@InProceedings{Nandakumar:Salehi:Baldwin:2018:ALTA2018,
author	 = {Nandakumar, Navnita and Salehi, Bahar and Baldwin, Timothy},
title		 = {A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions},
booktitle = {Proceedings of the Australasian Language Technology Association Workshop 2018},
month	 = {December},
year		 = {2018},
address	 = {Dunedin, New Zealand},
pages	 = {71--76},
abstract  = {In this paper, we perform a comparative evaluation of off-the-shelf embedding models over the task of compositionality prediction of multiword expressions("MWEs"). Our experimental results suggest that character- and document-level models capture knowledge of MWE compositionality and are effective in modelling varying levels of compositionality, with the advantage over word-level models that they do not require token-level identification of MWEs in the training corpus.},
url			 = {http://www.aclweb.org/anthology/U18-1009}
}