@InProceedings{shnarch-EtAl:2017:EMNLP2017,
  author    = {Shnarch, Eyal  and  Levy, Ran  and  Raykar, Vikas  and  Slonim, Noam},
  title     = {GRASP: Rich Patterns for Argumentation Mining},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1345--1350},
  abstract  = {GRASP (GReedy Augmented Sequential Patterns) is an algorithm for automatically
	extracting patterns that characterize subtle linguistic phenomena. To that end,
	GRASP augments each term of input text with multiple layers of linguistic
	information. These different facets of the text terms are systematically
	combined to reveal rich patterns. We report highly promising experimental
	results in several challenging text analysis tasks within the field of
	Argumentation Mining. We believe that GRASP is general enough to be useful for
	other domains too.
	For example, each of the following sentences includes a claim for a [topic]:
	1. Opponents often argue that the open primary is unconstitutional. [Open
	Primaries]
	2. Prof. Smith suggested that affirmative action devalues the accomplishments
	of the chosen. [Affirmative Action]
	3. The majority stated that the First Amendment does not guarantee the right to
	offend others. [Freedom of Speech]
	These sentences share almost no words in common, however, they are similar at a
	more abstract level. A human observer may notice the following underlying
	common structure, or pattern: [someone][argue/suggest/state][that][topic
	term][sentiment term].
	GRASP aims to automatically capture such underlying structures of the given
	data. For the above examples it finds the pattern
	[noun][express][that][noun,topic][sentiment], where [express] stands for all
	its (in)direct hyponyms, and [noun,topic] means a noun which is also related to
	the topic.},
  url       = {https://www.aclweb.org/anthology/D17-1140}
}