  This work presents a hierarchical deep learning natural language parser for
fashion. Our proposal intends not only to recognize fashion-domain entities but
also to expose syntactic and morphologic insights. We leverage the usage of an
architecture of specialist models, each one for a different task (from parsing
to entity recognition). Such architecture renders a hierarchical model able to
capture the nuances of the fashion language. The natural language parser is
able to deal with textual ambiguities which are left unresolved by our
currently existing solution. Our empirical results establish a robust baseline,
which justifies the use of hierarchical architectures of deep learning models
while opening new research avenues to explore.
