Representing topics labels for exploring digital libraries

Abstract

Topic models have been shown to be a useful way of representing the content of large document collections, for example via visualisation interfaces (topic browsers). These systems enable users to explore collections by way of latent topics. A standard way to represent a topic is using a set of keywords, i.e. the top-n words with highest marginal probability within the topic. However, alternative topic representations have been proposed, including textual and image labels. In this paper, we compare different topic representations, i.e. sets of topic words, textual phrases and images, in a document retrieval task. We asked participants to retrieve relevant documents based on pre-defined querie..