Results 11 to 20 of about 1,460 (118)

AI‐Enhanced Semantic Feature Norms for 786 Concepts

Topics in Cognitive Science, EarlyView.
Abstract Semantic feature norms have been foundational in the study of human conceptual knowledge, yet traditional methods face trade‐offs between concept/feature coverage and verifiability of quality due to the labor‐intensive nature of norming studies.
Siddharth Suresh, Kushin Mukherjee, Tyler Giallanza, Xizheng Yu, Mia Patil, Jonathan D. Cohen, Timothy T. Rogers   +6 more
wiley   +1 more source

Towards Enhancing Healthcare Data Privacy: Integrating BioClinicalBERT With Polyalphabetic Cipher for Entity Recognition and Anonymization

Engineering Reports, Volume 8, Issue 3, March 2026.
This study presents a dual‐layered anonymization model combining BioClinicalBERT and polyalphabetic cipher to accurately identify and protect sensitive healthcare data. It achieves strong F1 scores while maintaining data utility, enabling privacy‐preserving clinical research compliant with GDPR and HIPAA regulations.
Deblina Mazumder Setu, Tania Islam, Md Maklachur Rahman, Samrat Kumar Dey   +3 more
wiley   +1 more source

Explainable AI for Hate Speech Moderation: A Stakeholder‐Centered and Sociotechnical Review

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.
Overview of the proposed scoping review framework for Explainable AI (XAI) in hate speech moderation. The diagram maps the end to end structure of the study, beginning with the socio technical problem framing and research questions, followed by PRISMA guided methodology, dataset and model evaluation criteria, and comparative analysis of XAI methods for
Muhammad Deedahwar Mazhar Qureshi, M. Atif Qureshi, Wael Rashwan   +2 more
wiley   +1 more source

BLENDING FASTTEXT AND BERT PREDICTIONS FOR TAXONOMY ENRICHMENT

Computational Linguistics and Intellectual Technologies, 2020
In this paper, we present one of the solutions to the Taxonomy Enrichment shared task co-located with the Dialogue conference. The proposed method blends distributional information from fastText and BERT word embeddings to predict the most likely parent hypernym node for a new term in a taxonomy.
D. Puzyrev, E. Artemova, A. Shelmanov, A. Panchenko   +3 more
openaire   +1 more source

Mitigating the Negative Transfer in Multi‐Task Learning for Harmful Language Detection in Spanish and Arabic

Expert Systems, Volume 43, Issue 2, February 2026.
ABSTRACT Negative transfer continues to limit the benefits of multi‐task learning (MTL) in harmful language detection, where related tasks must share representations without diluting task‐specific nuances. We introduce task awareness (TA), a methodological framework that explicitly conditions MTL models on the task they must solve.
Angel Felipe Magnossão de Paula, Imene Bensalem, Damiano Spina, Paolo Rosso   +3 more
wiley   +1 more source

Omni Geometry Representation Learning Versus Large Language Models for Geospatial Entity Resolution

Transactions in GIS, Volume 30, Issue 1, February 2026.
ABSTRACT The development, integration, and maintenance of geospatial databases rely heavily on efficient and accurate matching procedures of Geospatial Entity Resolution (ER). While resolution of points‐of‐interest (POIs) has been widely addressed, resolution of entities with diverse geometries has been largely overlooked.
Kalana Wijegunarathna, Kristin Stock, Christopher B. Jones   +2 more
wiley   +1 more source

Automated item‐level measures of verbal fluency in semantic and logopenic primary progressive aphasia

Alzheimer's &Dementia, Volume 22, Issue 1, January 2026.
Abstract INTRODUCTION Verbal fluency tasks are widely used in primary progressive aphasia (PPA), but most studies rely only on total correct responses, overlooking qualitative features of the words produced. We applied a scalable computational framework to extract item‐level features from fluency responses in semantic variant (svPPA) and logopenic ...
Jet M. J. Vonk, Franco J. Ferrante, Brittany T. Morin, Diana Alejandra Rodriguez, Mia Lin, Rian Bogley, Jessica de Leon, Boon Lead Tee, Miguel Ángel Santos‐Santos, Zachary A. Miller, Maria Luisa Mandelli, Maria Luisa Gorno‐Tempini, Adolfo M. García   +12 more
wiley   +1 more source

HDR‐SA: A Hybrid Deep Learning and RoBERTa‐Based Framework for Sentiment and Aspect Analysis

IET Software, Volume 2026, Issue 1, 2026.
The ability to comprehend complex viewpoints in text is critical for sentiment analysis (SA), particularly at the aspect level, yet existing models struggle with accurately identifying sentiment polarities and aspect‐specific expressions due to their reliance on large, manually annotated, domain‐specific datasets.
Laxmi Pamulaparthy, C. H. Sumalakshmi, Nadeem Sarwar   +2 more
wiley   +1 more source

The Agent Preference in Ontogeny: Predictability of Agent and Patient Roles in Child‐Directed Utterances Across Languages

Cognitive Science, Volume 50, Issue 1, January 2026.
Abstract Language comprehension unfolds incrementally, requiring listeners to continually predict and revise interpretations. Comprehenders across very diverse languages show a consistent preference for agents, anticipating the agent (“the doer” of an action) more strongly than the patient (“the undergoer”). An unresolved question is how the preference
Eva Huber, Aylin C. Küntay, Balthasar Bickel, Sabine Stoll   +3 more
wiley   +1 more source

From Wording to Workforce: Gendered Language in Public Job Advertisements Shapes Gender Diversity in Applicant Pools

Public Administration Review, Volume 86, Issue 1, Page 258-275, January/February 2026.
ABSTRACT Gender imbalance in public sector hiring remains a persistent concern, yet research often overlooks how job advertisement features influence applicant self‐selection. Thus, we focus on gender sorting in the public labor market, a mechanism potentially causing structural self‐selection among job seekers.
Martin Sievert, Dominik Vogel, Matthias Döring   +2 more
wiley   +1 more source