基于大小模型结合与半监督自训练方法的古文事件抽取

Weiwei Fu; Shiquan Wang; Ruiyu Fang; Mengxiang Li; Zhongjiang He; Yongxiang Li; Shuangyong Song (宋双永)

基于大小模型结合与半监督自训练方法的古文事件抽取

Weiwei Fu (付薇薇), Shiquan Wang (王士权), Ruiyu Fang (方瑞玉), Mengxiang Li (李孟祥), Zhongjiang He (何忠江), Yongxiang Li (李永翔), Shuangyong Song (宋双永)

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use <b>...</b> for bold, <i>...</i> for italic, and <url>...</url> for URLs.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

“本文描述了队伍“TeleAI”在CCL2024古文历史事件类型抽取评测任务(CHED2024)中提交的参赛系统。该任务旨在自动识别出古代文本中的事件触发词与事件类型,其中事件类型判别被分为粗粒度和细粒度的事件类型判别两部分。为了提高古文历史事件类型抽取的性能,我们结合了大模型和小模型,并采用了半监督自训练的方法。在最终的评估中,我们在触发词识别任务得分0.763,粗粒度事件类型判别任务得分0.842,细粒度事件类型判别任务得分0.779,综合得分0.791,在所有单项任务和综合评分上均排名第一。”

Anthology ID:: 2024.ccl-3.19
Volume:: Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
Month:: July
Year:: 2024
Address:: Taiyuan, China
Editors:: Lin Hongfei, Tan Hongye, Li Bin
Venue:: CCL
SIG:
Publisher:: Chinese Information Processing Society of China
Note:
Pages:: 172–177
Language:: Chinese
URL:: https://aclanthology.org/2024.ccl-3.19/
DOI:
Bibkey:
Cite (ACL):: Weiwei Fu, Shiquan Wang, Ruiyu Fang, Mengxiang Li, Zhongjiang He, Yongxiang Li, and Shuangyong Song. 2024. 基于大小模型结合与半监督自训练方法的古文事件抽取. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 172–177, Taiyuan, China. Chinese Information Processing Society of China.
Cite (Informal):: 基于大小模型结合与半监督自训练方法的古文事件抽取 (Fu et al., CCL 2024)
Copy Citation:
PDF:: https://aclanthology.org/2024.ccl-3.19.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{weiwei-etal-2024-ji,
    title = "基于大小模型结合与半监督自训练方法的古文事件抽取",
    author = "Fu, Weiwei  and
      Wang, Shiquan  and
      Fang, Ruiyu  and
      Li, Mengxiang  and
      He, Zhongjiang  and
      Li, Yongxiang  and
      Song, Shuangyong",
    editor = "Hongfei, Lin  and
      Hongye, Tan  and
      Bin, Li",
    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-3.19/",
    pages = "172--177",
    language = "zho",
    abstract = "``本文描述了队伍{``}TeleAI{''}在CCL2024古文历史事件类型抽取评测任务(CHED2024)中提交的参赛系统。该任务旨在自动识别出古代文本中的事件触发词与事件类型,其中事件类型判别被分为粗粒度和细粒度的事件类型判别两部分。为了提高古文历史事件类型抽取的性能,我们结合了大模型和小模型,并采用了半监督自训练的方法。在最终的评估中,我们在触发词识别任务得分0.763,粗粒度事件类型判别任务得分0.842,细粒度事件类型判别任务得分0.779,综合得分0.791,在所有单项任务和综合评分上均排名第一。''"
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="weiwei-etal-2024-ji">
    <titleInfo>
        <title>基于大小模型结合与半监督自训练方法的古文事件抽取</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Weiwei</namePart>
        <namePart type="family">Fu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shiquan</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ruiyu</namePart>
        <namePart type="family">Fang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mengxiang</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhongjiang</namePart>
        <namePart type="family">He</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yongxiang</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shuangyong</namePart>
        <namePart type="family">Song</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <language>
        <languageTerm type="text">zho</languageTerm>
    </language>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lin</namePart>
            <namePart type="family">Hongfei</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tan</namePart>
            <namePart type="family">Hongye</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Li</namePart>
            <namePart type="family">Bin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Chinese Information Processing Society of China</publisher>
            <place>
                <placeTerm type="text">Taiyuan, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>“本文描述了队伍“TeleAI”在CCL2024古文历史事件类型抽取评测任务(CHED2024)中提交的参赛系统。该任务旨在自动识别出古代文本中的事件触发词与事件类型,其中事件类型判别被分为粗粒度和细粒度的事件类型判别两部分。为了提高古文历史事件类型抽取的性能,我们结合了大模型和小模型,并采用了半监督自训练的方法。在最终的评估中,我们在触发词识别任务得分0.763,粗粒度事件类型判别任务得分0.842,细粒度事件类型判别任务得分0.779,综合得分0.791,在所有单项任务和综合评分上均排名第一。”</abstract>
    <identifier type="citekey">weiwei-etal-2024-ji</identifier>
    <location>
        <url>https://aclanthology.org/2024.ccl-3.19/</url>
    </location>
    <part>
        <date>2024-07</date>
        <extent unit="page">
            <start>172</start>
            <end>177</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T 基于大小模型结合与半监督自训练方法的古文事件抽取
%A Fu, Weiwei
%A Wang, Shiquan
%A Fang, Ruiyu
%A Li, Mengxiang
%A He, Zhongjiang
%A Li, Yongxiang
%A Song, Shuangyong
%Y Hongfei, Lin
%Y Hongye, Tan
%Y Bin, Li
%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 weiwei-etal-2024-ji
%X “本文描述了队伍“TeleAI”在CCL2024古文历史事件类型抽取评测任务(CHED2024)中提交的参赛系统。该任务旨在自动识别出古代文本中的事件触发词与事件类型,其中事件类型判别被分为粗粒度和细粒度的事件类型判别两部分。为了提高古文历史事件类型抽取的性能,我们结合了大模型和小模型,并采用了半监督自训练的方法。在最终的评估中,我们在触发词识别任务得分0.763,粗粒度事件类型判别任务得分0.842,细粒度事件类型判别任务得分0.779,综合得分0.791,在所有单项任务和综合评分上均排名第一。”
%U https://aclanthology.org/2024.ccl-3.19/
%P 172-177

Download as File

Markdown (Informal)

[基于大小模型结合与半监督自训练方法的古文事件抽取](https://aclanthology.org/2024.ccl-3.19/) (Fu et al., CCL 2024)

基于大小模型结合与半监督自训练方法的古文事件抽取 (Fu et al., CCL 2024)

ACL

Weiwei Fu, Shiquan Wang, Ruiyu Fang, Mengxiang Li, Zhongjiang He, Yongxiang Li, and Shuangyong Song. 2024. 基于大小模型结合与半监督自训练方法的古文事件抽取. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 172–177, Taiyuan, China. Chinese Information Processing Society of China.