Search Results for author:
Found 10 papers, 2 papers with code
Disentangling Document Topic and Author Gender in Multiple Languages: Lessons for Adversarial Debiasing
Text classification is a central tool in NLP.
Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification
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”).
Swimming with the Tide? Positional Claim Detection across Political Text Types
Manifestos are official documents of political parties, providing a comprehensive topical overview of the electoral programs.
Improving Neural Political Statement Classification with Class Hierarchical Information
Many tasks in text-based computational social science (CSS) involve the classification of political statements into categories based on a domain-specific codebook.
Between welcome culture and border fence. A dataset on the European refugee crisis in German newspaper reports
Besides the released resources and the case-study, our contribution is also methodological: we talk the reader through the steps from a newspaper article to a discourse network, demonstrating that there is not just one discourse network for the German migration debate, but multiple ones, depending on the topic of interest (political actors, policy fields, time spans).
Masking Actor Information Leads to Fairer Political Claims Detection
A central concern in Computational Social Sciences (CSS) is fairness: where the role of NLP is to scale up text analysis to large corpora, the quality of automatic analyses should be as independent as possible of textual properties.
DEbateNet-mig15:Tracing the 2015 Immigration Debate in Germany Over Time
DEbateNet-migr15 is a manually annotated dataset for German which covers the public debate on immigration in 2015.
Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates
Understanding the structures of political debates (which actors make what claims) is essential for understanding democratic political decision making.
Team Howard Beale at SemEval-2019 Task 4: Hyperpartisan News Detection with BERT
This paper describes our system for SemEval-2019 Task 4: Hyperpartisan News Detection (Kiesel et al., 2019).
Morphological analysis using a sequence decoder
Our Morse implementation and the TrMor2018 dataset are available online to support future research\footnote{See \url{https://github. com/ai-ku/Morse. jl} for a Morse implementation in Julia/Knet \cite{knet2016mlsys} and \url{https://github. com/ai-ku/TrMor2018} for the new Turkish dataset.
