Towards Automation of Topic Taxonomy Construction

The automation of taxonomy construction has increased in popularity recently. Such an interest for the domain has been motivated by the large number of new scientific papers published each year that implies a growing difficulty in following the new topics of the different scientific domains and their importance in the topic hierarchy. In this paper, we propose a way to automatically construct topic taxonomies from millions of scientific article abstracts and ways to automatically evaluate this construction. While, to our knowledge, other approaches rely on pipelines of models and human evaluation to validate them, we chose to rely on simple models that are easier to evaluate automatically and, thus, promote the improvement of our models thanks to a large number of iterations. The contribution of this paper is threefold: 1) the proposition of a new method to construct taxonomies from a large set of scientific papers, 2) a method to precompile taxonomy information into matrices that will be quickly queried, and 3) an objective method to automatically evaluate the constructed taxonomies without requiring human evaluation.