• Computer Science
• Published in ArXiv 2018

Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks

@article{Stab2018CrosstopicAM,
  title={Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks},
  author={Christian Stab and Tristan Miller and Iryna Gurevych},
  journal={ArXiv},
  year={2018},
  volume={abs/1802.05758}
}

• Christian Stab, Tristan Miller, Iryna Gurevych

Argument mining is a core technology for automating argument search in large document collections. Despite its usefulness for this task, most current approaches to argument mining are designed for use only with specific text types and fall short when applied to heterogeneous texts. In this paper, we propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. We source annotations for over 25,000 instances covering eight controversial topics… CONTINUE READING

View PDF on ArXiv

Save to Library

Create Alert

Cite