Multi-label text classification based on semantic-sensitive graph convolutional network

Zeng, Delong; Zha, Enze; Kuang, Jiayi; Shen, Ying

doi:10.1016/j.knosys.2023.111303

Knowledge-Based Systems

2024

DOI: 10.1016/j.knosys.2023.111303

|View full text |Cite

Multi-label text classification based on semantic-sensitive graph convolutional network

Delong Zeng,

Enze Zha,

Jiayi Kuang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 6 publications

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Dual-view graph convolutional network for multi-label text classification

Li,

You,

Peng

et al. 2024

Appl Intell

View full text Add to dashboard Cite

Dual-view graph convolutional network for multi-label text classification

Li,

You,

Peng

et al. 2024

Appl Intell

View full text Add to dashboard Cite

Business text classification with imbalanced data and moderately large label spaces for digital transformation

Arslan,

Cruz

2024

Appl Netw Sci

View full text Add to dashboard Cite

Digital transformation refers to an organization’s use of digital technology to improve its products, services, and operations, aligning them with evolving business requirements. To demonstrate this transformative process, we present a real-life case study where a company seeks to automate the classification of their textual data rather than relying on manual methods. Transitioning to automated classification involves deploying machine learning models, which rely on pre-labeled datasets for training and making predictions on new data. However, upon receiving the dataset from the company, we faced challenges due to the imbalanced distribution of labels and moderately large label spaces. To tackle text classification with such a business dataset, we evaluated four distinct methods for multi-label text classification: fine-tuned Bidirectional Encoder Representations from Transformers (BERT), Binary Relevance, Classifier Chains, and Label Powerset. The results revealed that fine-tuned BERT significantly outperformed the other methods across key metrics like Accuracy, F1-score, Precision, and Recall. Binary Relevance also displayed competence in handling the dataset effectively, while Classifier Chains and Label Powerset exhibited comparatively less impressive performance. These findings highlight the remarkable effectiveness of fine-tuned BERT model and the Binary Relevance classifier in multi-label text classification tasks, particularly when dealing with imbalanced training datasets and moderately large label spaces. This positions them as valuable assets for businesses aiming to automate data classification in the digital transformation era.

show abstract

Adaptive label secondary reconstruction for missing multi-label learning

Qin,

Chen,

Yin

et al. 2024

Knowledge-Based Systems

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Multi-label text classification based on semantic-sensitive graph convolutional network

Cited by 6 publications

References 15 publications

Dual-view graph convolutional network for multi-label text classification

Dual-view graph convolutional network for multi-label text classification

Business text classification with imbalanced data and moderately large label spaces for digital transformation

Adaptive label secondary reconstruction for missing multi-label learning

Contact Info

Product

Resources

About