A new clustering strategy, TermCut, is presented to cluster short text snippets by finding core terms in the corpus. We model
the collection of short text snippets as a graph in which each vertex represents a piece of short text snippet and each weighted
edge between two vertices measures the relationship between the two vertices. TermCut is then applied to recursively select
a core term and bisect the graph such that the short text snippets in one part of the graph contain the term, whereas those
snippets in the other part do not. We apply the proposed method on different types of short text snippets, including questions
and search results. Experimental results show that the proposed method outperforms state-of-the-art clustering algorithms
for clustering short text snippets.