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Argumentation Mining in User-Generated Web Discourse

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/J17-1004.pdf

Summary

Habernal and Gurevych examine whether argumentation mining can work on messy, real-world Web text instead of the tidy, single-domain sources used in earlier studies, such as essays or legal documents. They gathered user-generated content on controversial education topics across several registers: article comments, forum posts, blogs, and newswire. A first annotation pass filtered off-topic or non-argumentative texts and produced 990 labelled documents. A second study selected a model from argumentation theory and tested how reliably annotators could apply it. The model has a logos side (claim, premise, backing, rebuttal) and a pathos side (appeal to emotion). The outcome is a gold-standard corpus of 340 documents and about 90k tokens, which the authors describe as the largest in the field at publication. They then trained several machine learning systems to detect argument components. The experiments show that the task can be done but remains hard. The work matters because it connects normative argumentation theory with descriptive NLP practice and releases openly licensed data and code.

Key points

  • Corpus: 340 documents (~90k tokens) annotated with argument components, plus 990 documents labelled in a preliminary relevance study.
  • Topics: homeschooling, single-sex education, mainstreaming, and public vs. private schools.
  • Registers: comments, forum posts, blog posts, and professional news articles. Some very noisy texts had to be annotated at token level because sentence splitting failed.
  • Annotation model: a theory-based scheme covering claim, premise, backing, rebuttal, and appeal to emotion. Implicit claims are permitted.
  • Openness: corpus, source code, and annotation guidelines are released under free licenses. The paper itself is CC BY-NC-ND 4.0.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Topics: Discourse analysis

Cited by

128 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 126 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00276

@article{habernal2017argumentation,
  title = {{Argumentation Mining in User-Generated Web Discourse}},
  author = {Habernal, Ivan and Gurevych, Iryna},
  journal = {Computational Linguistics},
  year = {2017},
  doi = {10.1162/coli\_a\_00276},
  url = {https://doi.org/10.1162/coli_a_00276}
}

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