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<!doctype html>
<head>
<title>Quintessence Alpha: About</title>
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<h2> How to Cite </h2>
<p>
Project Quintessence: A Dynamic Explorer for Early Modern Texts, UC Davis DataLab. 2021. http://quintessence.ds.library.ucdavis.edu.
</p>
<hr>
<h2> Team </h2>
<h4> Project Leads </h4>
<p> Samuel Pizelo (co-creator) </p>
<p> Arthur Koehl (co-creator) </p>
<p> Carl Stahmer, PhD (Executive Director of DataLab) </p>
<h4> Developers </h4>
<p> Chandni Nagda </p>
<p> Samuel Pizelo </p>
<p> Arthur Koehl </p>
<h4> UI/UX Consultant </h4>
<p> Kimmy Hescock </p>
<hr>
<h2> References </h2>
<h4> EEBO</h4>
<p>
Early English Books Online Text Creation Partnership, 2011. Web. <a href="https://quod.lib.umich.edu/e/eebogroup/">https://quod.lib.umich.edu/e/eebogroup/.</a>
</p>
<p>
Early English Books Online. Web. <a href="https://search.proquest.com/eebo/advanced"> https://search.proquest.com/eebo/advanced.</a>
</p>
<h4> Digital Projects </h4>
<p>
Early Modern Print: Text Mining Early Printed English. Humanities Digital Workshop at Washington University in St. Louis.<a style="display: inline" href="https://earlyprint.wustl.edu">https://earlyprint.wustl.edu</a>
</p>
<p>
Goldstone, Andrew, “dfr-browser”, <a style="display: inline" href="http://agoldst.github.io/dfr-browser/">http://agoldst.github.io/dfr-browser</a>
</p>
<h4> Software </h4>
<p>Burns, Philip R. (2013) "MorphAdorner v2: A Java Library for the Morphological Adornment of English Language Texts." Evanston, IL. Northwestern University. Web. https://morphadorner.northwestern.edu/morphadorner/download/morphadorner.pdf.
</p>
<p>
Sievert, Carson, 2015. LDAvis: Interactive Visualization of Topic Models. R package version 0.3.2.
</p>
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<h4> Journal Articles </h4>
<p>
Blei, David M., et al. “Latent Dirichlet Allocation.” <i>Journal of Machine Learning Research</i>, vol. 3, Mar. 2003, pp. 993--1022.
</p>
<p>
Blei, David M., and John D. Lafferty. “A Correlated Topic Model of Science.” <i>The Annals of Applied Statistics</i>, vol. 1, June 2007, pp. 17--35, doi:<a href="https://doi.org/10.1214/07-AOAS114">10.1214/07-AOAS114</a>.
</p>
<p>
Garg, Nikhil, et al. “Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes.” <i>National Academy of Sciences</i>, vol. Proceedings of the Natioanl Academy of Sciences vol. 115 no. 16 E3635-E3644, Apr. 2018.
</p>
<p>
Hamilton, William L., et al. “Diachronic Word Embeddings Reveal Stastical Laws of Semantic Change.” <i>Association for Computational Linguistics</i>, vol. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Aug. 2016, pp. 1489--1501, doi:<a href="https://doi.org/10.18653/v1/P16-1141">10.18653/v1/P16-1141</a>.
</p>
<p>
Kim, Yoon, et al. “Temporal Analysis of Language through Neural Language Models.” <i>Association for Computational Linguistics</i>, vol. Proceedings of the {ACL} 2014 Workshop on Language Technologies and Computational Social Science, June 2014, pp. 61--65, doi:<a href="https://doi.org/10.3115/v1/W14-2517">10.3115/v1/W14-2517</a>.
</p>
<p>Kulkarni, Vivek, et al. “Stastically Significant Detection of Linguistic Change.” <i>WWW ’15</i>, vol. Proceedings of the 24th International Conference on World Wide Web, May 2015, pp. 625--635, doi:<a href="https://doi.org/10.1145/2736277.2741627">10.1145/2736277.2741627</a>.
</p>
<p>
Miklov, Tomas, et al. <i>Efficient Estimation of Word Representations in Vector Space</i>. 2013, <a href="http://arxiv.org/abs/1301.3781">http://arxiv.org/abs/1301.3781</a>.
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