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Improving Neural Political Statement Classification with Class Hierarchical Information.

, , , , , , and . ACL (Findings), page 2367-2382. Association for Computational Linguistics, (2022)

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Optimizing text representations to capture (dis)similarity between political parties., , and . CoNLL, page 325-338. Association for Computational Linguistics, (2022)Between welcome culture and border fence., , , , , and . Lang. Resour. Evaluation, 57 (1): 121-153 (March 2023)An Environment for Relational Annotation of Political Debates., , , , , and . ACL (3), page 105-110. Association for Computational Linguistics, (2019)Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates., , , , , and . ACL (1), page 2841-2847. Association for Computational Linguistics, (2019)Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification., , , , , , and . SPNLP@ACL-IJCNLP, page 53-60. Association for Computational Linguistics, (2021)Improving Neural Political Statement Classification with Class Hierarchical Information., , , , , , and . ACL (Findings), page 2367-2382. Association for Computational Linguistics, (2022)DEbateNet-mig15: Tracing the 2015 Immigration Debate in Germany Over Time., , , , , , and . LREC, page 919-927. European Language Resources Association, (2020)