  This paper describes a hypernym discovery system for our participation in the
SemEval-2018 Task 9, which aims to discover the best (set of) candidate
hypernyms for input concepts or entities, given the search space of a
pre-defined vocabulary. We introduce a neural network architecture for the
concerned task and empirically study various neural network models to build the
representations in latent space for words and phrases. The evaluated models
include convolutional neural network, long-short term memory network, gated
recurrent unit and recurrent convolutional neural network. We also explore
different embedding methods, including word embedding and sense embedding for
better performance.
