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 sub- classes. 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 perfor- mance increases for joint prediction, in particular for infrequent categories and discuss the complementarity of the two approaches.

Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification

5th Workshop on Structured Prediction for NLP

workshop paper

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Sebastian Haunss

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Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification

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