Open Access

REVIEW

A Review of Joint Extraction Techniques for Relational Triples Based on NYT and WebNLG Datasets

Chenglong Mi¹, Huaibin Qin^1,*, Quan Qi¹, Pengxiang Zuo²

1 School of Information Science and Technology, Shihezi University, Shihezi, 832000, China
2 School of Medicine, Shihezi University, Shihezi, 832000, China

* Corresponding Author: Huaibin Qin. Email:

Computers, Materials & Continua 2025, 82(3), 3773-3796. https://doi.org/10.32604/cmc.2024.059455

Received 08 October 2024; Accepted 11 December 2024; Issue published 06 March 2025

Abstract

In recent years, with the rapid development of deep learning technology, relational triplet extraction techniques have also achieved groundbreaking progress. Traditional pipeline models have certain limitations due to error propagation. To overcome the limitations of traditional pipeline models, recent research has focused on jointly modeling the two key subtasks-named entity recognition and relation extraction-within a unified framework. To support future research, this paper provides a comprehensive review of recently published studies in the field of relational triplet extraction. The review examines commonly used public datasets for relational triplet extraction techniques and systematically reviews current mainstream joint extraction methods, including joint decoding methods and parameter sharing methods, with joint decoding methods further divided into table filling, tagging, and sequence-to-sequence approaches. In addition, this paper also conducts small-scale replication experiments on models that have performed well in recent years for each method to verify the reproducibility of the code and to compare the performance of different models under uniform conditions. Each method has its own advantages in terms of model design, task handling, and application scenarios, but also faces challenges such as processing complex sentence structures, cross-sentence relation extraction, and adaptability in low-resource environments. Finally, this paper systematically summarizes each method and discusses the future development prospects of joint extraction of relational triples.

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