  We replicate recent experiments attempting to demonstrate an attractive
hypothesis about the use of the Fisher kernel framework and mixture models for
aggregating word embeddings towards document representations and the use of
these representations in document classification, clustering, and retrieval.
Specifically, the hypothesis was that the use of a mixture model of von
Mises-Fisher (VMF) distributions instead of Gaussian distributions would be
beneficial because of the focus on cosine distances of both VMF and the vector
space model traditionally used in information retrieval. Previous experiments
had validated this hypothesis. Our replication was not able to validate it,
despite a large parameter scan space.
