Improving Neural Political Statement Classification with Class Hierarchical Information (2022.findings-acl)
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Erenay Dayanik, Andre Blessing, Nico Blokker, Sebastian Haunss, Jonas Kuhn, Gabriella Lapesa, Sebastian Pado
| Challenge: | skewed classification of fine-grained categories in text-based computational social science is challenging on the NLP side. |
| Approach: | They propose to use hierarchical relations among categories in codebooks to create constraints on the learned model. |
| Outcome: | The proposed model improves on two datasets and multiple languages. |
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