  This paper proposes a simple test for compositionality (i.e., literal usage)
of a word or phrase in a context-specific way. The test is computationally
simple, relying on no external resources and only uses a set of trained word
vectors. Experiments show that the proposed method is competitive with state of
the art and displays high accuracy in context-specific compositionality
detection of a variety of natural language phenomena (idiomaticity, sarcasm,
metaphor) for different datasets in multiple languages. The key insight is to
connect compositionality to a curious geometric property of word embeddings,
which is of independent interest.
