Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence
This addresses the need for more comprehensive relation extraction in natural language processing, though it appears incremental as it builds on existing pointer network and attention mechanisms.
The paper tackled the problem of extracting multiple semantic relations between entities in a single sentence, which previous studies often limited to one relation, and achieved an F1-score of 80.8% on the ACE-2005 corpus and 78.3% on the NYT corpus.
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.