@inproceedings{li-etal-2025-ynu-hpcc,
title = "{YNU}-{HPCC} at {S}em{E}val-2025 Task 10: A Two-Stage Approach to Solving Multi-Label and Multi-Class Role Classification Based on {D}e{BERT}a",
author = "Li, Ning and
Zhang, You and
Wang, Jin and
Xu, Dan and
Zhang, Xuejie",
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-1.258/",
pages = "1993--1999",
ISBN = "979-8-89176-273-2",
abstract = "A two-stage role classification model based on DeBERTa is proposed for the Entity Framework task in SemEval 2025 Task 10. The task is confronted with challenges such as multi-labeling, multi-category, and category imbalance, particularly in the semantic overlap and data sparsity of fine-grained roles. Existing methods primarily rely on rules, traditional machine learning, or deep learning, but the accurate classification of fine-grained roles is difficult to achieve. To address this, the proposed model integrates the deep semantic representation of the DeBERTa pre-trained language model through two sub-models: main role classification and sub-role classification, and utilizes Focal Loss to optimize the category imbalance issue. Experimental results indicate that the model achieves an accuracy of 75.32{\%} in predicting the main role, while the exact matching rate for the sub-role is 8.94{\%}. This is mainly limited by the strict matching standard and semantic overlap of fine-grained roles in the multi-label task. Compared to the baseline{'}s sub-role exact matching rate of 3.83{\%}, the proposed model significantly improves this metric. The model ultimately ranked 23rd on the leaderboard. The code of this paper is available at:https://github.com/jiyuaner/YNU-HPCC-at-SemEval-2025-Task10."
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<abstract>A two-stage role classification model based on DeBERTa is proposed for the Entity Framework task in SemEval 2025 Task 10. The task is confronted with challenges such as multi-labeling, multi-category, and category imbalance, particularly in the semantic overlap and data sparsity of fine-grained roles. Existing methods primarily rely on rules, traditional machine learning, or deep learning, but the accurate classification of fine-grained roles is difficult to achieve. To address this, the proposed model integrates the deep semantic representation of the DeBERTa pre-trained language model through two sub-models: main role classification and sub-role classification, and utilizes Focal Loss to optimize the category imbalance issue. Experimental results indicate that the model achieves an accuracy of 75.32% in predicting the main role, while the exact matching rate for the sub-role is 8.94%. This is mainly limited by the strict matching standard and semantic overlap of fine-grained roles in the multi-label task. Compared to the baseline’s sub-role exact matching rate of 3.83%, the proposed model significantly improves this metric. The model ultimately ranked 23rd on the leaderboard. The code of this paper is available at:https://github.com/jiyuaner/YNU-HPCC-at-SemEval-2025-Task10.</abstract>
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2025 Task 10: A Two-Stage Approach to Solving Multi-Label and Multi-Class Role Classification Based on DeBERTa
%A Li, Ning
%A Zhang, You
%A Wang, Jin
%A Xu, Dan
%A Zhang, Xuejie
%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 li-etal-2025-ynu-hpcc
%X A two-stage role classification model based on DeBERTa is proposed for the Entity Framework task in SemEval 2025 Task 10. The task is confronted with challenges such as multi-labeling, multi-category, and category imbalance, particularly in the semantic overlap and data sparsity of fine-grained roles. Existing methods primarily rely on rules, traditional machine learning, or deep learning, but the accurate classification of fine-grained roles is difficult to achieve. To address this, the proposed model integrates the deep semantic representation of the DeBERTa pre-trained language model through two sub-models: main role classification and sub-role classification, and utilizes Focal Loss to optimize the category imbalance issue. Experimental results indicate that the model achieves an accuracy of 75.32% in predicting the main role, while the exact matching rate for the sub-role is 8.94%. This is mainly limited by the strict matching standard and semantic overlap of fine-grained roles in the multi-label task. Compared to the baseline’s sub-role exact matching rate of 3.83%, the proposed model significantly improves this metric. The model ultimately ranked 23rd on the leaderboard. The code of this paper is available at:https://github.com/jiyuaner/YNU-HPCC-at-SemEval-2025-Task10.
%U https://aclanthology.org/2025.semeval-1.258/
%P 1993-1999
Markdown (Informal)
[YNU-HPCC at SemEval-2025 Task 10: A Two-Stage Approach to Solving Multi-Label and Multi-Class Role Classification Based on DeBERTa](https://aclanthology.org/2025.semeval-1.258/) (Li et al., SemEval 2025)
ACL