@InProceedings{chen-palmer:2017:EACLlong,
  author    = {Chen, Wei-Te  and  Palmer, Martha},
  title     = {Unsupervised AMR-Dependency Parse Alignment},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {558--567},
  abstract  = {In this paper, we introduce an Abstract Meaning Representation (AMR) to
	Dependency Parse aligner. Alignment is a preliminary step for AMR parsing, and
	our aligner improves current AMR parser performance. Our aligner involves
	several different features, including named entity tags and semantic role
	labels, and uses Expectation-Maximization training. Results show that our
	aligner reaches an 87.1% F-Score score with the experimental data, and enhances
	AMR parsing.},
  url       = {http://www.aclweb.org/anthology/E17-1053}
}