  Semantically non-compositional phrases constitute an intriguing research
topic in Natural Language Processing. Semantic non-compositionality --the
situation when the meaning of a phrase cannot be derived from the meaning of
its components, is the main characteristic of such phrases, however, they bear
other characteristics such as high statistical association and
non-substitutability. In this work, we present a model for identifying
non-compositional phrases that takes into account all of these characteristics.
We show that the presented model remarkably outperforms the existing models of
identifying non-compositional phrases that mostly focus only on one of these
characteristics.
