Co-occurring keywords · papers mentioning both
Papers
AFourP at SemEval-2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays
SemEval 2026
AI4PC-Howard University at SemEval-2026 Task 2: Fine-Tuning DistilBERT, DeBERTa and ModernBERT for Valence–Arousal Prediction and Change Estimation
SemEval 2026
CLRG at SemEval-2026 Task 3: One Size Does Not Fit All: A Resource Adaptive Framework for Dimensional Sentiment Regression
SemEval 2026
DualAxis AI at SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis
SemEval 2026
DUTH at SemEval-2026 Task 3: Multilingual Transformer Models for Dimensional Stance Prediction Across Tracks
SemEval 2026
hllwan at SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis via LLM Feature Fusion and Test-Time Adaptation
SemEval 2026
Momentum at SemEval-2026 Task 2: LongVA-RoBERTa, a transformer-Based Longitudinal Valence and Arousal Modeling
SemEval 2026
NLP-FSDM at SemEval-2026 Task 2: Temporal Smoothing and CCC-MAE Optimization for Balanced Longitudinal Affect Assessment
SemEval 2026
Pixel Phantoms at SemEval-2026 Task 3: Language-Specific Transformer Regression for Dimensional Aspect-Based Sentiment Analysis
SemEval 2026
RPI Team at SemEval-2026 Task 3: An LLM-Encoder Ensemble for Coarse-to-Fine Valence-Arousal Sentiment Prediction
SemEval 2026
ssurface3 at SemEval-2026 Task 3: Efficient Methods for Multilingual Dimensional Aspect-Based Sentiment Analysis
SemEval 2026
YangSteam at SemEval-2026 Task 3: Transformer-Based Aspect-Aware Regression for Dimensional Sentiment and Stance Analysis
SemEval 2026
YNU-HPCC at SemEval-2026 Task 2: Contrastive Calibration and Temporal Modeling for Continuous Valence-Arousal Prediction
SemEval 2026
MainiwayAI at IJCNLP-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction
IJCNLP 2017
NCTU-NTUT at IJCNLP-2017 Task 2: Deep Phrase Embedding using bi-LSTMs for Valence-Arousal Ratings Prediction of Chinese Phrases
IJCNLP 2017
THU_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM
IJCNLP 2017