gowithnlp at S em E val-2025 Task 10: Leveraging Entity-Centric Chain of Thought and Iterative Prompt Refinement for Multi-Label Classification (2025)

Abstract: This paper presents our system for Subtask 10 of Entity Framing, which focuses on assigning one or more hierarchical roles to named entities in news articles. Our approach iteratively refines prompts and utilizes the Entity-Centric Chain of Thought to complete the task. Specifically, to minimize ambiguity in label definitions, we use the model’s predictions as supervisory signals, iteratively refining the category definitions. Furthermore, to minimize the interference of irrelevant information during inference, we incorporate entity-related information into the CoT framework, allowing the model to focus more effectively on entity-centric reasoning. Our system achieved the highest ranking on the leaderboard in the Russian main role classification and the second in English, with an accuracy of 0.8645 and 0.9362, respectively. We discuss the impact of several components of our multilingual classification approach, highlighting their effectiveness.

## introduction the task of entity framing, as part of the semeval 2025 campaign, focuses on the automatic identification and classification of roles assigned to named entities (nes) in news articles @xcite . specifically, it involves determining the roles of protagonists, antagonists, and innocents within the context of news articles related to highstakes global issues such as the ukraine-russia war and climate change. this task is of particular importance as it supports the identification of manipulation attempts and disinformation in media, thereby facilitating a better understanding and analysis of news narratives. the task is multilingual, covering five languages: bulgarian, english, hindi, portuguese, and russian, and aims to offer valuable insights into how different languages handle entity roles in manipulative content. ## background the entity framing task in semeval 2025 aims to automatically identify and classify the roles of named entities (nes) within news articles. the task specifically targets three roles: protagonists, antagonists, and innocents, based on a predefined taxonomy. this is a multi-label, multi-class textspan classification problem, where the goal is to classify nes within the context of news articles related to high-profile topics. these areas are highly ## system overview to address the challenges posed by the rapid increase in label quantities, which can complicate classification tasks, we employ a pipeline approach. this approach utilizes the results from main role classification as input for fine-grained classification. for both main role and fine-grained tasks, we incorporate iterative prompt refinement to enhance category definitions, followed by inference using an entity-centric cot framework to improve the model's understanding of entity-based tasks. subsequent sections will provide a comprehensive overview of our model's key components, including prompt construction, multi-label classification, and post-processing.