A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions

Navnita Nandakumar, Bahar Salehi, Timothy Baldwin

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.