Introduction
Literary critics form interpretations of meaning in works of literature. Building computational models that can help form and test these interpretations is a fundamental goal of digital humanities research BIBREF0 . Within natural language processing, most previous work that engages with literature relies on “distant reading” BIBREF1 , which involves discovering high-level patterns from large collections of stories BIBREF2 , BIBREF3 . We depart from this trend by showing that computational techniques can also engage with literary criticism at a closer distance: concretely, we use recent advances in text representation learning to test a single literary theory about the novel Invisible Cities by Italo Calvino.
Framed as a dialogue between the traveler Marco Polo and the emperor Kublai Khan, Invisible Cities consists of 55 prose poems, each of which describes an imaginary city. Calvino categorizes these cities into eleven thematic groups that deal with human emotions (e.g., desires, memories), general objects (eyes, sky, signs), and unusual properties (continuous, hidden, thin). Many critics argue that Calvino's labels are not meaningful, while others believe that there is a distinct thematic separation between the groups, including the author himself BIBREF4 . The unique structure of this novel — each city's description is short and self-contained (Figure FIGREF1 ) — allows us to computationally examine this debate.
As the book is too small to train any models, we leverage recent advances in large-scale language model-based representations BIBREF5 , BIBREF6 to compute a representation of each city. We feed these representations into a clustering algorithm that produces exactly eleven clusters of five cities each and evaluate them against both Calvino's original labels and crowdsourced human judgments. While the overall correlation with Calvino's labels is low, both computers and humans can reliably identify some thematic groups associated with concrete objects.
While prior work has computationally analyzed a single book BIBREF7 , our work goes beyond simple word frequency or n-gram counts by leveraging the power of pretrained language models to engage with literary criticism. Admittedly, our approach and evaluations are specific to Invisible Cities, but we believe that similar analyses of more conventionally-structured novels could become possible as text representation methods improve. We also highlight two challenges of applying computational methods to literary criticisms: (1) text representation methods are imperfect, especially when given writing as complex as Calvino's; and (2) evaluation is difficult because there is no consensus among literary critics on a single “correct” interpretation.
Literary analyses of Invisible Cities
Before describing our method and results, we first review critical opinions on both sides of whether Calvino's thematic groups meaningfully characterize his city descriptions.
A Computational Analysis
We focus on measuring to what extent computers can recover Calvino's thematic groupings when given just raw text of the city descriptions. At a high level, our approach (Figure FIGREF4 ) involves (1) computing a vector representation for every city and (2) performing unsupervised clustering of these representations. The rest of this section describes both of these steps in more detail.
Embedding city descriptions
While each of the city descriptions is relatively short, Calvino's writing is filled with rare words, complex syntactic structures, and figurative language. Capturing the essential components of each city in a single vector is thus not as simple as it is with more standard forms of text. Nevertheless, we hope that representations from language models trained over billions of words of text can extract some meaningful semantics from these descriptions. We experiment with three different pretrained representations: ELMo BIBREF5 , BERT BIBREF6 , and GloVe BIBREF18 . To produce a single city embedding, we compute the TF-IDF weighted element-wise mean of the token-level representations. For all pretrained methods, we additionally reduce the dimensionality of the city embeddings to 40 using PCA for increased compatibility with our clustering algorithm.
Clustering city representations
Given 55 city representations, how do we group them into eleven clusters of five cities each? Initially, we experimented with a graph-based community detection algorithm that maximizes cluster modularity BIBREF20 , but we found no simple way to constrain this method to produce a specific number of equally-sized clusters. The brute force approach of enumerating all possible cluster assignments is intractable given the large search space ( INLINEFORM0 possible assignments). We devise a simple clustering algorithm to approximate this process. First, we initialize with random cluster assignments and define “cluster strength” to be the relative difference between “intra-group” Euclidean distance and “inter-group” Euclidean distance. Then, we iteratively propose random exchanges of memberships, only accepting these proposals when the cluster strength increases, until convergence. To evaluate the quality of the computationally-derived clusters against those of Calvino, we measure cluster purity BIBREF21 : given a set of predicted clusters INLINEFORM1 and ground-truth clusters INLINEFORM2 that both partition a set of INLINEFORM3 data points, INLINEFORM4
Evaluating clustering assignments
While the results from the above section allow us to compare our three computational methods against each other, we additionally collect human judgments to further ground our results. In this section, we first describe our human experiment before quantitatively analyzing our results.
Quantitative comparison
We compare clusters computed on different representations using community purity; additionally, we compare these computational methods to humans by their accuracy on the odd-one-out task.
City representations computed using language model-based representation (ELMo and BERT) achieve significantly higher purity than a clustering induced from random representations, indicating that there is at least some meaningful coherence to Calvino's thematic groups (first row of Table TABREF11 ). ELMo representations yield the highest purity among the three methods, which is surprising as BERT is a bigger model trained on data from books (among other domains). Both ELMo and BERT outperform GloVe, which intuitively makes sense because the latter do not model the order or structure of the words in each description.
While the purity of our methods is higher than that of a random clustering, it is still far below 1. To provide additional context to these results, we now switch to our “odd-one-out” task and compare directly to human performance. For each triplet of cities, we identify the intruder as the city with the maximum Euclidean distance from the other two. Interestingly, crowd workers achieve only slightly higher accuracy than ELMo city representations; their interannotator agreement is also low, which indicates that close reading to analyze literary coherence between multiple texts is a difficult task, even for human annotators. Overall, results from both computational and human approaches suggests that the author-assigned labels are not entirely arbitrary, as we can reliably recover some of the thematic groups.
Examining the learned clusters
Our quantitative results suggest that while vector-based city representations capture some thematic similarities, there is much room for improvement. In this section, we first investigate whether the learned clusters provide evidence for any arguments put forth by literary critics on the novel. Then, we explore possible reasons that the learned clusters deviate from Calvino's.
Related work
Most previous work within the NLP community applies distant reading BIBREF1 to large collections of books, focusing on modeling different aspects of narratives such as plots and event sequences BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , characters BIBREF2 , BIBREF26 , BIBREF27 , BIBREF28 , and narrative similarity BIBREF3 . In the same vein, researchers in computational literary analysis have combined statistical techniques and linguistics theories to perform quantitative analysis on large narrative texts BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , but these attempts largely rely on techniques such as word counting, topic modeling, and naive Bayes classifiers and are therefore not able to capture the meaning of sentences or paragraphs BIBREF34 . While these works discover general patterns from multiple literary works, we are the first to use cutting-edge NLP techniques to engage with specific literary criticism about a single narrative.
There has been other computational work that focuses on just a single book or a small number of books, much of it focused on network analysis: BIBREF35 extract character social networks from Alice in Wonderland, while BIBREF36 recover social networks from 19th century British novels. BIBREF37 disentangles multiple narrative threads within the novel Infinite Jest, while BIBREF7 provides several automated statistical methods for close reading and test them on the award-winning novel Cloud Atlas (2004). Compared to this work, we push further on modeling the content of the narrative by leveraging pretrained language models.
Conclusion
Our work takes a first step towards computationally engaging with literary criticism on a single book using state-of-the-art text representation methods. While we demonstrate that NLP techniques can be used to support literary analyses and obtain new insights, they also have clear limitations (e.g., in understanding abstract themes). As text representation methods become more powerful, we hope that (1) computational tools will become useful for analyzing novels with more conventional structures, and (2) literary criticism will be used as a testbed for evaluating representations.
Acknowledgement
We thank the anonymous reviewers for their insightful comments. Additionally, we thank Nader Akoury, Garrett Bernstein, Chenghao Lv, Ari Kobren, Kalpesh Krishna, Saumya Lal, Tu Vu, Zhichao Yang, Mengxue Zhang and the UMass NLP group for suggestions that improved the paper's clarity, coverage of related work, and analysis experiments.

Question: How do they model a city description using embeddings?
Answer: We experiment with three different pretrained representations: ELMo BIBREF5 , BERT BIBREF6 , and GloVe BIBREF18 . To produce a single city embedding, we compute the TF-IDF weighted element-wise mean of the token-level representations. For all pretrained methods, we additionally reduce the dimensionality of the city embeddings to 40 using PCA for increased compatibility with our clustering algorithm.

Question: How do they obtain human judgements?
Answer: Using crowdsourcing

Question: Which clustering method do they use to cluster city description embeddings?
Answer:  We devise a simple clustering algorithm to approximate this process. First, we initialize with random cluster assignments and define “cluster strength” to be the relative difference between “intra-group” Euclidean distance and “inter-group” Euclidean distance. Then, we iteratively propose random exchanges of memberships, only accepting these proposals when the cluster strength increases, until convergence.