  Topics models, such as LDA, are widely used in Natural Language Processing.
Making their output interpretable is an important area of research with
applications to areas such as the enhancement of exploratory search interfaces
and the development of interpretable machine learning models. Conventionally,
topics are represented by their n most probable words, however, these
representations are often difficult for humans to interpret. This paper
explores the re-ranking of topic words to generate more interpretable topic
representations. A range of approaches are compared and evaluated in two
experiments. The first uses crowdworkers to associate topics represented by
different word rankings with related documents. The second experiment is an
automatic approach based on a document retrieval task applied on multiple
domains. Results in both experiments demonstrate that re-ranking words improves
topic interpretability and that the most effective re-ranking schemes were
those which combine information about the importance of words both within
topics and their relative frequency in the entire corpus. In addition, close
correlation between the results of the two evaluation approaches suggests that
the automatic method proposed here could be used to evaluate re-ranking methods
without the need for human judgements.
