Labelling Topics using Unsupervised Graph-based Methods

Nikolaos Aletras, Mark Stevenson

Abstract: This paper introduces an unsupervised graph-based method that selects textual labels for automatically generated topics. Our approach uses the topic keywords to query a search engine and generate a graph from the words contained in the results. PageRank is then used to weigh the words in the graph and score the candidate labels. The state-of-the-art method for this task is supervised (Lau et al., 2011). Evaluation on a standard data set shows that the performance of our approach is consistently superior to previously reported methods.