Relation Extraction (RE) aims to label relations between groups of marked entities in raw text. Most current RE models learn context-aware representations of the target entities that are then used to establish relation between them. This works well for intrasentence RE and we call them first-order relations. However, this methodology can sometimes fail to capture complex and long dependencies. To address this, we hypothesize that at times two target entities can be explicitly connected via a context token. We refer to such indirect relations as second-order relations and describe an efficient implementation for computing them. These second-order relation scores are then combined with first-order relation scores. Our empirical results show that the proposed method leads to state-of-theart performance over two biomedical datasets.
Finally, ACM's RE function features a method that combines second-order relation scores-which utilizes a context token that connects the two target entities-and first-order relation scores REF .
Relation Extraction using Explicit Context Conditioning
ArXiv
ArXiv
Computer Science