research-article

QuAChIE: Question Answering based Chinese Information Extraction System

Authors:

Lin Qiu,

Lei Li,

Yong YuAuthors Info & Claims

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2177 - 2180

https://doi.org/10.1145/3397271.3401411

Published: 25 July 2020 Publication History

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Abstract

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.

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Cited By

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Lu SZhang WCheng L(2024)Combining Sentence-based Relational Features with Biaffine Mechanism for Triple ExtractionProceedings of the 2024 16th International Conference on Machine Learning and Computing10.1145/3651671.3651747(121-127)Online publication date: 2-Feb-2024
https://dl.acm.org/doi/10.1145/3651671.3651747
Sun JXing ZXia XLu QXu XZhu L(2023)Aspect-level Information Discrepancies across Heterogeneous Vulnerability Reports: Severity, Types and Detection MethodsACM Transactions on Software Engineering and Methodology10.1145/362473433:2(1-38)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3624734
He JHu YWang LXu XLiu NLiu HShen HChen HDuh WHuang HKato MMothe JPoblete B(2023)Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding ModelsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591670(569-579)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591670
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Index Terms

QuAChIE: Question Answering based Chinese Information Extraction System
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction
      2. Question answering

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Published In

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2020

2548 pages

ISBN:9781450380164

DOI:10.1145/3397271

General Chairs:
Jimmy Huang
York University, Canada
,
Yi Chang
Jilin University, China
,
Xueqi Cheng
Chinese Academy of Sciences, China
,
Program Chairs:
Jaap Kamps
University of Amsterdam, Netherlands
,
Vanessa Murdock
Amazon, U.S.A.
,
Ji-Rong Wen
Renmin University of China, China
,
Yiqun Liu
Tsinghua University, China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2020

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NSFC

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SIGIR '20

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SIGIR

SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval

July 25 - 30, 2020

Virtual Event, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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Lu SZhang WCheng L(2024)Combining Sentence-based Relational Features with Biaffine Mechanism for Triple ExtractionProceedings of the 2024 16th International Conference on Machine Learning and Computing10.1145/3651671.3651747(121-127)Online publication date: 2-Feb-2024
https://dl.acm.org/doi/10.1145/3651671.3651747
Sun JXing ZXia XLu QXu XZhu L(2023)Aspect-level Information Discrepancies across Heterogeneous Vulnerability Reports: Severity, Types and Detection MethodsACM Transactions on Software Engineering and Methodology10.1145/362473433:2(1-38)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3624734
He JHu YWang LXu XLiu NLiu HShen HChen HDuh WHuang HKato MMothe JPoblete B(2023)Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding ModelsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591670(569-579)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591670
Yang EHao FShang JChen XPark D(2023)BT-CKBQA: An efficient approach for Chinese knowledge base question answeringData & Knowledge Engineering10.1016/j.datak.2023.102204147(102204)Online publication date: Sep-2023
https://doi.org/10.1016/j.datak.2023.102204

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