References of "Haddadan, Shohreh 50029032"
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See detailYes, we can! Mining Arguments in 50 Years of US Presidential Campaign Debates
Haddadan, Shohreh UL; Villata, Serena; Cabrio, Elena

Scientific Conference (2019, July)

Political debates offer a rare opportunity for citizens to compare the candidates’ positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for ... [more ▼]

Political debates offer a rare opportunity for citizens to compare the candidates’ positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument Mining. As existing research lacks solid empirical investigation of the typology of argument components in political debates, we fill this gap by proposing an Argument Mining approach to political debates. We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims. We then propose two tasks: (1) identifying the argumentative components in such debates, and (2) classifying them as premises and claims. We show that feature-rich SVM learners and Neural Network architectures outperform standard baselines in Argument Mining over such complex data. We release the new corpus USElecDeb60To16 and the accompanying software under free licenses to the research community. [less ▲]

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See detailAnnotation of Argument Components in Political Debates Data
Haddadan, Shohreh UL; Cabrio, Elena; Villata, Serena

in Proceedings of the Workshop on Annotation in Digital Humanities (2018)

n this paper, we present the annotation guidelines we defined for annotating arguments in political debates. In our guidelines, we consider each argument as being composed of a claim and one or more ... [more ▼]

n this paper, we present the annotation guidelines we defined for annotating arguments in political debates. In our guidelines, we consider each argument as being composed of a claim and one or more premises. The annotation process has started with defining the guidelines for three annotators containing examples from the data, and continued as cyclic process of evaluation and revision on the annotation to resolve the ambiguities in the guidelines. In this paper, we briefly discuss the resulting annotated dataset and give some examples of the annotation scheme. The quality of the annotated dataset is assessed by computing inter-annotator agreement using Krippendorf’s α coefficient on a portion of the dataset. [less ▲]

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