@InProceedings{gopalan-lalithadevi:2016:BioTxtM2016,
  author    = {Gopalan, Sindhuja  and  Lalitha Devi, Sobha},
  title     = {BioDCA Identifier: A System for Automatic Identification of Discourse Connective and Arguments from Biomedical Text},
  booktitle = {Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {89--98},
  abstract  = {This paper describes a Natural language processing system developed for
	automatic identification of explicit connectives, its sense and arguments.
	Prior work has shown that the difference in usage of connectives across corpora
	affects the cross domain connective identification task negatively. Hence the
	development of domain specific discourse parser has become indispensable. Here,
	we present a corpus annotated with discourse relations on Medline abstracts.
	Kappa score is calculated to check the annotation quality of our corpus. The
	previous works on discourse analysis in bio-medical data have concentrated only
	on the                          identification of connectives and hence we have
	developed
	an
	end-end
	parser for connective and argument identification using Conditional Random
	Fields algorithm. The type and sub-type of the connective sense is also
	identified. The results obtained are encouraging.},
  url       = {http://aclweb.org/anthology/W16-5110}
}