QuAChIE: Question Answering based Chinese Information Extraction System

被引：4

作者：

Ru, Dongyu ^{[1
]}

Wang, Zhenghui ^{[1
]}

Qiu, Lin ^{[1
]}

Zhou, Hao ^{[2
]}

Li, Lei ^{[2
]}

Zhang, Weinan ^{[1
]}

Yu, Yong ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Shanghai, Peoples R China

[2] Bytedance AI Lab, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20) | 2020年

关键词：

Information Extraction; Question Answering;

D O I：

10.1145/3397271.3401411

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we present the design of QuAChIE, a Question Answering based Chinese Information Extraction system. QuAChIE mainly depends on a well-trained question answering model to extract high-quality triples. The group of head entity and relation are regarded as a question given the input text as the context. For the training and evaluation of each model in the system, we build a large-scale information extraction dataset using Wikidata and Wikipedia pages by distant supervision. The advanced models implemented on top of the pre-trained language model and the enormous distant supervision data enable QuAChIE to extract relation triples from documents with cross-sentence correlations. The experimental results on the test set and the case study based on the interactive demonstration show its satisfactory Information Extraction quality on Chinese document-level texts.

引用

页码：2177 / 2180

页数：4

共 13 条

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Xu Jingjing, 2017, ABS171107010 CORR

[12]

Zhang Y, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P1554

[13] Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme [J].

Zheng, Suncong ;

Wang, Feng ;

Bao, Hongyun ;

Hao, Yuexing ;

Zhou, Peng ;

Xu, Bo .

PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, 2017, :1227-1236

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