From September 9 to 13, I will be giving a class on Reflected Text Analysis beyond Linguistics, as part of the DGfS-CL fall school 2019 at the IMS at Stuttgart University. The class is also part of the CRETA Coaching.

This post serves as course page, containing the material, agenda etc.

Agenda

Day	14:00-15:30		16:00-17:30
Monday	Introduction, Overview, Annotation	☕	Annotation exercise, Inter-Annotator Agreement
Tuesday	Machine learning overview and evaluation, algorithms	☕	Algorithms
Wednesday	Introduction into shared task, hands on session	☕	Hands on session
Thursday	excursion to the German Literature Archive, Marbach (starting at 1pm!)
Friday	Hands on session, shared task evaluation	☕	What to do next, closing discussion

Material

Participants are asked to install the following things on their computers (this can be done during the first day of the class)

Python

• Python: If your computer already has Python 2, there is no need to update. If you’re installing Python from scratch, please use Python 3.
• pip: The Python package manager
• The Python libraries nltk and requests.

Detailed instructions for Windows, Mac OS X and Linux can be found here (PDF file). The file test_install.py can be used to test the installation.

Text Editor

For editing Python files, participants will need a plain text editor. We recommend the following:

• Windows: Notepad++
• Mac OS X: TextMate

Slides

Monday

• Slides
• Example annotation guidelines: STTS tag set (German parts of speech), Penn Treebank tag set (English parts of speech)
• Texts for annotation exercise: Lewis Carroll: Alice in Wonderland, chapter 11, Jules Verne: Around the World in 80 Days, chapter 13, Mary Rowlandson: Narrative of the Captivity and Restoration of Mrs. Mary Rowlandson

Tuesday

• Slides
• Addendum, slides

Wednesday

• Slides on shared tasks and hackatorial
• Hackatorial package: Please download the zip file and extract it into a directory on your drive. The zip file contains
  • Data with annotated entity references (sub directory data)
  • Code for training, testing and uploading (sub directory code)
  • Resources used for feature extraction (sub directory static)
• List of implemented features

Friday

• Slides on Hackatorial evaluation
• Slides on what to do next
• Hackatorial results
  • Bundestag
  • Grimm
  • Parzival
  • Werther

Projects (for ECTS credit points)

If you’re interested in getting ECTS credit points for taking part in this class, you’ll need to conduct a small project, according to the following recipe (unless we agreed on a different plan):

1. Pick a task (e.g., part of speech tagging)
2. Pick a non-standard text that is not too long (e.g., a poem)
3. Create a gold standard by applying the annotation guidelines for the task
4. Apply an existing tool for the task
5. Evaluate the tool against your annotations
6. Either
   • Develop hypotheses for improving/adapting the tool or
   • Retrain the tool on existing training data and your own corpus
   • Re-evaluate it after adding your own data
7. Write a brief report on this and send it to me

Your project should be finished (and the report sent to me) before October 14.