The Trace of Theory (TracT) project looked at the question “Can we find and track theory, especially literary theory, in texts using computers?” We proposed to do this on the large collections of the HathiTrust using a variety of techniques with the support of the HathiTrust Research Centre. This project brought together researchers who are part of the Text Mining the Novel project (http://novel-tm.ca/) led by Dr. Andrew Piper at McGill University.
It takes a two-step approach to trying to track theory through its textual traces.
1. Subsetting: We propose to experiment with two methods for identifying “theoretical” subsets of texts from large collections like the Google-digitized dataset (GDD) of the HathiTrust. The goal would be to identify subsets of the full GDD that are theoretical in different ways.
2. Mining: We would then experiment with large-scale text-mining and clustering methods on these subsets. In particular we propose to try topic modelling and other forms of clustering.
Final project can be found at https://docs.google.com/document/d/1BwWd_tR6TtA7kp6QYQuAQte88Ri4Vvcx9Bho7NTKQ6o/edit?ts=5665d43e# please refer to the report for project background, technical details, and community impact.
Geoffrey Rockwell (Univ of Alberta), Laura Mandell (Texas A&M Univ), Stefan Sinclair (McGill Univ), Matthew Wilkens (Notre Dame), Susan Brown (Univ of Guelph)
Boris Capitanu (HTRC), Kahyun Choi (HTRC)
- Using keyword lists to identify philosophical and literary critical texts
We extracted philosophical and literary critical texts by using list of keywords. A python script was developed to calculate the relative frequency of each word in a text and do it over a collection. The process also calculates the sum of the relative frequencies giving us a simple measurement of the use of philosophical keywords in a text. We tested this approach on the Project Gutenberg and HathiTrust collections. 2. Machine learning to identify subsets
2. Machine learning to identify subsets.
In addition to keyword list approaches we also tried machine learning approaches for identifying subsets.
We started from a training sets with the 20 philosophical texts and 20 non-philosophical texts from the Project Gutenberg mentioned above and applied a list of supervised algorithms. Below shows results using different algorithms.
b) unsupervised. Our approach mixed token unigram features with metadata and formal features in ways that may be portable to other text clustering and classification tasks.
3. Adapting the Galaxy Viewer.
HathiTrust Reader with the Robert Browning Text Seen in Galaxy Viewer
https://github.com/htrc/ACS-TT/blob/master/tools/notebooks/ClassifyingPhilosophicalText.ipynb (Classifying philosophical texts)
http://nbviewer.ipython.org/github/htrc/ACS-TT/blob/master/tools/notebooks/Unsupervised%20Clustering%20Philosophy.ipynb (Usupervised classification of philosophical genres)