TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names (2021.naacl-main)
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| Challenge: | Hierarchical multi-label text classification (HMTC) aims to assign each text document to a set of relevant classes from a taxonomy. |
| Approach: | They propose to conduct HMTC based on only class surface names as supervision signals to mimic human experts. |
| Outcome: | The proposed framework outperforms the best existing method by 25% on two challenging datasets. |
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| Challenge: | Existing methods to improve hierarchical text classification are expensive and lack high-quality labeled data. |
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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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| Challenge: | Hierarchical text classification (HTC) is a challenging subtask due to its complex taxonomic structure. |
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| Challenge: | NeuralClassifier is a toolkit for hierarchical multi-label text classification. |
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