  A challenging task for word embeddings is to capture the emergent meaning or
polarity of a combination of individual words. For example, existing approaches
in word embeddings will assign high probabilities to the words "Penguin" and
"Fly" if they frequently co-occur, but it fails to capture the fact that they
occur in an opposite sense - Penguins do not fly. We hypothesize that humans do
not associate a single polarity or sentiment to each word. The word contributes
to the overall polarity of a combination of words depending upon which other
words it is combined with. This is analogous to the behavior of microscopic
particles which exist in all possible states at the same time and interfere
with each other to give rise to new states depending upon their relative
phases. We make use of the Hilbert Space representation of such particles in
Quantum Mechanics where we subscribe a relative phase to each word, which is a
complex number, and investigate two such quantum inspired models to derive the
meaning of a combination of words. The proposed models achieve better
performances than state-of-the-art non-quantum models on the binary sentence
classification task.
