@inproceedings{xiong-etal-2023-ccl23,
title = "{CCL}23-Eval任务6系统报告:基于原型监督对比学习和模型融合的电信网络诈骗案件分类(System Report for {CCL}23-Eval Task 6: Classification of Telecom Network Fraud Cases Based on Prototypical Supervised Contrastive Learning and Model Fusion)",
author = "Xiong, Site and
Zhang, Jili and
Zhao, Yu and
Liu, Xinzhang and
Song, Yongshuang",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-3.22",
pages = "201--205",
abstract = "{``}本文提出了一种基于原型监督对比学习和模型融合的电信网络诈骗案件分类方法。为了增强模型区分易混淆类别的能力,我们采用特征学习与分类器学习并行的双分支神经网络训练框架,并通过领域预训练、模型融合、后置分类等策略优化分类效果。最终,本文方法在CCL2023-FCC评测任务上取得了Macro-F1为0.8601 的成绩。{''}",
language = "Chinese",
}
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<title>CCL23-Eval任务6系统报告:基于原型监督对比学习和模型融合的电信网络诈骗案件分类(System Report for CCL23-Eval Task 6: Classification of Telecom Network Fraud Cases Based on Prototypical Supervised Contrastive Learning and Model Fusion)</title>
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<abstract>“本文提出了一种基于原型监督对比学习和模型融合的电信网络诈骗案件分类方法。为了增强模型区分易混淆类别的能力,我们采用特征学习与分类器学习并行的双分支神经网络训练框架,并通过领域预训练、模型融合、后置分类等策略优化分类效果。最终,本文方法在CCL2023-FCC评测任务上取得了Macro-F1为0.8601 的成绩。”</abstract>
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%0 Conference Proceedings
%T CCL23-Eval任务6系统报告:基于原型监督对比学习和模型融合的电信网络诈骗案件分类(System Report for CCL23-Eval Task 6: Classification of Telecom Network Fraud Cases Based on Prototypical Supervised Contrastive Learning and Model Fusion)
%A Xiong, Site
%A Zhang, Jili
%A Zhao, Yu
%A Liu, Xinzhang
%A Song, Yongshuang
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G Chinese
%F xiong-etal-2023-ccl23
%X “本文提出了一种基于原型监督对比学习和模型融合的电信网络诈骗案件分类方法。为了增强模型区分易混淆类别的能力,我们采用特征学习与分类器学习并行的双分支神经网络训练框架,并通过领域预训练、模型融合、后置分类等策略优化分类效果。最终,本文方法在CCL2023-FCC评测任务上取得了Macro-F1为0.8601 的成绩。”
%U https://aclanthology.org/2023.ccl-3.22
%P 201-205
Markdown (Informal)
[CCL23-Eval任务6系统报告:基于原型监督对比学习和模型融合的电信网络诈骗案件分类(System Report for CCL23-Eval Task 6: Classification of Telecom Network Fraud Cases Based on Prototypical Supervised Contrastive Learning and Model Fusion)](https://aclanthology.org/2023.ccl-3.22) (Xiong et al., CCL 2023)
ACL