Linguistically Motivated Features for Enhanced Back-of-the-Book Indexing

In this paper we present a supervised method for back-of-the-book index construction. We introduce a novel set of features that goes be- yond the typical frequency-based analysis, in- cluding features based on discourse compre- hension, syntactic patterns, and information drawn from an online encyclopedia. In exper- iments carried out on a book collection, the method was found to lead to an improvement of roughly 140% as compared to an existing state-of-the-art supervised method.