Natural Language Processing 2026

The 2026 episode at Faculty of Mathematics, Physics and Informatics of Comenius University

Lectures M-XII

Friday, 9:00 - 10:20 (voluntary)

Labs M-XII

Friday, 10:20 - whenever (voluntary)

Course description#

This course tries to go deeper into how we can represent human language (say English or Slovak) in a way that can be processed by computational systems (a.k.a. computer programs), and how this representation can then be used to do interesting things, such as

• translation
• question answering
• grammatical error correction
• summarization
• text (like poems or song lyrics) generation
• and much more…

All of this combined is part of a field called Natural Language Processing, which ended up being the name of the course.

Feel free to check out the previous year’s class webpage as well!

Lectures#

Lecture I: The NLP Wohoo#

What can language models do – and how did we get here?

Discussed material:

• Live demos: translation, question answering, reasoning, code generation, multimodal understanding, agents
• A whirlwind tour of NLP: from ELIZA (1966) to GPT-5 (2026)
• What this course will cover and how it all fits together

Supplementary resources:

• Eliza Bot Demo: one of the most famous early NLP systems. Try having a conversation – you may be surprised.
• Intro to Large Language Models by Andrej Karpathy: probably the best one-hour introduction to the current state of LLMs.
• LMSYS Chatbot Arena Leaderboard: as many would say, “the only benchmark worth taking a look at”.

More lectures coming soon…

Resources#

Introduction to Natural Language Processing by Jacob Eisenstein

Speech and Language Processing, 3rd Edition by Daniel Jurafsky, James H Martin

A Primer on Neural Network Models for Natural Language Processing by Yoav Goldberg

Neural Network Methods for Natural Language Processing by Yoav Goldberg

Similar Courses Elsewhere#

There are more than a few similar (and often times even better) courses out there. Here is a sample:

• Computational Linguistics @ UPenn
• Natural Language Processing @ CMU
• CS 224N: NLP with Deep Learning @ Stanford
• Advanced NLP (11-711) @ CMU
• Quantitative Methods for NLP (6.8610) @ MIT
• Large Language Models @ ETH Zurich

Grading#

Assignments are available via Google Classroom (the class code is hcexzhmi -- feel free to use the following invite link ) but they are also available in the following repository on GitHub: https://github.com/NaiveNeuron/nlp-exercises

Check out the Project Ideas for 2026!