@inproceedings{dayanik-etal-2021-using,
title = "Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification",
author = "Dayanik, Erenay and
Blessing, Andre and
Blokker, Nico and
Haunss, Sebastian and
Kuhn, Jonas and
Lapesa, Gabriella and
Pad{\'o}, Sebastian",
editor = "Kozareva, Zornitsa and
Ravi, Sujith and
Vlachos, Andreas and
Agrawal, Priyanka and
Martins, Andr{\'e}",
booktitle = "Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.spnlp-1.6/",
doi = "10.18653/v1/2021.spnlp-1.6",
pages = "53--60",
abstract = "The analysis of public debates crucially requires the classification of political demands according to hierarchical \textit{claim ontologies} (e.g. for immigration, a supercategory {\textquotedblleft}Controlling Migration{\textquotedblright} might have subcategories {\textquotedblleft}Asylum limit{\textquotedblright} or {\textquotedblleft}Border installations{\textquotedblright}). 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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification
%A Dayanik, Erenay
%A Blessing, Andre
%A Blokker, Nico
%A Haunss, Sebastian
%A Kuhn, Jonas
%A Lapesa, Gabriella
%A Padó, Sebastian
%Y Kozareva, Zornitsa
%Y Ravi, Sujith
%Y Vlachos, Andreas
%Y Agrawal, Priyanka
%Y Martins, André
%S Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F dayanik-etal-2021-using
%X 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.
%R 10.18653/v1/2021.spnlp-1.6
%U https://aclanthology.org/2021.spnlp-1.6/
%U https://doi.org/10.18653/v1/2021.spnlp-1.6
%P 53-60
Markdown (Informal)
[Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification](https://aclanthology.org/2021.spnlp-1.6/) (Dayanik et al., spnlp 2021)
ACL