Our approach to voice identification in The Waste Land consists first of identifying the boundaries of voice spans <cite>(Brooke et al., 2012)</cite> . Given a segmentation of the text, we consider each span as a data point in a clustering problem. The elements of the vector correspond to the best feature set from the segmentation task, with the rationale that features which were useful for detecting changes in style should also be useful for identifying stylistic similarities. Our features therefore include: a collection of readability metrics (including word length), frequency of punctuation, line breaks, and various parts-ofspeech, lexical density, average frequency in a large external corpus (Brants and Franz, 2006) , lexiconbased sentiment metrics using SentiWordNet (Baccianella et al., 2010) , formality score (Brooke et al., 2010) , and, perhaps most notably, the centroid of 20-dimensional distributional vectors built using latent semantic analysis (Landauer and Dumais, 1997), reflecting the use of words in a large web corpus (Burton et al., 2009) ; in previous work (Brooke et al., 2010) , we established that such vectors contain useful stylistic information about the English lexicon (including rare words that appear only occasionally in such a corpus), and indeed LSA vectors were the single most promising feature type for segmentation.