Graph Neural Networks for Natural Language Processing

Abstract

By constructing graph-structured data from the input data, Graph Neural Network (GNN) enhances the performance of numerous Natural Language Processing (NLP) tasks. In this thesis, we mainly focus on two aspects of NLP: text classification and knowledge graph completion. TextGCN shows excellent performance in text classification by leveraging the graph structure of the entire corpus without using any external resources, especially under a limited labelled data setting. Two questions are explored: (1) Under the transductive semi-supervised setting, how to utilize the documents better and learn the complex relationship between nodes. (2) How to transform TextGCN into an inductive model and also reduce the time and space complexity? In detail, firstly, a comprehensive analysis was conducted on TextGCN and its variants. Secondly, we propose ME-GCN, a novel method for text classification that utilizes multi-dimensional edge features in a graph neural network (GNN) for the first time. It uses the corpus-trained word and document-based edge features for semi-supervised classification and has been shown to be effective through experiments on benchmark datasets under the limited labelled data setting. Thirdly, InducT-GCN, an inductive framework for GCN-based text classification that does not require additional resources is introduced. The framework introduces a novel approach to make transductive GCN-based text classification models inductive, improving performance and reducing time and space complexity. Most existing work for Temporal Knowledge Graph Completion (TKGC) overlooks the significance of explicit temporal information and fails to skip irrelevant snapshots based on the entity-related relation in the query. To address this, we introduced Re-Temp (Relation-Aware Temporal Representation Learning), a model that leverages explicit temporal embedding and a skip information flow after each timestamp to eliminate unnecessary information for prediction