This paper presents the first attempt to use word embeddings to predict the compositionality of multiword expressions. We consider both single-and multi-prototype word embeddings. Experimental results show that, in combination with a back-off method based on string similarity, word embeddings outperform a method using count-based distributional similarity. Our best results are competitive with, or superior to, state-of-the-art methods over three standard compositionality datasets, which include two types of multiword expressions and two languages.
REF were the first to apply word embeddings to the task of predicting the compositionality of MWEs.
A Word Embedding Approach to Predicting the Compositionality of Multiword Expressions
HLT-NAACL
Computer Science