Recent Trends in the Use of Graph Neural Network Models for Natural Language Processing

2020-01-01
Yılmaz, Burcu
Genç, Hilal
Ağrıman, Mustafa
Demirdöver, Buğra Kaan
Erdemir, Mert
Şimşek, Gökhan
Karagöz, Pınar
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.

Suggestions

Multi-resolution visualization of large scale protein networks enriched with gene ontology annotations
Yaşar, Sevgi; Can, Tolga; Department of Computer Engineering (2009)
Genome scale protein-protein interactions (PPIs) are interpreted as networks or graphs with thousands of nodes from the perspective of computer science. PPI networks represent various types of possible interactions among proteins or genes of a genome. PPI data is vital in protein function prediction since functions of the cells are performed by groups of proteins interacting with each other and main complexes of the cell are made of proteins interacting with each other. Recent increase in protein interactio...
Adapting a Robust Model into Hybrid Implementations of Machine Learning Algorithms and Statistical Methods for Longitudinal Data
Erduran, İbrahim Hakkı; Gökalp Yavuz, Fulya; Ebegil, Meral; Department of Statistics (2021-9)
Data structures in which the same characteristics are measured repeatedly at different time points are counted among the longitudinal data types. These datasets require the use of advanced modeling techniques because of the dependency structure amongst replicates. Linear mixed models (LMM) is an advanced regression method used in the analysis of such data sets. Although the LMM method provides many flexibility and advantages, the model setup is based on a number of assumptions that are challenging to provid...
On numerical optimization theory of infinite kernel learning
Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2010-10-01)
In Machine Learning algorithms, one of the crucial issues is the representation of the data. As the given data source become heterogeneous and the data are large-scale, multiple kernel methods help to classify "nonlinear data". Nevertheless, the finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, a novel method of "infinite" kernel combinations is proposed with the help of infinite and semi-infinite programming regarding all elements in kernel space. Look...
Continuous dimensionality characterization of image structures
Felsberg, Michael; Kalkan, Sinan; Kruger, Norbert (Elsevier BV, 2009-05-04)
Intrinsic dimensionality is a concept introduced by statistics and later used in image processing to measure the dimensionality of a data set. In this paper, we introduce a continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field. By making use of a cone structure and barycentric co-ordinates, we can associate three confidences to the three different ideal cases of intrinsic dimensions corresponding to homogeneous image patche...
A tool for visualization of risk information: the Risk Box
Karakoçak, Elif; Birgönül, Mustafa Talat; Dikmen Toker, İrem; Department of Civil Engineering (2021-6-28)
Visualization is an effective way to represent data that mainly aims to make the data easier to be understood, analyzed, and processed by the users. In literature, there exist many studies focusing on the necessity and effectiveness of visualization in decision-making processes. Risk, on the other hand, is an important topic that needs to be considered for decision-makers in the project to decide on the further pathways to follow and need to be visualized in such a way to aid the decision-makers in these pr...
Citation Formats
B. Yılmaz et al., “Recent Trends in the Use of Graph Neural Network Models for Natural Language Processing,” pp. 274–289, 2020, Accessed: 00, 2021. [Online]. Available: https://www.igi-global.com/book/deep-learning-techniques-optimization-strategies/231554.