Group for Experimental Methods in Humanistic Research
at Columbia University

Roget Tools

  • Phillip R. Polefrone
updates ↓

09/01/15 Added two new methods to enable export of category-count arrays to Pandas and CSV.

Following Klingenstein, Hitchcock, and DeDeo (2014)’s work on the “Old Bailey” records,1 Roget Tools is a Python class for tracking broad semantic categories through bodies of text using the top-down hierarchical structure of Peter Mark Roget’s Thesaurus.2 This hierarchy is a comprehensive and unbroken network encompassing all of Roget’s original thesaurus categories, and importing it into a Python-readable format achieves two goals. First, it enables the body of research on Roget’s thesaurus to incorporated into automated text analysis, thus providing a basis for stable interpretation of quantitative results. Second, it facilitates an integration of semantic network analysis into the analysis of textual corpora.

With this integration in mind, the library provides several primary tools for analysis. First, it enables Python-readable categorization of individual words at different levels of abstraction (i.e., specificity of semantic categorization). It can also return the full hierarchical path of all a given word’s categories to the top of Roget’s taxonomy, simultaneously measuring the path length. In addition to being applicable to individual words, both of these methods can be automatically applied to large samples of text, replacing words with their semantic categories. Roget Tools can also return the distance (in network edges) between any two words in the Thesaurus or any two nodes in the hierarchy. (See Jarmasz and Szpakowicz (2012)3 on the relevance of this measure.) Finally, given a text—be it a list of randomly selected words, a portion of a literary text, or part of the output from a topic modeling algorithm—the Roget tools can return the node or nodes that most accurately represent that text’s semantic character; this representativeness is measured as the minimum average distance in edges from each word in the list to the selected node.

This is a work in progress, and suggestions for application and development are welcome. Full instructions can be found on the main project page, which also contains projections for future development.


Roget Tools can be downloaded here.

  1. Klingenstein, Sara, Tim Hitchcock, and Simon DeDeo. “The Civilizing Process in London’s Old Bailey.” Proceedings of the National Academy of Sciences 111.26 (2014): 9419–24. Web. 20 Aug. 2014. 

  2. These tools were derived from the 1911 index to and full text of the Thesaurus available from Project Gutenberg and were generated using (1) automated regular expression text extraction on the index and (2) reconstruction of the hierarchy represented by the headings of the full 1911 edition

  3. Jarmasz, Mario, and Stan Szpakowicz. “Roget’s Thesaurus and Semantic Similarity.” arXiv:1204.0245 [cs](2012): n. pag. Web. 20 Aug. 2014.