The Historical Significance of Textual Distances
Abstract
Measuring similarity is a basic task in information retrieval, and now often a building-block for more complex arguments about cultural change. But do measures of textual similarity and distance really correspond to evidence about cultural proximity and differentiation? To explore that question empirically, this paper compares textual and social measures of the similarities between genres of English-language fiction. Existing measures of textual similarity (cosine similarity on tf-idf vectors or topic vectors) are also compared to new strategies that use supervised learning to anchor textual measurement in a social context.
Keywords
Cite
@article{arxiv.1807.00181,
title = {The Historical Significance of Textual Distances},
author = {Ted Underwood},
journal= {arXiv preprint arXiv:1807.00181},
year = {2018}
}
Comments
Preprint of a paper for the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018). Code is available at https://github.com/tedunderwood/genredistance or, archivally, at https://zenodo.org/record/1300934