@InProceedings{liebeskind-hacohenkerner:2016:COLING,
  author    = {Liebeskind, Chaya  and  HaCohen-Kerner, Yaakov},
  title     = {Semantically Motivated Hebrew Verb-Noun Multi-Word Expressions 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     = {1242--1253},
  abstract  = {Identification of Multi-Word Expressions (MWEs) lies at the heart of many
	natural language processing applications.
	  In this research, we deal with a particular type of Hebrew MWEs, Verb-Noun
	MWEs (VN-MWEs), which combine a verb and a noun with or without other words.
	  Most prior work on MWEs classification focused on linguistic and statistical
	information. In this paper, we claim that it is essential to utilize semantic
	  information. To this end, we propose a semantically motivated indicator for
	classifying VN-MWE and define features that are related to various semantic
	spaces and combine them as features in a supervised classification framework.
	We empirically demonstrate that our semantic feature set yields better
	performance than the common linguistic and statistical feature sets and that
	combining semantic features contributes to the VN-MWEs identification task.},
  url       = {http://aclweb.org/anthology/C16-1118}
}