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LE2C: LLM-Enhanced Event Evolutionary Graph for Explainable Classification
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AI
DOI：10.1007/978-981-95-5719-6_23.png)
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Abstract
En 中文
In the development of intelligent systems, multi-label event classification plays a vital role in enabling accurate decision-making across diverse scenarios such as incident response, customer service, and urban management. Although existing graph-based approaches for multi-label classification have shown potential, they struggle to model directed label dependencies and lack explainability, resulting in black-box decision-making processes. To address these issues, we propose a novel LLM-enhanced Event Evolutionary graph for Explainable Classification ((LEC)-C-2) method. Specifically, we first leverage the powerful semantic learning capabilities of Large Language Models to construct an event evolutionary graph that models event dynamics. Furthermore, we introduce a co-occurrence probability matrix to enhance the expressivity and explainability of the graph, guiding explainable classification. Extensive experiments on two large real-world event classification tasks demonstrate the efficiency, effectiveness, and explainability of (LEC)-C-2. The code is available at https://github.com/NinaLiangjy/LE2C.
Keywords:
Explainable Multi-label Classification
Event Evolutionary Graph
Knowledge Discovery
Large Language Model
Journal
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Papers:
32
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