A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents

• URL: http://arxiv.org/abs/2108.09505v1
• Date: Sat, 21 Aug 2021 12:33:50 GMT
• Title: A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents
• Authors: Tapas Nayak and Hwee Tou Ng
• Abstract summary: We propose cross-document relation extraction, where the two entities of a relation appear in two different documents. Following this idea, we create a dataset for two-hop relation extraction, where each chain contains exactly two documents. Our proposed dataset covers a higher number of relations than the publicly available sentence-level datasets.
• Score: 29.183245395412705
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Distantly supervised datasets for relation extraction mostly focus on sentence-level extraction, and they cover very few relations. In this work, we propose cross-document relation extraction, where the two entities of a relation tuple appear in two different documents that are connected via a chain of common entities. Following this idea, we create a dataset for two-hop relation extraction, where each chain contains exactly two documents. Our proposed dataset covers a higher number of relations than the publicly available sentence-level datasets. We also propose a hierarchical entity graph convolutional network (HEGCN) model for this task that improves performance by 1.1\% F1 score on our two-hop relation extraction dataset, compared to some strong neural baselines.

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