@InProceedings{lawrence-reed:2017:ArgumentMining1,
  author    = {Lawrence, John  and  Reed, Chris},
  title     = {Mining Argumentative Structure from Natural Language text using Automatically Generated Premise-Conclusion Topic Models},
  booktitle = {Proceedings of the 4th Workshop on Argument Mining},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {39--48},
  abstract  = {This paper presents a method of extracting argumentative structure from natural
	language text. The approach presented is based on the way in which we
	understand an argument being made, not just from the words said, but from
	existing contextual knowledge and understanding of the broader issues. We
	leverage high-precision, low-recall techniques in order to automatically build
	a large corpus of inferential statements related to the text's topic. These
	statements are then used to produce a matrix representing the inferential
	relationship between different aspects of the topic. From this matrix, we are
	able to determine connectedness and directionality of inference between
	statements in the original text. By following this approach, we obtain results
	that compare favourably to those of other similar techniques to classify
	premise-conclusion pairs (with results 22 points above baseline), but without
	the requirement of large volumes of annotated, domain specific data.},
  url       = {http://www.aclweb.org/anthology/W17-5105}
}