@inproceedings{wang-etal-2025-gowithnlp,
title = "gowithnlp at {S}em{E}val-2025 Task 10: Leveraging Entity-Centric Chain of Thought and Iterative Prompt Refinement for Multi-Label Classification",
author = "Wang, Bo and
Song, Ruichen and
Wang, Xiangyu and
Shi, Ge and
Hu, Linmei and
Huang, Heyan and
Feng, Chong",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.53/",
pages = "373--380",
ISBN = "979-8-89176-273-2",
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."
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%0 Conference Proceedings
%T gowithnlp at SemEval-2025 Task 10: Leveraging Entity-Centric Chain of Thought and Iterative Prompt Refinement for Multi-Label Classification
%A Wang, Bo
%A Song, Ruichen
%A Wang, Xiangyu
%A Shi, Ge
%A Hu, Linmei
%A Huang, Heyan
%A Feng, Chong
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F wang-etal-2025-gowithnlp
%X 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.
%U https://aclanthology.org/2025.semeval-1.53/
%P 373-380
Markdown (Informal)
[gowithnlp at SemEval-2025 Task 10: Leveraging Entity-Centric Chain of Thought and Iterative Prompt Refinement for Multi-Label Classification](https://aclanthology.org/2025.semeval-1.53/) (Wang et al., SemEval 2025)
ACL