  In this paper, we present a novel algorithm that combines multi-context term
embeddings using a neural classifier and we test this approach on the use case
of corpus-based term set expansion. In addition, we present a novel and unique
dataset for intrinsic evaluation of corpus-based term set expansion algorithms.
We show that, over this dataset, our algorithm provides up to 5 mean average
precision points over the best baseline.
