Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism
This work addresses a specific problem for literary scholars and digital humanities researchers by providing a computational method to engage with single-work literary theories, though it is incremental as it builds on existing NLP techniques.
The authors tackled the challenge of applying natural language processing to literary criticism by analyzing Italo Calvino's novel 'Invisible Cities', using pretrained embeddings and unsupervised clustering to evaluate the trustworthiness of the author's thematic categorization of 55 city descriptions, and found that computational results aligned with human similarity judgments.
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.