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

Jonas Rieger, Kostiantyn Yanchenko, Mattes Ruckdeschel, Gerret von Nordheim et al. • Studies in Communication, Media (2024)

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 ...

DOAJ Open Access (DOAJ) DOI: 10.5771/2192-4007-2024-1-72

Analyzing Sentiments in eLearning: A Comparative Study of Bangla and Romanized Bangla Text Using Transformers

Md Akash Rahman, Manoara Begum, Tanjim Mahmud, Mohammad Shahadat Hossain et al. • IEEE Access (2024)

In the modern world, learning is becoming increasingly critical due to rapid technological breakthroughs, which highlight the need for continuous skill development in both the personal and professional spheres. As a result, eLearning is a c...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3419024

Machine Reading Comprehension for the Tamil Language With Translated SQuAD

Anton Vijeevaraj Ann Sinthusha, Eugene Y. A. Charles, Ruvan Weerasinghe • IEEE Access (2025)

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...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3530949

Menstrual Health Education Using a Specialized Large Language Model in India: Development and Evaluation Study of MenstLLaMA

Prottay Kumar Adhikary, Isha Motiyani, Gayatri Oke, Maithili Joshi et al. • Journal of Medical Internet Research (2025)

Abstract BackgroundThe quality and accessibility of menstrual health education (MHE) in low- and middle-income countries, including India, remain inadequate due to persistent challenges (eg, poverty, social stigma, and gender in...

DOAJ Open Access (DOAJ) DOI: 10.2196/71977

Leveraging sentiment analysis of food delivery services reviews using deep learning and word embedding

Dheya Mustafa, Safaa M. Khabour, Mousa Al-kfairy, Ahmed Shatnawi • PeerJ Computer Science (2025)

Companies that deliver food (food delivery services, or FDS) try to use customer feedback to identify aspects where the customer experience could be improved. Consumer feedback on purchasing and receiving goods via online platforms is a cru...

DOAJ Open Access (DOAJ) DOI: 10.7717/peerj-cs.2669

Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy

Arezo Shakeri, Mina Farmanbar • Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring (2025)

Abstract INTRODUCTION Alzheimer's disease (AD) prevalence is increasing, with no current cure. Natural language processing (NLP) offers the potential for non‐invasive diagnostics, social burden assessment, and research advancements in AD. M...

DOAJ Open Access (DOAJ) DOI: 10.1002/dad2.70082

SAM-trace: a fuzzy logic approach in identifying interaction effects in the exacerbation of childhood malnutrition severity

S Shruthi, Priya Govindarajan, S. R. Shalini, Pavan John Antony • Discover Artificial Intelligence (2026)

Abstract Pediatric malnutrition remains a major global health challenge. Conventional WHO-based binary classification (Severe Acute Malnutrition (SAM) / Moderate Acute Malnutrition (MAM) relies solely on anthropometric thresholds. It fails ...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-026-01450-6

Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data

Gaurav Mohindru, Koushik Mondal, Haider Banka • CAAI Transactions on Intelligence Technology (2021)

Abstract In the era of automatic task processing or designing complex algorithms, to analyse data, it is always pertinent to find real‐life solutions using cutting‐edge tools and techniques to generate insights into the data. The data‐drive...

DOAJ Open Access (DOAJ) DOI: 10.1049/cit2.12032

ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing

Mark Neumann, Daniel King, Iz Beltagy, Waleed Ammar • BioNLP Workshop and Shared Task (BioNLP) (2019)

Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural l...

OPENALEX cc-by DOI: https://doi.org/10.18653/v1/w19-5034

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

裕二 池谷, Robert Tinn, Hao Cheng, MICHAEL A. LUCAS et al. • ACM Transactions on Computing for Healthcare (2021)

Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing a...

OPENALEX Open Access DOI: https://doi.org/10.1145/3458754

Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton et al. • Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) (2020)

We introduce Sta n z a , an open-source Python natural language processing toolkit supporting 66 human languages.Compared to existing widely used toolkits, Sta n z a features a language-agnostic fully neural pipeline for text analysis, incl...

OPENALEX cc-by DOI: https://doi.org/10.18653/v1/2020.acl-demos.14

A Survey on Gender Bias in Natural Language Processing

Karolina Stańczak, Isabelle Augenstein • arXiv (Cornell University) (2021)

Language can be used as a means of reproducing and enforcing harmful stereotypes and biases and has been analysed as such in numerous research. In this paper, we present a survey of 304 papers on gender bias in natural language processing. ...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.2112.14168

Digital footprints as a tool to evaluate the spatiotemporal environmental impacts of grocery shopping across Great Britain

Gavin Long, Evgeniya Lukinova, John Harvey, Joanne Parkes et al. • International Journal of Population Data Science (2025)

Introduction & Background Climate change represents one of the most pressing challenges of our time. Within this context, the food sector is a critical domain for intervention, accounting for approximately one-third of global greenhouse gas...

DOAJ Open Access (DOAJ) DOI: 10.23889/ijpds.v10i5.3336

Caps Captioning: A Modern Image Captioning Approach Based on Improved Capsule Network

Shima Javanmardi, Ali Mohammad Latif, Mohammad Taghi Sadeghi, Mehrdad Jahanbanifard et al. • Sensors (2022)

In image captioning models, the main challenge in describing an image is identifying all the objects by precisely considering the relationships between the objects and producing various captions. Over the past few years, many methods have b...

DOAJ Open Access (DOAJ) DOI: 10.3390/s22218376

Thyroid Ultrasound Appropriateness Identification Through Natural Language Processing of Electronic Health Records

Cristian Soto Jacome, MD, Danny Segura Torres, MD, Jungwei W. Fan, PhD, Ricardo Loor-Torres, MD et al. • Mayo Clinic Proceedings: Digital Health (2024)

Objective: To address thyroid cancer overdiagnosis, we aim to develop a natural language processing (NLP) algorithm to determine the appropriateness of thyroid ultrasounds (TUS). Patients and Methods: Between 2017 and 2021, we identified 18...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.mcpdig.2024.01.001

Ensembles machine learning for short duration rainfall intensity forecasting

Susilowati, Aprizal, Dyah Indriana Kusumastuti • Discover Artificial Intelligence (2026)

Abstract This study introduces an ensemble machine learning (EML) framework to enhance short-duration rainfall-intensity prediction under limited-data conditions. By integrating multiple base learners within a stacking architecture, the pro...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-026-01772-5