short-paper

A Conditional Cascade Model for Relational Triple Extraction

Authors:

Bochao LiAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 3393 - 3397

https://doi.org/10.1145/3459637.3482045

Published: 30 October 2021 Publication History

Abstract

Tagging based methods are one of the mainstream methods in relational triple extraction. However, most of them suffer from the class imbalance issue greatly. Here we propose a novel tagging based model that addresses this issue from following two aspects. First, at the model level, we propose a three-step extraction framework that can reduce the total number of samples greatly, which implicitly decreases the severity of the mentioned issue. Second, at the intra-model level, we propose a confidence threshold based cross entropy loss that can directly neglect some samples in the major classes. We evaluate the proposed model on NYT and WebNLG. Extensive experiments show that it can address the mentioned issue effectively and achieves state-of-the-art results on both datasets. The source code of our model is available at: https://github.com/neukg/ConCasRTE.

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Tagging based methods are one of the mainstream methods in relational triple extraction. However, most of them suffer from the class imbalance issue greatly. Here we propose a novel tagging based model that addresses this issue from following two aspects. First, at the model level, we propose a three-step extraction framework that can reduce the total number of samples greatly, which implicitly decreases the severity of the mentioned issue. Second, at the intramodel level, we propose a confidence threshold based cross entropy loss that can directly neglect some samples in the major classes. We evaluate the proposed model on NYT and WebNLG. Extensive experiments show that it can address the mentioned issue effectively and achieves state-of-the-art results on both datasets.

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Cited By

Liu YFu LXia XZhang Y(2024)Exploring the Role of Self-Adaptive Feature Words in Relation Quintuple Extraction for Scientific LiteratureApplied Sciences10.3390/app1410402014:10(4020)Online publication date: 9-May-2024
https://doi.org/10.3390/app14104020
Xiao YZheng SLeng JGao RFu ZHong J(2024)An assembly process planning pipeline for industrial electronic equipment based on knowledge graph with bidirectional extracted knowledge from historical process documentsJournal of Intelligent Manufacturing10.1007/s10845-024-02423-1Online publication date: 5-Jun-2024
https://doi.org/10.1007/s10845-024-02423-1
Xiao YZheng STian ZShi JDu XHong J(2024)Bidirectional network-based relational triple extraction with prior relation mechanismKnowledge and Information Systems10.1007/s10115-024-02241-0Online publication date: 24-Sep-2024
https://doi.org/10.1007/s10115-024-02241-0

Index Terms

A Conditional Cascade Model for Relational Triple Extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

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Published: 30 October 2021

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Cited By

Liu YFu LXia XZhang Y(2024)Exploring the Role of Self-Adaptive Feature Words in Relation Quintuple Extraction for Scientific LiteratureApplied Sciences10.3390/app1410402014:10(4020)Online publication date: 9-May-2024
https://doi.org/10.3390/app14104020
Xiao YZheng SLeng JGao RFu ZHong J(2024)An assembly process planning pipeline for industrial electronic equipment based on knowledge graph with bidirectional extracted knowledge from historical process documentsJournal of Intelligent Manufacturing10.1007/s10845-024-02423-1Online publication date: 5-Jun-2024
https://doi.org/10.1007/s10845-024-02423-1
Xiao YZheng STian ZShi JDu XHong J(2024)Bidirectional network-based relational triple extraction with prior relation mechanismKnowledge and Information Systems10.1007/s10115-024-02241-0Online publication date: 24-Sep-2024
https://doi.org/10.1007/s10115-024-02241-0

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