HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization (D18-1)
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| Challenge: | Existing methods for categorization of short texts use non-hierarchical flat model, but they are limited by domain-independent knowledge distribution. |
| Approach: | They propose a method which leverages hierarchical relationships between pre-defined categories to tackle the data sparsity problem. |
| Outcome: | The proposed method is competitive with the state-of-the-art methods on a multi-label categorization task for short texts using two benchmark datasets. |
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Hierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations (2021.emnlp-main)
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| Challenge: | Existing hierarchical classification models are unable to handle large corpora and the number of categories increases with increasing corpus. |
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| Challenge: | Existing methods to improve hierarchical text classification are expensive and lack high-quality labeled data. |
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Hierarchical Label Generation for Text Classification (2023.findings-eacl)
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| Challenge: | None Hierarchical text classification (HTC) aims to assign the most relevant labels with their structure for a given document. |
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A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification (2020.findings-emnlp)
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| Challenge: | NeuralClassifier is a toolkit for hierarchical multi-label text classification. |
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