Recent Trends in the Use of Graph Neural Network Models for Natural Language Processing
Burcu Yılmaz, Hilal Genc, Mustafa Ağriman, Bugra Kaan Demirdover, Mert Erdemir, Gokhan Simsek, Pınar Karagöz · Advances in systems analysis, software engineering, and high performance computing book series · 2019
Graphs are powerful data structures that allow us to represent varying relationships within data. In the past, due to the difficulties related to the time complexities of processing graph models, graphs rarely involved machine learning tasks. In recent years, especially with the new advances in deep learning techniques, increasing number of graph models related to the feature engineering and machine learning are proposed. Recently, there has been an increase in approaches that automatically learn to encode graph structure into low dimensional embedding. These approaches are accompanied by models for machine learning tasks, and they fall into two categories. The first one focuses on feature engineering techniques on graphs. The second group of models assembles graph structure to learn a graph neighborhood in the machine learning model. In this chapter, the authors focus on the advances in applications of graphs on NLP using the recent deep learning models.