@inproceedings{liu-etal-2021-mian,
title = "面向对话文本的实体关系抽取(Entity Relation Extraction for Dialogue Text)",
author = "Liu, Liang and
Kong, Fang",
editor = "Li, Sheng and
Sun, Maosong and
Liu, Yang and
Wu, Hua and
Liu, Kang and
Che, Wanxiang and
He, Shizhu and
Rao, Gaoqi",
booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
month = aug,
year = "2021",
address = "Huhhot, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2021.ccl-1.31",
pages = "327--338",
abstract = "实体关系抽取旨在从文本中抽取出实体之间的语义关系,是自然语言处理的一项基本任务。在新闻报道、维基百科等规范文本上该任务的研究相对丰富,已经取得了一定的效果,但面向对话文本的相关研究还处于起始阶段。相较于规范文本,用于实体关系抽取的对话语料规模较小,对话文本的有效特征难以捕获,这使得面向对话文本的实体关系抽取更具挑战。该文针对这一任务提出了一个基于Star-Transformer的实体关系抽取模型,通过融入高速网络进行信息桥接,并在此基础上融入交互信息和知识,最后使用多任务学习机制进一步提升模型的性能。在DialogRE公开数据集上实验得到F1值为55.7{\%},F1c值为52.3{\%},证明了提出方法的有效性。",
language = "Chinese",
}
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<abstract>实体关系抽取旨在从文本中抽取出实体之间的语义关系,是自然语言处理的一项基本任务。在新闻报道、维基百科等规范文本上该任务的研究相对丰富,已经取得了一定的效果,但面向对话文本的相关研究还处于起始阶段。相较于规范文本,用于实体关系抽取的对话语料规模较小,对话文本的有效特征难以捕获,这使得面向对话文本的实体关系抽取更具挑战。该文针对这一任务提出了一个基于Star-Transformer的实体关系抽取模型,通过融入高速网络进行信息桥接,并在此基础上融入交互信息和知识,最后使用多任务学习机制进一步提升模型的性能。在DialogRE公开数据集上实验得到F1值为55.7%,F1c值为52.3%,证明了提出方法的有效性。</abstract>
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%0 Conference Proceedings
%T 面向对话文本的实体关系抽取(Entity Relation Extraction for Dialogue Text)
%A Liu, Liang
%A Kong, Fang
%Y Li, Sheng
%Y Sun, Maosong
%Y Liu, Yang
%Y Wu, Hua
%Y Liu, Kang
%Y Che, Wanxiang
%Y He, Shizhu
%Y Rao, Gaoqi
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 August
%I Chinese Information Processing Society of China
%C Huhhot, China
%G Chinese
%F liu-etal-2021-mian
%X 实体关系抽取旨在从文本中抽取出实体之间的语义关系,是自然语言处理的一项基本任务。在新闻报道、维基百科等规范文本上该任务的研究相对丰富,已经取得了一定的效果,但面向对话文本的相关研究还处于起始阶段。相较于规范文本,用于实体关系抽取的对话语料规模较小,对话文本的有效特征难以捕获,这使得面向对话文本的实体关系抽取更具挑战。该文针对这一任务提出了一个基于Star-Transformer的实体关系抽取模型,通过融入高速网络进行信息桥接,并在此基础上融入交互信息和知识,最后使用多任务学习机制进一步提升模型的性能。在DialogRE公开数据集上实验得到F1值为55.7%,F1c值为52.3%,证明了提出方法的有效性。
%U https://aclanthology.org/2021.ccl-1.31
%P 327-338
Markdown (Informal)
[面向对话文本的实体关系抽取(Entity Relation Extraction for Dialogue Text)](https://aclanthology.org/2021.ccl-1.31) (Liu & Kong, CCL 2021)
ACL