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Argument Mining: A Survey

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/J19-4006.pdf

Summary

Argument mining tries to automatically recover the reasoning structure in natural-language text: which claims are made, what reasons back them, and how pieces support or undermine one another. Opinion mining and sentiment analysis show what people think but not why. Manual argument analysis answers the "why" but is far too slow. Horn's argument maps on whether computers can think took over 7,000 hours to build.

Lawrence and Reed survey the field for a computational linguistics audience. They cover:

  • precursor areas (opinion mining, controversy detection, citation mining, argumentative zoning)
  • manual analysis and its tools
  • available annotated data
  • the mining tasks, ordered by growing argumentative complexity: finding components and their boundaries, classifying clause roles, and detecting relations up to argumentation schemes

Unlike earlier reviews, they replace the linear pipeline picture with a network of interdependent subtasks. Their coverage also extends to more recent techniques, including attention-based models, Integer Linear Programming and the transfer of learning across tasks. Their conclusion is that combining statistical and linguistic techniques gives richer analyses. Further progress, however, needs consistent data, shared theory, reproducible code and more knowledge-driven processing.

Key points

  • The survey reviews annotated argument data sources totalling over 2.2 million words.
  • The ACL argument mining workshops have run annually since 2014; the 2019 edition received a record 41 submissions.
  • In a foundational sentiment study (Pang, Lee, and Vaithyanathan 2002), SVMs with unigram and bigram features reached above 0.82 accuracy in 3-fold cross-validation.
  • Daxenberger et al. (2017) compared six widely used argument datasets and found that each conceptualizes "claim" differently. Schulz et al. (2018) showed that multi-task learning across domains helps when in-domain annotation is scarce.
  • Open challenges include domain-specific annotation schemes, too little fine-grained data, weak reproducibility (CLARIN and LAPPSGrid are named as possible remedies), and implicit content that never appears in the text itself.

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

Cited by

205 papers of our library cite it (of the 140673 whose bibliographies we read).

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00364

@article{lawrence2019argument,
  title = {{Argument Mining: A Survey}},
  author = {Lawrence, John and Reed, Chris},
  journal = {Computational Linguistics},
  year = {2019},
  doi = {10.1162/coli\_a\_00364},
  url = {https://doi.org/10.1162/coli_a_00364}
}

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