A Position Encoding Convolutional Neural Network Based on Dependency Tree for Relation Classification

Abstract

With the renaissance of neural network in recent years, relation classification has again become a research hotspot in natural language processing, and leveraging parse trees is a common and effective method of tackling this problem. In this work, we offer a new perspective on utilizing syntactic information of dependency parse tree and present a position encoding convolutional neural network (PECNN) based on dependency parse tree for relation classification. First, treebased position features are proposed to encode the relative positions of words in dependency trees and help enhance the word representations. Then, based on a redefinition of "context", we design two kinds of tree-based convolution kernels for capturing the semantic and structural information provided by dependency trees. Finally, the features extracted by convolution module are fed to a classifier for labelling the semantic relations. Experiments on the benchmark dataset show that PECNN outperforms state-of-the-art approaches. We also compare the effect of different position features and visualize the influence of treebased position feature by tracing back the convolution process.

Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures

“…Although Deep Neural Networks (DNN) such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have achieved state-of-the-art results on within-sentence relation extraction (Zeng et al, 2014;Liu et al, 2015;Santos et al, 2015;Nguyen and Grishman, 2015;Yang et al, 2016;, there are limited studies on SF using DNN. Adel and Schütze (2015) and Adel et al (2016) exploited DNN for SF but did not achieve comparable results as traditional methods.…”

Syntax-Aware Neural Semantic Role Labeling

“…In order to use syntactic trees, Yang et al (2016) extend the position features and propose the tree-based position features (TPF) for the task of relation classification.…”

“…where f i is the TPF of w i . 1 Yang et al (2016) propose two versions of TPF. We directly use the Tree-based Position Feature 2 due to its better performance.…”

Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework

“…These works, such as (Zelenko et al, 2002;Zhou et al, 2005;Bunescu and Mooney, 2005), although achieving good performance, rely on carefully selected features and well labelled dataset. Recently, neural network models, have been used in (Zeng et al, 2014;dos Santos et al, 2015;Yang et al, 2016;Xu et al, 2015;Miwa and Bansal, 2016) for supervised relation extraction. These models avoid feature engineering and are shown to improve upon previous models.…”