@misc{AfantenosPeldszusStede2018,
  author    = {Afantenos, Stergos and Peldszus, Andreas and Stede, Manfred},
  title     = {Comparing decoding mechanisms for parsing argumentative structures},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1062},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-47052},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-470527},
  pages     = {18},
  year      = {2018},
  abstract  = {Parsing of argumentative structures has become a very active line of research in recent years. Like discourse parsing or any other natural language task that requires prediction of linguistic structures, most approaches choose to learn a local model and then perform global decoding over the local probability distributions, often imposing constraints that are specific to the task at hand. Specifically for argumentation parsing, two decoding approaches have been recently proposed: Minimum Spanning Trees (MST) and Integer Linear Programming (ILP), following similar trends in discourse parsing. In contrast to discourse parsing though, where trees are not always used as underlying annotation schemes, argumentation structures so far have always been represented with trees. Using the 'argumentative microtext corpus' [in: Argumentation and Reasoned Action: Proceedings of the 1st European Conference on Argumentation, Lisbon 2015 / Vol. 2, College Publications, London, 2016, pp. 801-815] as underlying data and replicating three different decoding mechanisms, in this paper we propose a novel ILP decoder and an extension to our earlier MST work, and then thoroughly compare the approaches. The result is that our new decoder outperforms related work in important respects, and that in general, ILP and MST yield very similar performance.},
  language  = {en}
}
@article{Stede2020,
  author    = {Stede, Manfred},
  title     = {Automatic argumentation mining and the role of stance and sentiment},
  series = {Journal of argumentation in context},
  volume    = {9},
  journal   = {Journal of argumentation in context},
  number    = {1},
  publisher = {John Benjamins Publishing Co.},
  address   = {Amsterdam},
  issn      = {2211-4742},
  doi       = {10.1075/jaic.00006.ste},
  pages     = {19 -- 41},
  year      = {2020},
  abstract  = {Argumentation mining is a subfield of Computational Linguistics that aims (primarily) at automatically finding arguments and their structural components in natural language text. We provide a short introduction to this field, intended for an audience with a limited computational background. After explaining the subtasks involved in this problem of deriving the structure of arguments, we describe two other applications that are popular in computational linguistics: sentiment analysis and stance detection. From the linguistic viewpoint, they concern the semantics of evaluation in language. In the final part of the paper, we briefly examine the roles that these two tasks play in argumentation mining, both in current practice, and in possible future systems.},
  language  = {en}
}