Chenglong Mi1, Huaibin Qin1,*, Quan Qi1, Pengxiang Zuo2
CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3773-3796, 2025, DOI:10.32604/cmc.2024.059455
- 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… More >
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