Argumentation Mining Task

An Argumentation Mining Task is a IE from text task that of argument element mentions (e.g., premises and conclusion; data, claim and warrant), argumentation schemes, relationships between arguments in and between documents, and relationships to discourse goals (e.g. stages of a “critical discussion”) and/or rhetorical strategies;

• See: Semantic Role Labeling.

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References

2016

• http://argmining2016.arg.tech/
  • QUOTE: Argument mining (also, `argumentation mining’) is a relatively new challenge in corpus-based discourse analysis that involves automatically identifying argumentative structures within discourse, e.g., the premises, conclusion, and argumentation scheme of each argument, as well as argument-subargument and argument-counterargument relationships between pairs of arguments in the document. To date, researchers have investigated methods for argument mining in areas such as legal documents , on-line debates, product reviews, academic literature, user comments on proposed regulations, newspaper articles and court cases, as well as in dialogical domains.
• http://argmining2016.arg.tech/index.php/home/call-for-papers/
  • QUOTE: Suggested topics for the workshop include but are not limited to:
    • Automatic identification of argument elements (e.g., premises and conclusion; data, claim and warrant), argumentation schemes, relationships between arguments in and between documents, and relationships to discourse goals (e.g. stages of a “critical discussion”) and/or rhetorical strategies;
    • Creation and evaluation of argument annotation schemes, relationship of argument annotation to linguistic and discourse structure annotation schemes, (semi-)automatic argument annotation methods and tools, and creation and annotation of high-quality shared argumentation corpora;
    • Management of dialogue (spoken and transcribed) and the extraction of argument structures from such data, including additional challenges posed by real-time processing; argument mining in dialogue for mixed initiative argumentation;
    • Processing strategies integrating NLP methods and AI models developed for argumentation such as abstract and structured argumentation frameworks;
    • Applications of argument mining to, e.g., acquiring requirements for technical documents, analysis of arguments in meetings, opinion analysis and mining consumer reviews, evaluation of students’ written arguments and argument diagrams, and information access (retrieval, extraction, summarization, and visualization) in domains such as scientific and legal discourse.
    • Finally, to highlight the importance of, and close connection with, the emerging area of Debating Technologies, the workshop will introduce a Special Track on Debating Technologies. Specifically, Debating Technologies are defined as computational technologies developed directly to enhance, support, and engage with human debating. The proposed special track will be managed in particular by Noam Slonim of IBM Research and Iryna Gurevych from the Technische Universitat Darmstadt.

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