Sensing Ambiguity in Henry James' "The Turn of the Screw"

TL;DR

This paper combines computational text analysis with literary interpretation to demonstrate that ambiguity in Henry James' 'The Turn of the Screw' is integral to its meaning and can be effectively detected using NLP techniques.

Contribution

It introduces a novel interdisciplinary approach that models literary ambiguity as persistent topics and employs NLP metrics to analyze subtle ambiguity in classic literature.

Findings

Dual interpretations are consistently modeled as topics throughout the novella.

Cosine similarity and word mover's distance detect subtle ambiguity effectively.

Computational analysis complements close reading in understanding literary ambiguity.

Abstract

Fields such as the philosophy of language, continental philosophy, and literary studies have long established that human language is, at its essence, ambiguous and that this quality, although challenging to communication, enriches language and points to the complexity of human thought. On the other hand, in the NLP field there have been ongoing efforts aimed at disambiguation for various downstream tasks. This work brings together computational text analysis and literary analysis to demonstrate the extent to which ambiguity in certain texts plays a key role in shaping meaning and thus requires analysis rather than elimination. We revisit the discussion, well known in the humanities, about the role ambiguity plays in Henry James' 19th century novella, The Turn of the Screw. We model each of the novella's two competing interpretations as a topic and computationally demonstrate that the duality between them exists consistently throughout the work and shapes, rather than obscures, its meaning. We also demonstrate that cosine similarity and word mover's distance are sensitive enough to detect ambiguity in its most subtle literary form, despite doubts to the contrary raised by literary scholars. Our analysis is built on topic word lists and word embeddings from various sources. We first claim, and then empirically show, the interdependence between computational analysis and close reading performed by a human expert.