Label Dependencies-aware Set Prediction Networks for Multi-label Text Classification
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866910366398676992 |
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| author | Xinkai, Du Quanjie, Han Yalin, Sun Chao, Lv Maosong, Sun |
| author_facet | Xinkai, Du Quanjie, Han Yalin, Sun Chao, Lv Maosong, Sun |
| contents | Multi-label text classification involves extracting all relevant labels from a sentence. Given the unordered nature of these labels, we propose approaching the problem as a set prediction task. To address the correlation between labels, we leverage Graph Convolutional Networks and construct an adjacency matrix based on the statistical relations between labels. Additionally, we enhance recall ability by applying the Bhattacharyya distance to the output distributions of the set prediction networks. We evaluate the effectiveness of our approach on two multi-label datasets and demonstrate its superiority over previous baselines through experimental results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_07022 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Label Dependencies-aware Set Prediction Networks for Multi-label Text Classification Xinkai, Du Quanjie, Han Yalin, Sun Chao, Lv Maosong, Sun Computation and Language Multi-label text classification involves extracting all relevant labels from a sentence. Given the unordered nature of these labels, we propose approaching the problem as a set prediction task. To address the correlation between labels, we leverage Graph Convolutional Networks and construct an adjacency matrix based on the statistical relations between labels. Additionally, we enhance recall ability by applying the Bhattacharyya distance to the output distributions of the set prediction networks. We evaluate the effectiveness of our approach on two multi-label datasets and demonstrate its superiority over previous baselines through experimental results. |
| title | Label Dependencies-aware Set Prediction Networks for Multi-label Text Classification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2304.07022 |