ensuring service a...

Diminished Auditory Responses during NREM Sleep Correlate with the Hierarchy of Language Processing.

Natural sleep provides a powerful model system for studying the neuronal correlates of awareness and state changes in the human brain. To quantitatively map the nature of sleep-induced modulations in sensory responses we presented participa...

Target reindexing and residual reconstruction enable reliable retrieval augmented few shot recognition

Abstract Retrieval-augmented few-shot recognition supplements sparsely annotated target classes with external visual samples, but open retrieval results may exhibit unstable quality, semantic mismatch, and cross-domain feature shifts. Direc...

Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review

Abstract This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box alg...

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...

A maximum entropy approach to natural language processing

The concept of maximum entropy can be traced back along multiple threads to Biblical times. Only recently, however, have computers become powerful enough to permit the widescale application of this concept to real world problems in statisti...

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...

Graph Neural Networks for Natural Language Processing: A Survey

Deep learning has become the dominant approach in addressing various tasks in Natural Language Processing (NLP). Although text inputs are typically represented as a sequence of tokens, there is a rich variety of NLP problems that can be bes...

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...

Design of public space guide system based on augmented reality technology

Abstract With the rapid development of science and technology, augmented reality technology provides intelligent and application services. The research is based on imaging techniques using augmented reality technology and camera image captu...

Enhancing adult lifelong learning through data fusion algorithms and quality assurance

Abstract This study presents a Data Fusion + Random Forest (RF) framework to predict adult learners’ educational outcomes, such as dropout risk and course completion. Adult lifelong learning is crucial for personal and professional growth, ...

An Analysis of the Training Data Impact for Domain-Adapted Tokenizer Performances—The Case of Serbian Legal Domain Adaptation

Various areas of natural language processing (NLP) have greatly benefited from the development of large language models in recent years. This research addresses the challenge of developing efficient tokenizers for transformer-based domain-s...

THE NORTHERN LABOR MARKET IN A RESOURCE-BASED REGIONAL ECONOMY: THE CASE OF THE KHANTY-MANSIYSK AUTONOMOUS OKRUG—YUGRA

This study examines the labor market of the Khanty-Mansi Autonomous Okrug–Yugra. The relevance of the research stems from the region’s strong dependence on natural resource reserves and production volumes, driven by the dominance of the min...

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 ...