Label-Specific Dual Graph Neural Network for Multi-Label Text Classification

Qianwen Ma; Chunyuan Yuan; Wei Zhou; Songlin Hu

doi:10.18653/v1/2021.acl-long.298

January 1, 2021Open Access

Label-Specific Dual Graph Neural Network for Multi-Label Text Classification

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Key Points

The study aims to improve multi-label text classification by developing a dual graph neural network framework that models both label-specific features and complex label dependencies.
Constructed a dual graph neural network architecture integrating text representations with label correlation structures.
Generated label-specific document representations to capture distinctive semantic signals associated with individual target categories.
Evaluated classification performance against standard multi-label text classification baselines across benchmark natural language processing datasets.
The dual graph neural network framework achieved superior classification accuracy over existing baseline methods across benchmark multi-label datasets.
Integrating label-specific representations with structural graph modeling significantly enhanced the model's ability to disambiguate interdependent labels.

Implication

Computational study demonstrates improved multi-label text classification using dual graph neural networks, highlighting the value of modeling label-specific textual semantics.

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Cite This Study

Ma et al. (2021) studied this question.

synapsesocial.com/papers/6a0f2c821cf410a932427282 https://doi.org/10.18653/v1/2021.acl-long.298

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Label-Specific Dual Graph Neural Network for Multi-Label Text Classification

Key Points

Implication

Abstract

Discussion

Cite This Study

Label-Specific Dual Graph Neural Network for Multi-Label Text Classification

Key Points

Implication

Abstract

Discussion

Cite This Study

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