https://github.com/diyclassics/exploratory-philology-rostock

This repo contains notebooks for an online workshop at Universität Rostock on 11.7.22-12.7.22 on learning to explore historical language text with Python.
https://github.com/diyclassics/exploratory-philology-rostock

Last synced: about 1 year ago
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This repo contains notebooks for an online workshop at Universität Rostock on 11.7.22-12.7.22 on learning to explore historical language text with Python.

• Host: GitHub
• URL: https://github.com/diyclassics/exploratory-philology-rostock
• Owner: diyclassics
• Created: 2022-07-10T12:41:26.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2023-06-06T00:48:38.000Z (about 3 years ago)
• Last Synced: 2025-02-17T09:43:35.489Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 3.78 MB
• Stars: 1
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.MD

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README

          
# Exploratory Text Analysis for Philologists Workshop @ Universität Rostock Zoom

*Written by [diyclassics](https://diyclassics.github.io). Last updated 10.7.2022.*

This repo contains notebooks for an online workshop at Universität Rostock on 11.7.22-12.7.22 on learning to explore historical language text with Python.

These notebooks can be run in Binder (all data/models already available) here: 

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/diyclassics/exploratory-philology-rostock/HEAD?labpath=notebooks)

## Description

Where should historical-language researchers get started with computational analysis of the texts they work on? This is the focus of Patrick Burns' latest project “Exploratory Philology” and this two-day workshop will guide participants through a series of computer programming exercises designed to help them, building on the work of Nick Montfort and Marina Umaschi Bers among others, learn “how to think with computation” as philologists. Sample “exploratory” exercises including counting animal terms, searching for acrostics, generating random text, and creating cryptogram puzzles.

Two half-day sessions will give participants experience reading, modifying, and writing computer code (in Python and using the Classical Language Toolkit and CLTK Readers) to explore textual data for philological ends—Session 1: What is Exploratory Text Analysis? and Session 2: Exploratory Text Analysis in Practice. The first session will guide participants through six text analysis experiments designed to introduce them to describing, searching, manipulating, and generating texts primarily in Ancient Greek and Latin. The second session will dive deeper into the design of corpus readers and how to use them in their own research projects. Participants are encouraged to bring sample texts from their own projects to learn how to build custom readers.

No prior installation is necessary to participate in the workshop. All experiments can be run, refactored, and rerun in a web browser like Firefox. That said, the final part of Session 2 will demonstrate how to get a minimal installation of the tools from the workshop running on a local machine (Mac/Windows); for participants who want to follow along with this part of the workshop, it will be helpful to have a code editor (e.g. Microsoft Visual Studio Code) already installed.

## Links

- [Classical Language Toolkit](https//cltk.org)

- [CLTK Readers](https://github.com/diyclassics/cltk_readers)

- CLTK Tesserae Corpora: [Latin](https://github.com/cltk/lat_text_tesserae) [Greek](https://github.com/cltk/grc_text_tesserae)

## Recommended reading

- Montfort, N. 2021. *Exploratory Programming for the Arts and Humanities*. 2nd edition. Cambridge, MA: MIT Press.

- Rockwell, G., and Sinclair, S. 2016. *Hermeneutica: Computer-Assisted Interpretation in the Humanities*. Cambridge, MA: MIT Press.

## Further reading

- Bengfort, B., Bilbro, R., and Ojeda, T. 2018. *Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning*. Sebastopol, CA: O’Reilly.

- Karsdorp, F., Kestemont, M., and Riddell, A. 2021. *Humanities Data Analysis: Case Studies with Python*. Princeton: Princeton University Press.

- Lane, H., Hapke, H., and Howard, C. 2019. *Natural Language Processing in Action: Understanding, Analyzing, and Generating Text with Python*. Shelter Island, NY: Manning Publications.

- Mattingly, W. 2021. "Latin Natural Language Processing" on YouTube. [https://www.youtube.com/playlist?list=PL2VXyKi-KpYuKYUkf1aODP4vHpOh7yvjr].

- Walsh, M. 2021. *Introduction to Cultural Analytics & Python*. [https://melaniewalsh.github.io/Intro-Cultural-Analytics/welcome.html].