RTJTN: Relational Triplet Joint Tagging Network for Joint Entity and Relation Extraction

Area of Science:

• Natural Language Processing
• Information Extraction
• Machine Learning

Background:

• Extracting entities and relations from unstructured text is a key challenge in NLP.
• Existing methods often suffer from error propagation due to sequential processing.
• Relation overlapping and error iteration are significant limitations in current approaches.

Purpose of the Study:

• To develop a novel network for joint entity and relation extraction.
• To address the limitations of sequential processing and error iteration.
• To improve the accuracy of relation extraction, especially in cases of overlapping relations.

Main Methods:

• Introduction of a relational triplet joint tagging network (RTJTN).
• Implementation of a joint entities and relations tagging layer for simultaneous extraction.
• Development of a relational triplet judgment layer to resolve relation overlaps.

Main Results:

• The RTJTN demonstrated improved performance on English (NYT) and Chinese (DuIE 2.0, CMED) datasets.
• Achieved F1 score improvements of 1.1, 6.0, and 5.1 on NYT, DuIE 2.0, and CMED, respectively.
• Showed significant gains in handling overlapping relations and complex sentences.

Conclusions:

• The proposed RTJTN effectively mitigates error iteration in entity and relation extraction.
• The network successfully addresses the challenge of overlapping relations.
• RTJTN offers a robust solution for accurate information extraction from unstructured text.