  Functionality is of utmost importance to customers when they purchase
products. However, it is unclear to customers whether a product can really
satisfy their needs on functions. Further, missing functions may be
intentionally hidden by the manufacturers or the sellers. As a result, a
customer needs to spend a fair amount of time before purchasing or just
purchase the product on his/her own risk. In this paper, we first identify a
novel QA corpus that is dense on product functionality information
\footnote{The annotated corpus can be found at
\url{https://www.cs.uic.edu/~hxu/}.}. We then design a neural network called
Semi-supervised Attention Network (SAN) to discover product functions from
questions. This model leverages unlabeled data as contextual information to
perform semi-supervised sequence labeling. We conduct experiments to show that
the extracted function have both high coverage and accuracy, compared with a
wide spectrum of baselines.
