  Literary critics often attempt to uncover meaning in a single work of
literature through careful reading and analysis. Applying natural language
processing methods to aid in such literary analyses remains a challenge in
digital humanities. While most previous work focuses on "distant reading" by
algorithmically discovering high-level patterns from large collections of
literary works, here we sharpen the focus of our methods to a single literary
theory about Italo Calvino's postmodern novel Invisible Cities, which consists
of 55 short descriptions of imaginary cities. Calvino has provided a
classification of these cities into eleven thematic groups, but literary
scholars disagree as to how trustworthy his categorization is. Due to the
unique structure of this novel, we can computationally weigh in on this debate:
we leverage pretrained contextualized representations to embed each city's
description and use unsupervised methods to cluster these embeddings.
Additionally, we compare results of our computational approach to similarity
judgments generated by human readers. Our work is a first step towards
incorporating natural language processing into literary criticism.
