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.
The current state-of-the-art in compositionality prediction involves the use of word embeddings REF .
A Word Embedding Approach to Predicting the Compositionality of Multiword Expressions
HLT-NAACL
Computer Science