Relation of the Relations: A New Paradigm of the Relation Extraction Problem

• URL: http://arxiv.org/abs/2006.03719v2
• Date: Mon, 12 Oct 2020 11:45:16 GMT
• Title: Relation of the Relations: A New Paradigm of the Relation Extraction Problem
• Authors: Zhijing Jin, Yongyi Yang, Xipeng Qiu, Zheng Zhang
• Abstract summary: We propose a new paradigm of Relation Extraction (RE) that considers as a whole the predictions of all relations in the same context. We develop a data-driven approach that does not require hand-crafted rules but learns by itself the relation of relations (RoR) using Graph Neural Networks and a relation matrix transformer. Experiments show that our model outperforms the state-of-the-art approaches by +1.12% on the ACE05 dataset and +2.55% on SemEval 2018 Task 7.2.
• Score: 52.21210549224131
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In natural language, often multiple entities appear in the same text. However, most previous works in Relation Extraction (RE) limit the scope to identifying the relation between two entities at a time. Such an approach induces a quadratic computation time, and also overlooks the interdependency between multiple relations, namely the relation of relations (RoR). Due to the significance of RoR in existing datasets, we propose a new paradigm of RE that considers as a whole the predictions of all relations in the same context. Accordingly, we develop a data-driven approach that does not require hand-crafted rules but learns by itself the RoR, using Graph Neural Networks and a relation matrix transformer. Experiments show that our model outperforms the state-of-the-art approaches by +1.12\% on the ACE05 dataset and +2.55\% on SemEval 2018 Task 7.2, which is a substantial improvement on the two competitive benchmarks.

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