  Separable Non-negative Matrix Factorization (SNMF) is an important method for
topic modeling, where "separable" assumes every topic contains at least one
anchor word, defined as a word that has non-zero probability only on that
topic. SNMF focuses on the word co-occurrence patterns to reveal topics by two
steps: anchor word selection and topic recovery. The quality of the anchor
words strongly influences the quality of the extracted topics. Existing anchor
word selection algorithm is to greedily find an approximate convex hull in a
high-dimensional word co-occurrence space. In this work, we propose a new
method for the anchor word selection by associating the word co-occurrence
probability with the words similarity and assuming that the most different
words on semantic are potential candidates for the anchor words. Therefore, if
the similarity of a word-pair is very low, then the two words are very likely
to be the anchor words. According to the statistical information of text
corpora, we can get the similarity of all word-pairs. We build the word
similarity graph where the nodes correspond to words and weights on edges stand
for the word-pair similarity. Following this way, we design a greedy method to
find a minimum edge-weight anchor clique of a given size in the graph for the
anchor word selection. Extensive experiments on real-world corpus demonstrate
the effectiveness of the proposed anchor word selection method that outperforms
the common convex hull-based methods on the revealed topic quality. Meanwhile,
our method is much faster than typical SNMF based method.
