@inproceedings{wenjun-etal-2024-ji,
title = "基于增量预训练与外部知识的古文历史事件检测",
author = "Wenjun, Kang and
Jiali, Zuo and
Yiyu, Hu and
Mingwen, Wang",
editor = "Lin, Hongfei and
Tan, Hongye and
Li, Bin",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-3.21/",
pages = "191--200",
language = "zho",
abstract = "{\textquotedblleft}古文历史事件检测任务旨在识别文本中的事件触发词和类型。为了解决传统pipeline方法容易产生级联错误传播,以及大多数事件检测方法仅依赖句子层面信息的问题,本文提出了一种结合外部信息和全局对应矩阵的联合抽取模型EIGC,以实现触发词和事件类型的精确抽取。此外,本文还整理了一个包含{\textquotedblleft}二十四史{\textquotedblright}等古汉语文献的数据集,共计约97万条古汉语文本,并利用该文本对BERT-Ancient-Chinese进行增量预训练。最终,本文所提出的模型在三个任务上的总F1值达到了76.2{\%},验证了该方法的有效性。{\textquotedblright}"
}
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<abstract>“古文历史事件检测任务旨在识别文本中的事件触发词和类型。为了解决传统pipeline方法容易产生级联错误传播,以及大多数事件检测方法仅依赖句子层面信息的问题,本文提出了一种结合外部信息和全局对应矩阵的联合抽取模型EIGC,以实现触发词和事件类型的精确抽取。此外,本文还整理了一个包含“二十四史”等古汉语文献的数据集,共计约97万条古汉语文本,并利用该文本对BERT-Ancient-Chinese进行增量预训练。最终,本文所提出的模型在三个任务上的总F1值达到了76.2%,验证了该方法的有效性。”</abstract>
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%0 Conference Proceedings
%T 基于增量预训练与外部知识的古文历史事件检测
%A Wenjun, Kang
%A Jiali, Zuo
%A Yiyu, Hu
%A Mingwen, Wang
%Y Lin, Hongfei
%Y Tan, Hongye
%Y Li, Bin
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F wenjun-etal-2024-ji
%X “古文历史事件检测任务旨在识别文本中的事件触发词和类型。为了解决传统pipeline方法容易产生级联错误传播,以及大多数事件检测方法仅依赖句子层面信息的问题,本文提出了一种结合外部信息和全局对应矩阵的联合抽取模型EIGC,以实现触发词和事件类型的精确抽取。此外,本文还整理了一个包含“二十四史”等古汉语文献的数据集,共计约97万条古汉语文本,并利用该文本对BERT-Ancient-Chinese进行增量预训练。最终,本文所提出的模型在三个任务上的总F1值达到了76.2%,验证了该方法的有效性。”
%U https://aclanthology.org/2024.ccl-3.21/
%P 191-200
Markdown (Informal)
[基于增量预训练与外部知识的古文历史事件检测](https://aclanthology.org/2024.ccl-3.21/) (Wenjun et al., CCL 2024)
ACL
- Kang Wenjun, Zuo Jiali, Hu Yiyu, and Wang Mingwen. 2024. 基于增量预训练与外部知识的古文历史事件检测. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 191–200, Taiyuan, China. Chinese Information Processing Society of China.