Papers by Wanting Ning
Rule-Guided Extraction: A Hierarchical Rule Optimization Framework for Document-Level Event Argument Extraction (2025.findings-emnlp)
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| Challenge: | Document-level event argument extraction (EAE) is a critical task in natural language processing. |
| Approach: | They propose an LLM-driven HiErarchical Rule Optimization framework that iteratively generates and selects optimal hierarchical rules. |
| Outcome: | The proposed framework outperforms few-shot supervised methods and outperformed state-of-the-art prompting baselines. |
GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction (2025.findings-emnlp)
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| Challenge: | Existing approaches to analyzing large language models are limited by their pre-trained knowledge of Small Language Models(SLMs). |
| Approach: | They propose a global distance-aware modeling approach based on Large Language Models (LLMs) they first present a distance-based graph structure utilizing Graph Attention Network(GAT) |
| Outcome: | The proposed approach significantly improves minority relation class learning ability on two publicly available datasets, TB-Dense and MATRES. |
Temporal Cognitive Tree: A Hierarchical Modeling Approach for Event Temporal Relation Extraction (2024.findings-emnlp)
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| Challenge: | Recent studies focus on locating relative position of event pairs on timeline . hierarchical modeling approach neglects multidimensional information in temporal relation and hierarchy of reasoning. |
| Approach: | They propose a novel hierarchical modeling approach that mimics human logical reasoning by introducing a Temporal Cognitive Tree. |
| Outcome: | The proposed model outperforms existing methods on TB-Dense and MATRES datasets. |