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.

本文旨在为主题模型找到更加代表性的关键词列表，通过重新排名给定主题中的单词并进一步考虑每个主题上的边际概率，将重排列的关键词列表用作主题模型的新主题呈现形式之一，实验结果表明，我们的表示可以提取更具代表性的主题词，与人类判断一致。

主题表示：在主题模型中找到更具代表性的词