Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification

Erenay Dayanik, Andre Blessing, Nico Blokker, Sebastian Haunss, Jonas Kuhn, Gabriella Lapesa, Sebastian Padó


Abstract
The analysis of public debates crucially requires the classification of political demands according to hierarchical claim ontologies (e.g. for immigration, a supercategory “Controlling Migration” might have subcategories “Asylum limit” or “Border installations”). A major challenge for automatic claim classification is the large number and low frequency of such subclasses. We address it by jointly predicting pairs of matching super- and subcategories. We operationalize this idea by (a) encoding soft constraints in the claim classifier and (b) imposing hard constraints via Integer Linear Programming. Our experiments with different claim classifiers on a German immigration newspaper corpus show consistent performance increases for joint prediction, in particular for infrequent categories and discuss the complementarity of the two approaches.
Anthology ID:
2021.spnlp-1.6
Volume:
Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | spnlp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
53–60
Language:
URL:
https://aclanthology.org/2021.spnlp-1.6
DOI:
10.18653/v1/2021.spnlp-1.6
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.spnlp-1.6.pdf