3 papers
cs.IR2025
CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models
Junze Chen, Xinjie Yang, Cheng Yang +4
Recommender systems (RSs) are designed to retrieve candidate items a user might be interested in from a large pool. A common approach is using graph neural networks (GNNs) to captu…
cs.CL2023
Type-aware Decoding via Explicitly Aggregating Event Information for Document-level Event Extraction
Gang Zhao, Yidong Shi, Shudong Lu +5
Document-level event extraction (DEE) faces two main challenges: arguments-scattering and multi-event. Although previous methods attempt to address these challenges, they overlook…
cs.CL2023
DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction
Gang Zhao, Xiaocheng Gong, Xinjie Yang +3
Most current Event Extraction (EE) methods focus on the high-resource scenario, which requires a large amount of annotated data and can hardly be applied to low-resource domains. T…