Argument Mining with Graph Representation Learning
Gechuan Zhang, Paul Nulty, David Lillis
Abstract
Argument Mining (AM) is a unique task in Natural Language Processing (NLP) that targets arguments: a meaningful logical structure in human language. Since the argument plays a significant role in the legal field, the interdisciplinary study of AM on legal texts has significant promise. For years, a pipeline architecture has been used as the standard paradigm in this area. Although this simplifies the development and management of AM systems, the connection between different parts of the pipeline causes inevitable shortcomings such as cascading error propagation.
Topics & Concepts
Computer scienceArgumentativeArtificial intelligenceClassifier (UML)Natural language processingTextual entailmentGraphMachine learningArgument (complex analysis)Natural languageNatural language understandingTheoretical computer scienceLogical consequenceLawPolitical scienceBiochemistryChemistryTopic ModelingNatural Language Processing TechniquesSoftware Engineering Research