Relation Extraction with Self-determined Graph Convolutional Network

• URL: http://arxiv.org/abs/2008.00441v2
• Date: Thu, 27 Aug 2020 05:55:43 GMT
• Title: Relation Extraction with Self-determined Graph Convolutional Network
• Authors: Sunil Kumar Sahu, Derek Thomas, Billy Chiu, Neha Sengupta, Mohammady Mahdy
• Abstract summary: Relation Extraction is a way of obtaining the semantic relationship between entities in text. The state-of-the-art methods use linguistic tools to build a graph for the text in which the entities appear. We propose a novel model, the Self-determined Graph Convolutional Network (SGCN), which determines a weighted graph using a self-attention mechanism.
• Score: 9.752388851329664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Relation Extraction is a way of obtaining the semantic relationship between entities in text. The state-of-the-art methods use linguistic tools to build a graph for the text in which the entities appear and then a Graph Convolutional Network (GCN) is employed to encode the pre-built graphs. Although their performance is promising, the reliance on linguistic tools results in a non end-to-end process. In this work, we propose a novel model, the Self-determined Graph Convolutional Network (SGCN), which determines a weighted graph using a self-attention mechanism, rather using any linguistic tool. Then, the self-determined graph is encoded using a GCN. We test our model on the TACRED dataset and achieve the state-of-the-art result. Our experiments show that SGCN outperforms the traditional GCN, which uses dependency parsing tools to build the graph.

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