Reset
Showing 20 of 2,356 academic articles

Natural Language Processing of Social Media as Screening for Suicide Risk

Glen Coppersmith, Ryan Leary, Patrick Crutchley, Alex B. Fine • Biomedical Informatics Insights (2018)

Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people's lives are meaningfully affected, and almost any other statistic around suicide deaths i...

OPENALEX cc-by-nc DOI: https://doi.org/10.1177/1178222618792860 PMC: 6111391

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

Empowering text classification with NLP and explainable AI for enhanced interpretability

Sumaya Mustafa, Mariwan Hama Saeed • Journal of Electrical Systems and Information Technology (2025)

Abstract Artificial intelligence (AI) models have demonstrated significant success in classifying various types of text. However, the complex nature of these models often complicates the interpretability of their classifications. To address...

DOAJ Open Access (DOAJ) DOI: 10.1186/s43067-025-00273-2

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

Ivan S. Blekanov, Nikita Tarasov, Svetlana S. Bodrunova • Future Internet (2022)

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...

DOAJ Open Access (DOAJ) DOI: 10.3390/fi14030069

Weighted DAG Automata for Semantic Graphs

David Chiang, Frank Drewes, Daniel Gildea, Adam Lopez et al. • Computational Linguistics (2018)

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...

DOAJ Open Access (DOAJ) DOI: 10.1162/COLI_a_00309

Natural Language Processing

Yasha Hasija, Rajkumar Chakraborty • CRC Press eBooks (2021)

Natural language processing is a fast-evolving field with popular applications in language translation, speech recognition, text classification, and summarization of text, among others. The central aspect of NLP is vectorizing text data. Te...

OPENALEX DOI: https://doi.org/10.1201/9781003090113-15-15

Fraud detection with natural language processing

Petros Boulieris, John Pavlopoulos, Alexandros Xenos, Vasilis Vassalos • Machine Learning (2023)

Abstract Automated fraud detection can assist organisations to safeguard user accounts, a task that is very challenging due to the great sparsity of known fraud transactions. Many approaches in the literature focus on credit card fraud and ...

OPENALEX cc-by DOI: https://doi.org/10.1007/s10994-023-06354-5

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

Tom Young, Devamanyu Hazarika, Soujanya Poria, Erik Cambria • IEEE Computational Intelligence Magazine (2018)

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...

OPENALEX Open Access DOI: https://doi.org/10.1109/mci.2018.2840738

Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach

Jawanpuria, Pratik, Balgovind, Arjun, Kunchukuttan, Anoop, Mishra, Bamdev • Transactions of the Association for Computational Linguistics (2019)

We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples the source-to-target language transformation into (a) language-specific rotations on the o...

DOAJ Open Access (DOAJ) DOI: 10.1162/tacl_a_00257

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

Ramteja Sajja, Yusuf Sermet, Ibrahim Demir • Discover Education (2026)

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...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44217-026-01825-y

Paradigm Shift in Natural Language Processing

Tianxiang Sun, Xiang-Yang Liu, Xipeng Qiu, Xuanjing Huang • Machine Intelligence Research (2022)

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...

OPENALEX cc-by DOI: https://doi.org/10.1007/s11633-022-1331-6

Adversarial Attacks on Deep-learning Models in Natural Language Processing

Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, Chenliang Li • ACM Transactions on Intelligent Systems and Technology (2020)

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...

OPENALEX Open Access DOI: https://doi.org/10.1145/3374217

Biomedical Natural Language Processing

Kevin Bretonnel Cohen, Dina Demner‐Fushman • Natural language processing (2014)

Biomedical Natural Language Processing is a comprehensive tour through the classic and current work in the field. It discusses all subjects from both a rule-based and a machine learning approach, and also describes each subject from the per...

OPENALEX DOI: https://doi.org/10.1075/nlp.11

Natural Language Processing

Yue Zhang, Zhiyang Teng • Cambridge University Press eBooks (2020)

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...

OPENALEX DOI: https://doi.org/10.1017/9781108332873

USE OF ARTIFICIAL INTELLIGENCE TO IDENTIFY AND CORRECT MISCONCEPTIONS ABOUT RADIATION

Oleksandr Tymoshchuk • Інформаційні технології і засоби навчання (2025)

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...

DOAJ Open Access (DOAJ) DOI: 10.33407/itlt.v105i1.5905