review-article

Semantic Relation Extraction: A Review of Approaches, Datasets, and Evaluation Methods With Looking at the Methods and Datasets in the Persian Language

Authors:

Hamid Gharagozlou,

Javad Mohammadzadeh,

Azam Bastanfard,

Saeed Shiry GhidaryAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 7

Article No.: 189, Pages 1 - 29

https://doi.org/10.1145/3592601

Published: 20 July 2023 Publication History

Abstract

A large volume of unstructured data, especially text data, is generated and exchanged daily. Consequently, the importance of extracting patterns and discovering knowledge from textual data is significantly increasing. As the task of automatically recognizing the relations between two or more entities, semantic relation extraction has a prominent role in the exploitation of raw text. This article surveys different approaches and types of relation extraction in English and the most prominent proposed methods in Persian. We also introduce, analyze, and compare the most important datasets available for relation extraction in Persian and English. Furthermore, traditional and emerging evaluation metrics for supervised, semi-supervised, and unsupervised methods are described, along with pointers to commonly used performance evaluation datasets. Finally, we briefly describe challenges in extracting relationships in Persian and English and dataset creation challenges.

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Index Terms

Semantic Relation Extraction: A Review of Approaches, Datasets, and Evaluation Methods With Looking at the Methods and Datasets in the Persian Language
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
2. General and reference
  1. Document types
    1. Surveys and overviews

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 22, Issue 7

July 2023

422 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3610376

Editor:
Imed Zitouni
Google, USA

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Publication History

Published: 20 July 2023

Online AM: 14 April 2023

Accepted: 08 April 2023

Revised: 04 April 2023

Received: 02 May 2022

Published in TALLIP Volume 22, Issue 7

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Ju XLin CLee SWei S(2024)Melanoma classification using generative adversarial network and proximal policy optimizationPhotochemistry and Photobiology10.1111/php.14006Online publication date: 30-Jul-2024
https://doi.org/10.1111/php.14006
Li MZhang JAlizadehsani RPławiak P(2024)A Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution AlgorithmIEEE Access10.1109/ACCESS.2024.342935912(100559-100580)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3429359
Li JGu WOta KHasegawa S(2024)Object Recognition from Scientific Document Based on Compartment and Text Blocks Refinement FrameworkSN Computer Science10.1007/s42979-024-03130-75:7Online publication date: 23-Aug-2024
https://dl.acm.org/doi/10.1007/s42979-024-03130-7
Xu Y(2024)Advanced RUL Estimation for Lithium-Ion Batteries: Integrating Attention-Based LSTM with Mutual Learning-enhanced Artificial Bee Colony OptimizationJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-01123-xOnline publication date: 4-Aug-2024
https://doi.org/10.1007/s40031-024-01123-x
Adilakshmi KSrinivas MAnuradha KSrilakshmi V(2024)Aspect term extraction and optimized deep learning for sentiment classificationSocial Network Analysis and Mining10.1007/s13278-024-01375-x14:1Online publication date: 24-Nov-2024
https://doi.org/10.1007/s13278-024-01375-x
Dai ZHuang T(2024)Smartphone detector examination for transportation mode identification utilizing imbalanced maximizing-area under the curve proximal support vector machineSignal, Image and Video Processing10.1007/s11760-024-03479-518:11(8361-8377)Online publication date: 13-Aug-2024
https://doi.org/10.1007/s11760-024-03479-5

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