Topic Ontologies for Arguments
This work addresses the need for better topic diversity in argumentation corpora for researchers in computational linguistics, though it is incremental as it primarily surveys existing data.
The paper tackled the problem of topic dependence in computational argumentation tasks by conducting the first comprehensive survey of topic coverage across 45 argument corpora, revealing that while corpora topics are well-covered by authoritative sources, other topics from these sources are less extensively covered.
Many computational argumentation tasks, like stance classification, are topic-dependent: the effectiveness of approaches to these tasks significantly depends on whether the approaches were trained on arguments from the same topics as those they are tested on. So, which are these topics that researchers train approaches on? This paper contributes the first comprehensive survey of topic coverage, assessing 45 argument corpora. For the assessment, we take the first step towards building an argument topic ontology, consulting three diverse authoritative sources: the World Economic Forum, the Wikipedia list of controversial topics, and Debatepedia. Comparing the topic sets between the authoritative sources and corpora, our analysis shows that the corpora topics-which are mostly those frequently discussed in public online fora - are covered well by the sources. However, other topics from the sources are less extensively covered by the corpora of today, revealing interesting future directions for corpus construction.