Results 71 to 80 of about 15,278 (236)

OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers using Piecewise Convolutional Neural Networks

, 2018
We describe our system for SemEval-2018 Shared Task on Semantic Relation Extraction and Classification in Scientific Papers where we focus on the Classification task.
Dhyani, Dushyanta
core   +1 more source

Exploring Causal Learning Through Graph Neural Networks: An In‐Depth Review

WIREs Data Mining and Knowledge Discovery, Volume 15, Issue 2, June 2025.
Graphical abstract of the survey with a taxonomical approach to causal learning with graph neural networks. ABSTRACT In machine learning, exploring data correlations to predict outcomes is a fundamental task. Recognizing causal relationships embedded within data is pivotal for a comprehensive understanding of system dynamics, the significance of which ...
Simi Job, Xiaohui Tao, Taotao Cai, Haoran Xie, Lin Li, Qing Li, Jianming Yong   +6 more
wiley   +1 more source

CTSys at SemEval-2018 Task 3: Irony in Tweets [PDF]

Proceedings of The 12th International Workshop on Semantic Evaluation, 2018
L'objectif de cet article est de fournir une description d'un système construit comme notre participation à la tâche 3 de SemEval-2018 sur la détection de l'ironie dans les tweets en anglais. Ce système classe un tweet comme ironique ou non ironique grâce à une approche d'apprentissage supervisé.
Myan Sherif, Sherine Mamdouh, Wegdan Ghazi   +2 more
openaire   +2 more sources

Multi-task transfer learning for the prediction of entity modifiers in clinical text: application to opioid use disorder case detection

Journal of Biomedical Semantics
Background The semantics of entities extracted from a clinical text can be dramatically altered by modifiers, including entity negation, uncertainty, conditionality, severity, and subject.
Abdullateef I. Almudaifer, Whitney Covington, JaMor Hairston, Zachary Deitch, Ankit Anand, Caleb M. Carroll, Estera Crisan, William Bradford, Lauren A. Walter, Ellen F. Eaton, Sue S. Feldman, John D. Osborne   +11 more
doaj   +1 more source

Enhanced Sentiment Intensity Regression Through LoRA Fine-Tuning on Llama 3

IEEE Access
Sentiment analysis and emotion detection are critical research areas in natural language processing (NLP), offering benefits to numerous downstream tasks.
Diefan Lin, Yi Wen, Weishi Wang, Yan Su
doaj   +1 more source

ADeCNN: An Improved Model for Aspect-Level Sentiment Analysis Based on Deformable CNN and Attention

IEEE Access, 2020
Aspect-level sentiment analysis aims at identifying the sentiment polarity of target in the context. In most of the previous sentiment analysis models, there usually exists the problem of insufficient extraction capability of local features and long ...
Jie Zhou, Siqi Jin, Xinli Huang
doaj   +1 more source

Semantic Sentiment Analysis of Twitter Data

, 2017
Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype
B Jansen, B Liu, B Pang, F Sebastiani, G Forman, J Bollen, J Villena-Roman, J Wiebe, J Wiebe, JW Pennebaker, KW Church, P Nakov, PJ Stone, S Burton, S Kiritchenko, SR Das   +15 more
core   +1 more source

Arabic Short‐Text Dataset for Sentiment Analysis of Tourism and Leisure Events

Expert Systems, Volume 42, Issue 5, May 2025.
ABSTRACT The focus of this study is to present the detailed process of collecting a dataset of Arabic short‐text in the tourism context and annotating this dataset for the task of sentiment analysis using an automatic zero‐shot labelling technique utilising transformer‐based models.
Seham Basabain, Ahmed Al‐Dubai, Erik Cambria, Khalid Alomar, Amir Hussain   +4 more
wiley   +1 more source

Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets

, 2017
There has been a good amount of progress in sentiment analysis over the past 10 years, including the proposal of new methods and the creation of benchmark datasets.
Barnes, Jeremy, Klinger, Roman, Walde, Sabine Schulte im   +2 more
core   +1 more source

Leveraging Unsupervised Task Adaptation and Semi‐Supervised Learning With Semantic‐Enriched Representations for Online Sexism Detection

Expert Systems, Volume 42, Issue 2, February 2025.
ABSTRACT Over the past decade, the proliferation of hateful and sexist content targeting women on social media has become a concerning issue, adversely affecting women's lives and freedom of expression. Previous efforts to detect online sexism have utilized monolingual ensemble transformers combined with data augmentation techniques that incorporate ...
Francisco Rodríguez‐Sánchez, Jorge Carrillo‐de‐Albornoz, Laura Plaza   +2 more
wiley   +1 more source