Natural Language Processing 2

Course manual 2025/2026

Course content

Do you want to taste the many flavours of cutting-edge research into Natural Language Processing? 

NLP2 is the course that will offer these exciting flavours in the form of mini research projects, pitches by leading researchers and presentations and discussions involving the students and these researchers.

NLP is becoming ever more important and is currently at the heart of AI. This is largely because natural languages are central in daily communication but also because we store our knowledge, science and history in the form of  text and voice recordings. This means that the amount of language data that is available to us electronically is increasing continuously.  With this eminent increase, a question arises as to the possibility of inducing latent structure in this data that can be useful discovering how human language understanding works, but also how to use it for engineering tasks such as machine translation, conversational agents and models of human language understanding. 

This course will cover the breadth of research topics within Natural Language Processing, considering latent and linguistic structure in language data.  The course will cover recent research on  topics such as:

• Large Language Models and deep generative models
• Machine translation and paraphrasing
• The role of perception (e.g., visual)  in language understanding
• Dialogue systems and interaction
• Inducing latent, hierarchical or linguistic structure in natural language data 
• Explainability/interpretability of NLP systems

Study materials

Other

• Recent papers in NLP - Links to required reading will be provided.

Objectives

• The student is able to analyse state-of-the-art published research articles and reports in NLP and related fields
• The student is able to critically present a scientific article in NLP and discuss it together with peers and supervisors
• The student is able to evaluate and report basic solutions to example NLP problems within multiple mini-projects
• The student is able to write an article to report the results of research on a mini-project

Teaching methods

• Lecture
• Working independently on e.g. a project or thesis
• Laptop seminar
• Presentation/symposium
• Computer lab session/practical training
• Self-study

Lectures by lecturers; Presentation of articles by students; Project work and practical training; 

Learning activities

Attendance

This programme does not have requirements concerning attendance (OER part B).

Additional requirements for this course:

• The student may be absent in max 2 out of 10 lecture slots.
• Absence needs to be communicated to the course coordinator at least a week before the absence date.

Assessment

60% for final project grade; 20% for paper presentation; 10% for moderation of paper.; 10% for attendance of lectures and contributing to discussion. 

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Additional information

Recommended prior knowledge: Natural Language Processing 1; Machine Learning: Pattern Recognition.

Contact information

Coordinator

• prof. dr. Khalil Sima'an

Staff

• K. Sima'an (Coordinator)
• S. Aycock (TA)
• A. Bavaresco (TA)
• John Gkountouras (TA)