@InProceedings{hakimiparizi-cook:2018:W18-49,
  author    = {Hakimi Parizi, Ali  and  Cook, Paul},
  title     = {Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?},
  booktitle = {Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {185--192},
  abstract  = {In this paper, we propose the first model for multiword expression (MWE) compositionality prediction based on character-level neural network language models. Experimental results on two kinds of MWEs (noun compounds and verb-particle constructions) and two languages (English and German) suggest that character-level neural network language models capture knowledge of multiword expression compositionality, in particular for English noun compounds and the particle component of English verb-particle constructions. In contrast to many other approaches to MWE compositionality prediction, this character-level approach does not require token-level identification of MWEs in a training corpus, and can potentially predict the compositionality of out-of-vocabulary MWEs.},
  url       = {http://www.aclweb.org/anthology/W18-4920}
}