  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.
