survey

Relation Extraction Using Distant Supervision: A Survey

Authors:

Alisa Smirnova,

Philippe Cudré-MaurouxAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 51, Issue 5

Article No.: 106, Pages 1 - 35

https://doi.org/10.1145/3241741

Published: 19 November 2018 Publication History

Abstract

Relation extraction is a subtask of information extraction where semantic relationships are extracted from natural language text and then classified. In essence, it allows us to acquire structured knowledge from unstructured text. In this article, we present a survey of relation extraction methods that leverage pre-existing structured or semi-structured data to guide the extraction process. We introduce a taxonomy of existing methods and describe distant supervision approaches in detail. We describe, in addition, the evaluation methodologies and the datasets commonly used for quality assessment. Finally, we give a high-level outlook on the field, highlighting open problems as well as the most promising research directions.

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Index Terms

Relation Extraction Using Distant Supervision: A Survey
1. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection
    2. Retrieval tasks and goals
      1. Information extraction

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 51, Issue 5

September 2019

791 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3271482

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

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Publication History

Published: 19 November 2018

Accepted: 01 July 2018

Revised: 01 July 2018

Received: 01 March 2018

Published in CSUR Volume 51, Issue 5

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Irrera OMarchesin SSilvello G(2024)MetaTron: advancing biomedical annotation empowering relation annotation and collaborationBMC Bioinformatics10.1186/s12859-024-05730-925:1Online publication date: 14-Mar-2024
https://doi.org/10.1186/s12859-024-05730-9
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Li JFeng SChiu B(2024)Few-Shot Relation Extraction With Dual Graph Neural Network InteractionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.327893835:10(14396-14408)Online publication date: Oct-2024
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