Abstract Concepts in NLP

Module Description

Lecturer	Vagrant Gautam
Module Type	Hauptseminar
Language	English
First Session	14.04.2026
Time and Place	Tuesday, 15:15 - 16:45, INF 326 / SR 27
Commitment Period	tbd.

Participants

All advanced CL Bachelor students and all CL master students. Students from MSc Data and Computer Science or MSc Scientific Computing with Field of Application Computational Linguistics are welcome after getting permission from the lecturer. If the seminar should be oversubscribed, CL students will have priority.

Prerequisites for Participation

There are no prerequisites, but the course is more research-oriented than engineering-oriented.

Assessment

• Present a concept and lead a discussion about it (a sample will be given) - 35%
• Engage with other concepts (weekly/biweekly assignments as well as in-class participation) - 30%
• Write a report designing a novel NLP project that addresses a gap in how an abstract concept is conceptualized / operationalized in NLP - 35%

Contents

NLP papers commonly use various abstract concepts like “interpretability," “bias," “reasoning," “stereotypes," and so on. Each subfield has a shared understanding of what these terms mean and how we should treat them, and this shared understanding is the basis on which datasets are built to evaluate these abilities, metrics are proposed to quantify them, and claims are made about systems. But what exactly do these terms mean? And, indeed, what should they mean, and how do we measure that? These questions are the focus of this seminar on defining and measuring abstract concepts in NLP.

We will cover various concepts in NLP research from the selection below based on class interest. For each concept, we will read papers that analyze or critique how it is defined and used, and then we will use this as a lens to read, discuss, and critique 2 or more recent NLP papers that use that concept. We will also try to reimagine how we would run these projects and write these papers in light of what we have learned.

This course will help you acquire / practise the following skills, among others:

• How to read and critique papers (both interdisciplinary and more conventional NLP papers)
• How to critically evaluate aspects of conceptualization (defining an abstract concept) and operationalization(creating empirical measures of the abstract concept), towards answering questions like: What is X? How do we conceive of X? What does an abstract X mean? How do we translate our conceptions of X into something that we can observe and measure? How, concretely, can we measure it with an NLP system? How do we operationalize the measurement with data and metrics and gold labels? What should X mean? How do our choices in defining, conceptualizing and operationalizing X lead to gaps in how we make claims about X?
• How to design NLP projects in ways that address critiques and push the discipline forward

Literature

See here for a list of concepts we can choose from and associated readings.