Piecewise graph convolutional network with edge-level attention for relation extraction

Graph Convolutional Network (GCN) is a critical method to capture non-sequential information of sentences and recognize long-distance syntactic information. However, the adjacency matrix of GCN has two problems: redundant syntactic information and wrong dependency parsing results. Because the syntactic information is represented by unweighted adjacency matrices in most existing GCN methods. Toward this end, we propose a novel model, PGCN-EA, using Piecewise Graph Convolutional Network with Edge-level Attention to address these two problems. In specific, we first employ the piecewise adjacency matrix based on entity pair, which aims to dynamically reduce the sentence’s redundant features. Second, we propose Edge-level Attention to assign the different weights among nodes based on GCN’s input and create the weight adjacency matrix, emphasizing the importance of child words with the target word and alleviating the influence of wrong dependency parsing. Our model on a benchmark dataset has carried out extensive experiments and achieved the best PR curve as compared to seven baseline models, which are at least more than 2.3%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.3\%$$\end{document}.