Linguistic Data and Annotations (Summer Semester 2027)

Time & Location:

Lecture/Vorlesung: Mi 14:15–15:45, Seminarraum 1.12 (C 7.3)

Instructors: Dr Volha Petukhova

***ANNOUNCEMENTS***

Veranstaltungsnr. in LSF TBA

Please join the course Teams TBA

All class material will be published in Teams!

Study Program: Bachelor CoLi and BA Language Science (WP2); minor Bachelor Informatics

This course will teach a multistage process and important techniques in natural language data collection and preparation, and linguistic annotation that are needed to set an AI &ML model up for success. Students will learn how to define an annotation goal for a given NLP task, how to collect a dataset, define an annotation model, evaluate and adjudicate the annotations, create a gold standard corpus, perform a variety of statistical analytics over the corpus, train several machine learning (ML) algorithms on the dataset by selecting effective features drawn from the annotations, evaluate the learned ML models and compare their evaluation results, come up with a (strong) baseline model for the initial NLP task.

Organisation:

The course consists of lectures, lab group sessions and the final seminar. The students work both individually and by exchanging ideas in larger groups (e.g. working on projects). Presenting project outcomes in the final seminar will serve as final evaluation.

Basic reading:

Pustejovsky, J., & Stubbs, A. (2012). Natural Language Annotation for Machine Learning: A guide to corpus-building for applications. ” O’Reilly Media, Inc.”

Detailed Plan of Lectures and Tutorials

1. Linguistic data annotation: needs, challenges; the layers of linguistic description

Reading:
Pustejovsky, J., & Stubbs, A. (2012). Natural Language Annotation for Machine Learning: A guide to corpus-building for applications. " O'Reilly Media, Inc." (Ch. 1)

2. Multimodal Data Collection

goals, set-ups and collection methods, existing resources and data banks, metadata, data protection, ethics and privacy

Reading:
Pustejovsky, J., & Stubbs, A. (2012). Natural Language Annotation for Machine Learning: A guide to corpus-building for applications. " O'Reilly Media, Inc."(Ch.2)

3. Linguistic Annotations

Reading:
Pustejovsky, J., & Stubbs, A. (2012). Natural Language Annotation for Machine Learning: A guide to corpus-building for applications. " O'Reilly Media, Inc."(Ch.4,5,6)

4. Basics of Corpus Analytics

probabilities, n-grams, language models

Lecture (05.05.2027)

Reading:
Pustejovsky, J., & Stubbs, A. (2012). Natural Language Annotation for Machine Learning: A guide to corpus-building for applications. " O'Reilly Media, Inc." (Ch.3)

5. Training: Baseline ML Models

Lecture (11.05.2026)

Reading:
Pustejovsky, J., & Stubbs, A. (2012). Natural Language Annotation for Machine Learning: A guide to corpus-building for applications. " O'Reilly Media, Inc." (Ch.7)

Group Work

12.05.2027- 16.06.2027 and 16.06.2027- 21.07.2027

Presentation 1: project idea 16.06.2027

Presentation 2: final project results 21.07.2027

For any questions, please send an email to:
v.petukhova@lsv.uni-saarland.de
Use subject tag: [LD&A_27]