Unsupervised Stylistic Segmentation of Poetry with Change Curves and Extrinsic Features

The identification of stylistic inconsistency is a challenging task relevant to a number of genres, including literature. In this work, we carry out stylistic segmentation of a well-known poem, The Waste Land by T.S. Eliot, which is traditionally analyzed in terms of numerous voices which appear throughout the text. Our method, adapted from work in topic segmentation and plagiarism detection, predicts breaks based on a curve of stylistic change which combines information from a diverse set of features, most notably co-occurrence in larger corpora via reduced-dimensionality vectors. We show that this extrinsic information is more useful than (within-text) distributional features. We achieve well above baseline performance on both artificial mixed-style texts and The Waste Land itself.