@InProceedings{eckhoff-berdicevskis:2016:LT4DH,
  author    = {Eckhoff, Hanne Martine  and  Berdicevskis, Aleksandrs},
  title     = {Automatic parsing as an efficient pre-annotation tool for historical texts},
  booktitle = {Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {62--70},
  abstract  = {Historical treebanks tend to be manually annotated, which is not surprising,
	since state-of-the-art parsers are not accurate enough to ensure high-quality
	annotation for historical texts. We test whether automatic parsing can be an
	efficient pre-annotation tool for Old East Slavic texts. We use the TOROT
	treebank from the PROIEL treebank family. We convert the PROIEL format to the
	CONLL format and use MaltParser to create syntactic pre-annotation. Using the
	most conservative evaluation method, which takes into account PROIEL-specific
	features, MaltParser by itself yields 0.845 unlabelled attachment score, 0.779
	labelled attachment score and 0.741 secondary dependency accuracy (note,
	though, that the test set comes from a relatively simple genre and contains
	rather short sentences). Experiments with human annotators show that
	preparsing, if limited to sentences where no changes to word or sentence
	boundaries are required, increases their annotation rate. For experienced
	annotators, the speed gain varies from 5.80% to 16.57%, for inexperienced
	annotators from 14.61% to 32.17% (using conservative estimates). There are no
	strong reliable differences in the annotation accuracy, which means that there
	is no reason to suspect that using preparsing might lower the final annotation
	quality.},
  url       = {http://aclweb.org/anthology/W16-4009}
}