  The top word list, i.e., the top-M words with highest marginal probability in
a given topic, is the standard topic representation in topic models. Most of
recent automatical topic labeling algorithms and popular topic quality metrics
are based on it. However, we find, empirically, words in this type of top word
list are not always representative. The objective of this paper is to find more
representative top word lists for topics. To achieve this, we rerank the words
in a given topic by further considering marginal probability on words over
every other topic. The reranking list of top-M words is used to be a novel
topic representation for topic models. We investigate three reranking
methodologies, using (1) standard deviation weight, (2) standard deviation
weight with topic size and (3) Chi Square \c{hi}2statistic selection.
Experimental results on real world collections indicate that our
representations can extract more representative words for topics, agreeing with
human judgements.
