Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence

• Published in: arXiv.org
• Main Authors: Park, Seongsik, Kim, Harksoo
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 05.03.2021
• Subjects: Computer Science - Computation and Language; Information retrieval; Semantics
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2103.03509

Relation extraction is a type of information extraction task that recognizes semantic relationships between entities in a sentence. Many previous studies have focused on extracting only one semantic relation between two entities in a single sentence. However, multiple entities in a sentence are associated through various relations. To address this issue, we propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject-object relations using a forward object decoder. Then, it finds 1-to-n subject-object relations using a backward subject decoder. Our experiments confirmed that the proposed model outperformed previous models, with an F1-score of 80.8% for the ACE-2005 corpus and an F1-score of 78.3% for the NYT corpus.