@article{4015,
  author = {Jonathan Kobbe, Ioana Hulpus, Heiner Stuckenschmidt},
  title = {Arguments Taxonomy System using Linguistic and Knowledge-based Features},
  journal = {Journal of E - Technology},
  year = {2024},
  volume = {15},
  number = {2},
  doi = {https://doi.org/10.6025/jet/2024/15/2/51-63},
  url = {https://www.dline.info/jet/fulltext/v15n2/jetv15n2_1.pdf},
  abstract = {Classifying Arguments Argument relations classification is a way of classifying the type of relationship between two argument units. Current models mainly rely on surface-level language features such as discourse markers, modal, or adverbial to classify the relationship. However, a model that primarily relies on language features to classify an argument can be easily misled by the style rather than the content of the argument, particularly when a weak argument is masked by strong language. This paper examines the challenges and potential advantages of knowledge-based argument analysis in advancing the current state of argument analysis towards a deeper, knowledge-driven comprehension and representation of arguments. We propose an Arguments Classification System that uses linguistic and knowledge-based features to classify Arguments. We start with a Neural Baseline Model for classifying a Pair of Arguments based on the Siamese Network and expand it with a set of Features derived from two additional background knowledge sources: ConceptNet and DBpedia.},
}