• Computer Science
• Published in EMNLP 2018
• DOI:10.18653/v1/d18-1402

Cross-topic Argument Mining from Heterogeneous Sources

@inproceedings{Stab2018CrosstopicAM,
  title={Cross-topic Argument Mining from Heterogeneous Sources},
  author={Christian Stab and Tristan Miller and Benjamin Schiller and Pranav Rai and Iryna Gurevych},
  booktitle={EMNLP},
  year={2018}
}

• Christian Stab, Tristan Miller, +2 authors 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 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. We show that… CONTINUE READING

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