  Short text classi cation is a method for classifying short sentence with
prede ned labels. However, short text is limited in shortness in text length
that leads to a challenging problem of sparse features. Most of existing
methods treat each short sentences as independently and identically distributed
(IID), local context only in the sentence itself is focused and the relational
information between sentences are lost. To overcome these limitations, we
propose a PathWalk model that combine the strength of graph networks and short
sentences to solve the sparseness of short text. Experimental results on four
different available datasets show that our PathWalk method achieves the
state-of-the-art results, demonstrating the efficiency and robustness of graph
networks for short text classification.
