NPFL128 Language Technologies in Practice
2025 Summer

Instructor:	Jirka Hana
e-mail:	Jirka.<my last name>@gmail.com (start the email's subject with NPFL128)
Time & Place:	Monday 15:40 - 17:10 S1

 

1. Description and Objectives of the Course

The course surveys solutions to common NLP tasks ranging from entity recognition to text generation. It evaluates various approaches (machine learning, rules, larger resources, ...) and their combinations.

Most of the course consists of students presenting and discussing papers relevant to a given topic. Part of the course also involves implementing a prototype system, typically replicating one described in one of the papers.

2. Discussion

This course is organized as a discussion of important papers. Everybody reads all the papers to be able to participate in the discussion. For each paper, one student will be responsible for presenting the paper and leading the discussion.

Not every detail of each paper is important to us now. Do not present aspects that are no longer relevant (e.g., there was a lot of development in word embeddings, so there is typically no reason to discuss how a paper from 2001 handled it).

• Choose a paper and tell me which one.
• Reads all papers
• Create a Google doc document named (NPFL128 <your_name>) and share it with me. Use the same document for all papers.
• For a paper that you did not choose, write 4-8 bullets summarizing:
  • the basic idea of the paper
  • aspects of the paper you find interesting, useful in other areas, etc.
  • aspects of the paper you think could be improved
  • Do this before the paper is scheduled, and send me a notification about it.
• For the selected paper, create a presentation and send it to me by noon on the Monday before the class.

3. Project

There is one programming [Project] due on July 31 (talk to me if you cannot meet the deadline). Note that using git and Pull requests is required.

4. Grading

5. Schedule (to be updated)

Candidate topics and papers

• Morphology and General approches:
• Yarowsky & Wicentowski (2000): Minimally Supervised Morphological Analysis by Multimodal Alignment
• Cucerzan & Yarowsky (2002): Bootstrapping a Multilingual Part-of-speech Tagger in One Person-day and Cucerzan & Yarowsky (2003): Minimally Supervised Induction of Grammatical Gender (presented together)
• LLM:
• RAG: Gao et al (2023): Retrieval-Augmented Generation for Large Language Models: A Survey. or similar
• Prompting methods (CoT, GoTs, etc): Besta et al (2024): Graph of Thoughts: Solving Elaborate Problems with Large Language Models or similar (see this post for a quick overview)
• Hallucinations: Tonmoy et al (2024): A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models or similar
• Agents: TBD
• Entity recognition and linking
• Nadeau & Sekine (2007): A survey of named entity recognition and classification
• Cucerzan (2007): Large-Scale Named Entity Disambiguation Based on Wikipedia Data
• Sentiment Analysis:
• taken: Liu (2017): Many Facets of Sentiment Analysis
• taken: Mohammad (2017): Challenges in Sentiment Analysis
• Evaluation:
• Ribeiro et al (2020): Beyond Accuracy: Behavioral Testing of NLP Models with CheckList.
• Other:
• Shlens: A Tutorial on Principal Component Analysis

Discussion on	Presented by	Topic	Slides
Feb 17	me	Introduction
Feb 24
Mar 3
Mar 10
Mar 17
Mar 24
Mar 31
Apr 7
Apr 14
Apr 21		NO CLASS
Apr 28
May 5
May 12
May 19		NO CLASS