@inproceedings{danqingxin-etal-2024-mian,
title = "面向工艺文本的实体与关系最近邻联合抽取模型(Nearest Neighbor Joint Extraction Model for Entity and Relationship in Process Text)",
author = "Yang, Danqingxin and
Wang, Peiyan and
Xu, Lijun",
editor = "Maosong, Sun and
Jiye, Liang and
Xianpei, Han and
Zhiyuan, Liu and
Yulan, He",
booktitle = "Proceedings of the 23rd {C}hinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.31/",
pages = "406--417",
language = "zho",
abstract = "``该 文 研 究 工 艺 文 本 中 实 体 关 系 联 合 抽 取 问 题 , 提 出 了 最 近 邻 联 合 抽 取 模 型(NNJE)。NNJE利用工艺文本中实体边界字间搭配规律建模外显记忆,通过最近邻方法在某种指定关系下为待预测组合检索出具有相似字间搭配的实例,为实体边界识别以及实体对组合提供更有力的限制条件,提升模型预测准确率,改善模型性能。实验设置了工艺文本关系数据集。实验结果表明,该文方法较基线模型准确率P值提高了3.53{\%},F1值提升了1.03{\%},优于PURE、CasRel、PRGC与TPlinker等方法,表明提出的方法能够有效地提升三元组抽取效果。''"
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="danqingxin-etal-2024-mian">
<titleInfo>
<title>面向工艺文本的实体与关系最近邻联合抽取模型(Nearest Neighbor Joint Extraction Model for Entity and Relationship in Process Text)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Danqingxin</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Peiyan</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lijun</namePart>
<namePart type="family">Xu</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 1: Main Conference)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sun</namePart>
<namePart type="family">Maosong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liang</namePart>
<namePart type="family">Jiye</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Han</namePart>
<namePart type="family">Xianpei</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liu</namePart>
<namePart type="family">Zhiyuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">He</namePart>
<namePart type="family">Yulan</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>“该 文 研 究 工 艺 文 本 中 实 体 关 系 联 合 抽 取 问 题 , 提 出 了 最 近 邻 联 合 抽 取 模 型(NNJE)。NNJE利用工艺文本中实体边界字间搭配规律建模外显记忆,通过最近邻方法在某种指定关系下为待预测组合检索出具有相似字间搭配的实例,为实体边界识别以及实体对组合提供更有力的限制条件,提升模型预测准确率,改善模型性能。实验设置了工艺文本关系数据集。实验结果表明,该文方法较基线模型准确率P值提高了3.53%,F1值提升了1.03%,优于PURE、CasRel、PRGC与TPlinker等方法,表明提出的方法能够有效地提升三元组抽取效果。”</abstract>
<identifier type="citekey">danqingxin-etal-2024-mian</identifier>
<location>
<url>https://aclanthology.org/2024.ccl-1.31/</url>
</location>
<part>
<date>2024-07</date>
<extent unit="page">
<start>406</start>
<end>417</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T 面向工艺文本的实体与关系最近邻联合抽取模型(Nearest Neighbor Joint Extraction Model for Entity and Relationship in Process Text)
%A Yang, Danqingxin
%A Wang, Peiyan
%A Xu, Lijun
%Y Maosong, Sun
%Y Jiye, Liang
%Y Xianpei, Han
%Y Zhiyuan, Liu
%Y Yulan, He
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F danqingxin-etal-2024-mian
%X “该 文 研 究 工 艺 文 本 中 实 体 关 系 联 合 抽 取 问 题 , 提 出 了 最 近 邻 联 合 抽 取 模 型(NNJE)。NNJE利用工艺文本中实体边界字间搭配规律建模外显记忆,通过最近邻方法在某种指定关系下为待预测组合检索出具有相似字间搭配的实例,为实体边界识别以及实体对组合提供更有力的限制条件,提升模型预测准确率,改善模型性能。实验设置了工艺文本关系数据集。实验结果表明,该文方法较基线模型准确率P值提高了3.53%,F1值提升了1.03%,优于PURE、CasRel、PRGC与TPlinker等方法,表明提出的方法能够有效地提升三元组抽取效果。”
%U https://aclanthology.org/2024.ccl-1.31/
%P 406-417
Markdown (Informal)
[面向工艺文本的实体与关系最近邻联合抽取模型(Nearest Neighbor Joint Extraction Model for Entity and Relationship in Process Text)](https://aclanthology.org/2024.ccl-1.31/) (Yang et al., CCL 2024)
ACL