Academic Articles & Research Papers

Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the mo...

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...

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

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the contex...

FREDC: A Few-Shot Relation Extraction Dataset for Chinese

Few-shot relation extraction (RE) is a critical task in natural language processing (NLP), yet benchmark datasets for non-English languages, particularly Chinese, remain scarce. Owing to their narrow focus and insufficient coverage of relat...

The Effectiveness of Arabic Stemmers Using Arabized Word Removal

Other languages have influenced Arabic because of several factors, such as geographical nearness, trade communication, past Islamic conquests, science and technology, new devices, brand names, models, and fashion. As a result of these facto...

One Picture and One Thousand Words: Toward integrated multimodal generative models

Thanks to independent advances in language and image generation, we could soon be in the position to have systems that communicate with us by combining language and images in their output, a skill that humans do not possess (we receive, but...

Enhancing educational semantic retrieval through dual objective fine tuning of open source embedding models

Abstract Recent advances in AI have catalyzed the adoption of intelligent educational tools, yet many semantic retrieval systems remain ill-suited to the unique linguistic and structural characteristics of academic content. This study prese...

Paradigm Shift in Natural Language Processing

Abstract In the era of deep learning, modeling for most natural language processing (NLP) tasks has converged into several mainstream paradigms. For example, we usually adopt the sequence labeling paradigm to solve a bundle of tasks such as...

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

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are sy...

Natural Language Processing in Surgery

OBJECTIVE: The aim of this study was to systematically assess the application and potential benefits of natural language processing (NLP) in surgical outcomes research. SUMMARY BACKGROUND DATA: Widespread implementation of electronic health...

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 ...

Analyzing Brain Tumor Classification Techniques: A Comprehensive Survey

Classification of brain tumour is critical in medical image analysis and diagnosis as it aids in determining the affinity of cases to select treatment paths. This feature underscores the importance of accurate classification of brain tumour...

Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)

Neural networks models for NLP are typically implemented without the explicit encoding of language rules and yet they are able to break one performance record after another. This has generated a lot of research interest in interpreting the ...

Brains and algorithms partially converge in natural language processing

Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, w...

A Survey on Natural Language Processing for Fake News Detection

Fake news detection is a critical yet challenging problem in Natural Language Processing (NLP). The rapid rise of social networking platforms has not only yielded a vast increase in information accessibility but has also accelerated the spr...

Study on Building Business-oriented Resource On-demand Resolution Model

To address the issue of re-analyzing and repeating development of natural language processing tools and resource ana-lysis plugins when new requirements arise during project development,this paper proposes a business-oriented on-demand reso...