@inproceedings{li-etal-2023-ccl23-eval-ren-wu-6xi,
title = "{CCL}23-Eval 任务6系统报告:基于深度学习的电信网络诈骗案件分类(System Report for {CCL}23-Eval Task 6: Classification of Telecom {I}nternet Fraud Cases Based on Deep Learning)",
author = "Li, Chenyang and
Zhang, Long and
Zhao, Zhongjie and
Guo, Hui",
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-3.16",
pages = "167--172",
abstract = "{``}文本分类任务作为自然语言处理领域的基础任务,在面向电信网络诈骗领域的案件分类中扮演着至关重要的角色,对于智能化案件分析具有重大意义和深远影响。本任务的目的是对给定案件描述文本进行分类,案件文本包含对案件的经过脱敏处理后的整体描述。我们首先采用Ernie预训练模型对案件内容进行微调的方法得到每个案件的类别,再使用伪标签和模型融合方法对目前的F1值进行提升,最终在CCL23-Eval任务6电信网络诈骗案件分类评测中取得第二名的成绩,该任务的评价指标F1值为0.8628,达到了较为先进的检测效果。{''}",
language = "Chinese",
}
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<title>CCL23-Eval 任务6系统报告:基于深度学习的电信网络诈骗案件分类(System Report for CCL23-Eval Task 6: Classification of Telecom Internet Fraud Cases Based on Deep Learning)</title>
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<abstract>“文本分类任务作为自然语言处理领域的基础任务,在面向电信网络诈骗领域的案件分类中扮演着至关重要的角色,对于智能化案件分析具有重大意义和深远影响。本任务的目的是对给定案件描述文本进行分类,案件文本包含对案件的经过脱敏处理后的整体描述。我们首先采用Ernie预训练模型对案件内容进行微调的方法得到每个案件的类别,再使用伪标签和模型融合方法对目前的F1值进行提升,最终在CCL23-Eval任务6电信网络诈骗案件分类评测中取得第二名的成绩,该任务的评价指标F1值为0.8628,达到了较为先进的检测效果。”</abstract>
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%0 Conference Proceedings
%T CCL23-Eval 任务6系统报告:基于深度学习的电信网络诈骗案件分类(System Report for CCL23-Eval Task 6: Classification of Telecom Internet Fraud Cases Based on Deep Learning)
%A Li, Chenyang
%A Zhang, Long
%A Zhao, Zhongjie
%A Guo, Hui
%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 li-etal-2023-ccl23-eval-ren-wu-6xi
%X “文本分类任务作为自然语言处理领域的基础任务,在面向电信网络诈骗领域的案件分类中扮演着至关重要的角色,对于智能化案件分析具有重大意义和深远影响。本任务的目的是对给定案件描述文本进行分类,案件文本包含对案件的经过脱敏处理后的整体描述。我们首先采用Ernie预训练模型对案件内容进行微调的方法得到每个案件的类别,再使用伪标签和模型融合方法对目前的F1值进行提升,最终在CCL23-Eval任务6电信网络诈骗案件分类评测中取得第二名的成绩,该任务的评价指标F1值为0.8628,达到了较为先进的检测效果。”
%U https://aclanthology.org/2023.ccl-3.16
%P 167-172
Markdown (Informal)
[CCL23-Eval 任务6系统报告:基于深度学习的电信网络诈骗案件分类(System Report for CCL23-Eval Task 6: Classification of Telecom Internet Fraud Cases Based on Deep Learning)](https://aclanthology.org/2023.ccl-3.16) (Li et al., CCL 2023)
ACL