| dc.description.abstract | Automatic fact-checking relies on claim detection systems to find claims and estimate
their check-worthiness. To improve current claim detection systems, we need high-quality
labeled data sets. More specifically, a data set based on claims from general news articles.
To our knowledge, no such dataset exists currently. We explore an approach for collecting
data for such a set by creating an annotation tool and distributing the work using
crowdsourcing platforms. We show that such platforms can be viable, even with complex
annotation tasks. We can train participants and test the submitted data quality by
developing the right tools and systems. We show that a structured approach to claim
definitions using a claim taxonomy can be beneficial when creating a labeling schema.
Furthermore, we implement and test a rules-based claim detection system using natural
language processing libraries, intending to integrate it into the data collection process. | |