@InProceedings{wang-li-wang:2017:Short,
  author    = {Wang, Yizhong  and  Li, Sujian  and  Wang, Houfeng},
  title     = {A Two-Stage Parsing Method for Text-Level Discourse Analysis},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {184--188},
  abstract  = {Previous work introduced transition-based algorithms to form a unified
	architecture
	of parsing rhetorical structures (including span, nuclearity and relation), but
	did not achieve satisfactory performance. In this paper, we propose that
	transition-based model is more appropriate for parsing the naked discourse tree
	(i.e., identifying span and nuclearity) due to data sparsity. At the same time,
	we argue that relation labeling can benefit from naked tree structure and
	should be treated elaborately with consideration of three kinds of relations
	including within-sentence, across-sentence and across-paragraph relations.
	Thus, we design a pipelined two-stage parsing method for generating an RST tree
	from text. Experimental results show that our method achieves state-of-the-art
	performance, especially on span and nuclearity identification.},
  url       = {http://aclweb.org/anthology/P17-2029}
}