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

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

Delong Zeng,

Enze Zha,

Jiayi Kuang

et al.

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Cited by 11 publications

References 15 publications

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All is attention for multi-label text classification

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Xia

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All is attention for multi-label text classification

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Huang,

Xia

et al. 2024

Knowl Inf Syst

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

Li,

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Peng

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

Arslan,

Cruz

2024

Appl Netw Sci

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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.

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

Cited by 11 publications

References 15 publications

All is attention for multi-label text classification

All is attention 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

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