Recurrent Graph Neural Networks for Text Classification

Wei, Xinde; Huang, Hai; Ma, Longxuan; Yang, Ze; Xu, Liutong

doi:10.1109/icsess49938.2020.9237709

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…Wei et al [25] propose a graph recurrent neural network for text categorization. In the paper, the authors compare their method to widely used text classification methods, such as CNN, LSTM, RNN, and achieve higher accuracy than the other methods using GloVe with 300-dimensional embeddings to initialize word representation.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Most papers report using only one dataset (14 papers), but the amount varies from 1 to 45 datasets. The papers that perform experiments on more than three datasets are: Silva, Almeida, and Yamakami [24] (45 datasets), Liu et al [15] (12 datasets), Liu and Guo [36] and Tellez et al [18] (7 datasets), Zheng and Zheng [37] and Xu et al [28] (6 datasets), and Conover et al [21] (5 datasets), Shi and Lu [38] and Wei et al [25] (4 datasets). We also identify that eight papers analyze three datasets, and five papers analyze two datasets.…”

Section: Characteristics Of the Datasets Used In The Experimentsmentioning

confidence: 99%

“…However, "Identifying double-meaning terms" contains articles related to specific terms, such as wordmarks and polysemy, that-like others in this class-can be incorporated into a TA system to improve its operation. Among the papers related to text categorization, [25], [33], and [24] can be applied to the feature Close notes, as shown in Table 1, to classify the incident into a category with similar instances. In many cases, the description contains the informal text.…”

Section: What Are the Most Used Machinementioning

confidence: 99%

“…Directly approaches TA -Classifying the issue [2,3,4,5,6,16,21,22,23,24,25,26,28,30,31,33,36,37,38,39,40,41,42,43] -Dealing with imbalanced data [15] Indirectly approaches TA -Analyzing historical interactions data [17,20,34] -Identifying double-meaning terms [29,32,35] -Identifying languages [18,27] -Receiving feedback…”

Section: Ta Application Papersmentioning

confidence: 99%

See 3 more Smart Citations

On the use of Machine Learning and Deep Learning for Text Similarity and Categorization and its Application to Troubleshooting Automation

Couto¹,

Tomaz²,

Godoy³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Troubleshooting is a labor-intensive task that includes repetitive solutions to similar problems. This task can be partially or fully automated using text-similarity matching to find previous solutions, lowering the workload of technicians. We develop a systematic literature review to identify the best approaches to solve the problem of troubleshooting automation and classify incidents effectively. We identify promising approaches and point in the direction of a comprehensive set of solutions that could be employed in solving the troubleshooting automation problem. 1 Available at https://archive.ics.uci.edu/ml/ datasets/reuters-21578+text+categorization+ collection. Accessed in May 2021. 2 Available at https://archive.ics.uci.edu/ml/ datasets/Twenty+Newsgroups. Accessed in May 2021.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Characteristics Of the Datasets Used In The Experimentsmentioning

confidence: 99%

Section: What Are the Most Used Machinementioning

confidence: 99%

Section: Ta Application Papersmentioning

confidence: 99%

See 2 more Smart Citations

On the use of Machine Learning and Deep Learning for Text Similarity and Categorization and its Application to Troubleshooting Automation

Couto¹,

Tomaz²,

Godoy³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Then we would like to show some more concrete applications of GNN. For instance, (Wu et al, 2021a) and (Wei et al, 2020) found the GNNs such as RecGNN could be utilized in Natural Language Processing (Wu et al, 2021b). And the GNN could also be in the field of Computer Vision.…”

Section: Applications Of Gnnmentioning

confidence: 99%

A Survey of GNN in Bioinformation Data

Zhu¹

2022

Preprint

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With the development of data science, more and more machine learning technologies have been designed to solve complicated and challenging real-world tasks containing a large volume of data. And many significant real-world datasets contain data in the form of networks or graphs. Graph Neural Networks is one of the powerful machine learning tools, which could provide a perfect solution to processing a large amount of non-Euclidean data. And because most bio information data in bioinformatics is in the non-Euclidean domain, Graph Neural Networks could then directly be applied to solve problems in bioinformatics. Much research has been done in the field of GNN, and there are also some surveys related to GNN and its applications. However, there has been little research focusing on GNN in bioinformatics, and we think in the future we could better utilize GNN in the field of biology, so we would like to write a literature review to help take a comprehensive look at GNN and their applications in the field of bioinformatics. In this paper, we would first introduce SOTA models in Graph Neural Networks, and second, introduce their applications in bio information. And then we would provide future directions of Graph Neural Networks in bioinformatics.

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A survey of aspect-based sentiment analysis classification with a focus on graph neural network methods

Zarandi,

Mirzaei

2023

Multimed Tools Appl

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Recurrent Graph Neural Networks for Text Classification

Cited by 6 publications

References 16 publications

On the use of Machine Learning and Deep Learning for Text Similarity and Categorization and its Application to Troubleshooting Automation

On the use of Machine Learning and Deep Learning for Text Similarity and Categorization and its Application to Troubleshooting Automation

A Survey of GNN in Bioinformation Data

A survey of aspect-based sentiment analysis classification with a focus on graph neural network methods

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