@InProceedings{vaidya-agarwal-palmer:2016:COLING,
  author    = {Vaidya, Ashwini  and  Agarwal, Sumeet  and  Palmer, Martha},
  title     = {Linguistic features for Hindi light verb construction identification},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1320--1329},
  abstract  = {Light verb constructions (LVC) in Hindi are highly productive. If we can
	distinguish a case such as nirnay lenaa ‘decision take; decide’ from an
	ordinary verb-argument combination kaagaz lenaa ‘paper take; take (a)
	paper’,it has been shown to aid NLP applications such as parsing (Begum et
	al., 2011) and machine translation (Pal et al., 2011). In this paper, we
	propose an LVC identification system using language specific features for Hindi
	which shows an improvement over previous work(Begum et al., 2011). To build our
	system, we carry out a linguistic analysis of Hindi LVCs using Hindi Treebank
	annotations and propose two new features that are aimed at capturing the
	diversity of Hindi LVCs in the corpus. We find that our model performs
	robustly across a diverse range of LVCs and our results underscore the
	importance of semantic features, which is in keeping with the findings for
	English. Our error analysis also demonstrates that our classifier can be used
	to further refine LVC annotations in the Hindi Treebank and make them more
	consistent across the board.},
  url       = {http://aclweb.org/anthology/C16-1125}
}