@InProceedings{yordanova:2017:RANLP2,
  author    = {Yordanova, Kristina},
  title     = {A Simple Model for Improving the Performance of the Stanford Parser for Action Detection in Textual Instructions},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {831--838},
  abstract  = {Different approaches for behaviour understanding rely on textual instructions
	to generate models of human behaviour. 
	These approaches usually use state of the art parsers to obtain the part of
	speech (POS) meaning and dependencies of the words in the instructions.
	For them it is essential that the parser is able to correctly annotate the
	instructions and especially the verbs as they describe the actions of the
	person.
	State of the art parsers usually make errors when annotating textual
	instructions, as they have short sentence structure often in imperative form.
	 The inability of the parser to identify the verbs results in the inability of
	behaviour understanding systems to identify the relevant actions.
	To address this problem, we propose a simple rule-based model that attempts to
	correct any incorrectly annotated verbs.
	We argue that the model is able to significantly improve the parser's
	performance without the need of additional training data. 
	We evaluate our approach by extracting the actions from 61 textual instructions
	annotated only with the Stanford parser and once again after applying our
	model. 
	The results show a significant improvement in the recognition rate when
	applying the rules (75% accuracy compared to 68% without the rules, p-value <
	0.001).},
  url       = {https://doi.org/10.26615/978-954-452-049-6_106}
}