A Hybrid Neuro-Symbolic Pipeline for Coreference Resolution and AMR-Based Semantic Parsing
Large Language Models (LLMs) have transformed Natural Language Processing (NLP), yet they continue to struggle with deep semantic understanding, particularly in tasks like coreference resolution and structured semantic inference. This study presents a hybrid neuro-symbolic pipeline that combines tra...
Saved in:
| Main Authors: | Christos Papakostas, Christos Troussas, Akrivi Krouska, Cleo Sgouropoulou |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/7/529 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Integrating Bayesian Knowledge Tracing and Human Plausible Reasoning in an Adaptive Augmented Reality System for Spatial Skill Development
by: Christos Papakostas, et al.
Published: (2025-05-01) -
Enhancing Human-Computer Interaction in Digital Repositories through a MCDA-Based Recommender System
by: Christos Troussas, et al.
Published: (2021-01-01) -
Exploring the Acceptance and Impact of a Digital Escape Room Game for Environmental Education Using Structural Equation Modeling
by: Akrivi Krouska, et al.
Published: (2025-06-01) -
A Fuzzy-Neural Model for Personalized Learning Recommendations Grounded in Experiential Learning Theory
by: Christos Troussas, et al.
Published: (2025-04-01) -
Personalized Instructional Strategy Adaptation Using TOPSIS: A Multi-Criteria Decision-Making Approach for Adaptive Learning Systems
by: Christos Troussas, et al.
Published: (2025-05-01)