# Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism

arXiv: https://arxiv.org/abs/1904.08386 · HTML: https://mlsanity.com/paper/1904.08386/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Evaluating Computational Representations of Character: An Austen Character Similarity Benchmark](https://mlsanity.com/paper/2408.16131/) (2024)
- [Tecnologica cosa: Modeling Storyteller Personalities in Boccaccio's Decameron](https://mlsanity.com/paper/2109.10506/) (2021)

## Abstract (arXiv)

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.