@InProceedings{aker-EtAl:2017:ArgumentMining,
  author    = {Aker, Ahmet  and  Sliwa, Alfred  and  Ma, Yuan  and  Lui, Ruishen  and  Borad, Niravkumar  and  Ziyaei, Seyedeh  and  Ghobadi, Mina},
  title     = {What works and what does not: Classifier and feature analysis for argument mining},
  booktitle = {Proceedings of the 4th Workshop on Argument Mining},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {91--96},
  abstract  = {This paper offers a comparative analysis of the performance of different
	supervised machine learning methods and feature sets on argument mining tasks.
	Specifically, we address the tasks of extracting argumentative segments from
	texts and predicting the structure between those segments. Eight classifiers
	and different combinations of six feature types reported in previous work are
	evaluated. The results indicate that overall best performing features are the
	structural ones. Although the performance of classifiers varies depending on
	the feature combinations and corpora used for training and testing, Random
	Forest seems to be among the best performing classifiers. These results build a
	basis for further development of argument mining techniques and can guide an
	implementation of argument mining into different applications such as argument
	based search.},
  url       = {http://www.aclweb.org/anthology/W17-5112}
}