Abstract

Natural language processing (NLP) and understanding aim to read from unformatted text to accomplish different tasks. While word embeddings learned by deep neural networks are widely used, the underlying linguistic and semantic structures of text pieces cannot be fully exploited in these representations. Graph is a natural way to capture the connections between different text pieces, such as entities, sentences, and documents. To overcome the limits in vector space models, researchers combine deep learning models with graph-structured representations for various tasks in NLP and text mining. Such combinations help to make full use of both the structural information in text and the representation learning ability of deep neural networks. In this chapter, we introduce the various graph representations that are extensively used in NLP, and show how different NLP tasks can be tackled from a graph perspective.We summarize recent research works on graph-based NLP, and discuss two case studies related to graph-based text clustering, matching, and multihop machine reading comprehension in detail. Finally, we provide a synthesis about the important open problems of this subfield.

Contents

• Introduction
• Modeling Text as Graphs
• Graph Representations in Natural Language Processing
• Tackling Natural Language Processing Tasks from a Graph Perspective
• Case Study 1: Graph-based Text Clustering and Matching
• Graph-based Clustering for Hot Events Discovery and Organization
• Long Document Matching with Graph Decomposition and Convolution
• Case Study 2: Graph-based Multi-Hop Reading Comprehension
• Future Directions
• Summary

Citation