  We provide a comparative study between neural word representations and
traditional vector spaces based on co-occurrence counts, in a number of
compositional tasks. We use three different semantic spaces and implement seven
tensor-based compositional models, which we then test (together with simpler
additive and multiplicative approaches) in tasks involving verb disambiguation
and sentence similarity. To check their scalability, we additionally evaluate
the spaces using simple compositional methods on larger-scale tasks with less
constrained language: paraphrase detection and dialogue act tagging. In the
more constrained tasks, co-occurrence vectors are competitive, although choice
of compositional method is important; on the larger-scale tasks, they are
outperformed by neural word embeddings, which show robust, stable performance
across the tasks.
