  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.
