Research on joint model relation extraction method based on entity mapping

Relationship Extraction (RE) is a central task in information extraction. The use of entity mapping to address complex scenarios with overlapping triples, such as CasRel, is gaining traction, yet faces challenges such as inadequate consideration of sentence continuity, sample imbalance and data nois...

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Bibliographic Details
Published in:PloS one 2024-02, Vol.19 (2), p.e0298974-e0298974
Main Authors: Tang, Hongmei, Zhu, Dixiongxiao, Tang, Wenzhong, Wang, Shuai, Wang, Yanyang, Wang, Lihong
Format: Article
Language:English
Subjects:
Data entry
Evaluation
Information management
Methods
Relation (Philosophy)
Relationism
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Summary:Relationship Extraction (RE) is a central task in information extraction. The use of entity mapping to address complex scenarios with overlapping triples, such as CasRel, is gaining traction, yet faces challenges such as inadequate consideration of sentence continuity, sample imbalance and data noise. This research introduces an entity mapping-based method CasRelBLCF building on CasRel. The main contributions include: A joint decoder for the head entity, utilizing Bi-LSTM and CRF, integration of the Focal Loss function to tackle sample imbalance and a reinforcement learning-based noise reduction method for handling dataset noise. Experiments on relation extraction datasets indicate the superiority of the CasRelBLCF model and the enhancement on model's performance of the noise reduction method.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0298974