YNU zwt at S em E val-2025 Task 10: Tree-guided Stagewise Classifier for Entity Framing and Narrative Classification (2025)

Abstract: This paper presents a hierarchical classification framework, designated as the Tree-guided Stagewise Classifier (TGSC) , which implements a Chain-of-Thought (CoT) reasoning paradigm for addressing multi-label and multi-class classification challenges in multilingual news article analysis in SemEval-2025 Task 10. The proposed methodology leverages the zero-shot capabilities inherent in Large Language Models (LLMs) through a systematic hierarchical reasoning mechanism. This process proceeds through successive hierarchical levels, wherein the classification commences from root nodes and progressively navigates category branches via iterative determinations at each hierarchical tier, ultimately culminating in leaf category identification during the final classification stage. To optimize classification precision, a specialized prompt engineering strategy incorporating hierarchical structural constraints is developed to guide the reasoning trajectory. Experimental results demonstrate the effectiveness of our approach, achieving competitive performance across multiple languages in Subtask 1 and Subtask 2.

## introduction semeval-2025 task 10 foot_0 @xcite focuses on the multilingual characterization and extraction of narratives from online news. this task is of strategic significance for both computational social science and multilingual natural language processing (nlp), as it addresses key aspects of contemporary information ecosystems-such as cross-cultural narrative modeling and resilience to disinformation. the task comprises three subtasks. our team participated in subtask 1 (entity framing) and subtask 2 (narrative classification), evaluating our approach across three languages: english, portuguese, and russian. to address both subtasks, we propose the treeguided stagewise classifier (tgsc), a hierarchical reasoning framework that systematically harnesses the zero-shot capabilities of llms through parentchild knowledge propagation. the architecture of tgsc adopts a stagewise classification protocol in which parent-level predictions dynamically condition subsequent child-level decisions by restructuring the reasoning context provided to the llm. at each level of the hierarchy, validated parent class labels are explicitly integrated into cot prompts using constrained text generation templates. this design guides the llm's zero-shot inference toward taxonomically valid subclasses. the top-down classification process improves accuracy: parent-level decisions eliminate irrelevant categories, while child-level classification leverages contextual cues to resolve ambiguities among finer-grained classes. importantly, tgsc achieves hierarchical constraint propagation purely through prompt engineering, without relying on parametric gating mechanisms. this preserves full zero-shot flexibility while structurally preventing category violations. by recursively anchoring coarse-grained classifications to guide more specific decisions, tgsc enables efficient knowledge transfer across classification tiers, effectively balancing broad conceptual coverage with finegrained precision. our participation in this task demonstrates that a zero-shot hierarchical reasoning framework can achieve competitive performance across languages. our contributions include the following: ## conclusion this paper introduces the tree-guided stagewise classifier (tgsc), a hierarchical framework that leverages chain-of-thought reasoning for multilabel news categorization. tgsc progressively narrows the classification space through stagewise refinement, transitioning from coarse-grained parent-category hypotheses to fine-grained childcategory predictions via structured reasoning paths. experimental results validate the effectiveness of tgsc in zero-shot multilingual settings, demonstrating consistent accuracy improvements over baseline models across english, portuguese, and russian-without requiring model retraining. nevertheless, tgsc also has limitations. specifically, the stagewise hierarchical structure introduces a potential risk of error propagation: misclassifications at parent nodes may constrain or mislead subsequent child-level predictions. in future work, we plan to explore mechanisms such as confidencebased node re-evaluation, multi-path traversal, or uncertainty-aware prompting strategies to further enhance robustness against hierarchical prediction errors.