Natural Language Processing (NLP)

Course content

Have you ever wondered how to build a system that can process, understand or generate text automatically? For instance, to translate between languages, answer questions, or recognise the names of people in text? Then this course is for you.

This course will introduce the fundamentals of natural language processing (NLP), i.e., computational models of language and their applications to text. Language is at the heart of human intelligence, giving NLP a central role in Artificial Intelligence research and development.

We will combine machine learning (ML), including fundamental formalisms and algorithms, with a strong hands-on experience, i.e., the practical implementation of the methods for concrete NLP problems.

The course will utilise interactive lecture materials built with Jupyter notebook. Course materials from last year are publicly available here:; and the course will closely follow last year’s iteration. Please skim these materials if you are in doubt about course prerequisites or course content.


The course covers the following tentative topic list:

• NLP tasks: language modelling, text classification, semantics, information extraction, parsing, pragmatics, machine translation, summarisation, question answering

• methods: text classification, structured prediction, representation and deep learning, conditional random fields, beam search

• implementations: relationship between NLP tasks, efficient implementations


Throughout the course, we will also discuss the themes of discriminative and generative learning and different ways of obtaining supervision for training statistical NLP models.

Learning outcome

Knowledge of

  • core NLP tasks (e.g. machine translation, question answering, information extraction)

  • methods (e.g. classification, structured prediction, representation learning)

  • implementations (e.g. relationship between NLP tasks, efficient implementations)


Skills to

  • identify the different kinds of NLP tasks

  • choose the correct algorithm for a given problem situation

  • implement core algorithms in Python

  • assess the most appropriate algorithms to solve a given NLP problem

  • distinguish and evaluate the advantages of different approaches to the same task


Competences to

  • decompose natural language tasks into manageable components

  • evaluate systems quantitatively and qualitatively

  • apply the learned skills in a wider context to areas that face similar challenges, for example data science or political science research, or gene sequencing

The format of the class consists of lectures (including guest lectures), exercises, and project work.

Selected papers and book chapters. See Absalon when the course is set up.

Knowledge of machine learning (probability theory, linear algebra, classification, neural networks) and programming (Python) is required, either through formal education or self-study. No prior knowledge of natural language processing or linguistics is required.

Relevant machine learning competencies can be obtained through one of the following courses:
- NDAK22002U Advanced Deep Learning (ADL)
- NDAK22000U Machine Learning A (MLA)
- NDAK15007U Machine Learning (ML)
- NDAK16003U Introduction to Data Science (IDS)
- Machine Learning, Coursera

Academic qualifications equivalent to a BSc degree are recommended.

If you are in doubt about if you meet the course prerequisites, you can check the course materials from last year here: https:/​/​​copenlu/​stat-nlp-book.

This course will teach the fundamentals of natural language processing, in terms of methods, typical tasks and implementations. For those students with a specific interest in opinion and data mining, the course NDAK14004U Web Science (WS) is recommended. There will be no significant overlap between the two courses, and students are welcome to attend both of them.

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Written examination, 1.5 hours under invigilation
Written assignment, During course
Type of assessment details
The exam consists of two parts:

1. A group project to count for 50% of the mark, written during the course (either group members hand-in individual reports or they mark their contribution in the group report).

2. A 1.5 hours written exam that counts for 50% of the mark.
All aids allowed

The use of Large Language Models (LLM)/Large Multimodal Models (LMM) – such as ChatGPT and GPT-4 – is permitted for the written assignment.

Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

The re-exam consists of two parts:

1) Resubmission of (possibly revised) final project. The revised project has to be handed in no later than 2 weeks before the re-exam week.
2) A 30 minutes individual oral examination without preparation, based on submitted project and full syllabus.

The final grade is based on an overall assessment.

Criteria for exam assessment

See Learning Outcome.


Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 14
  • Theory exercises
  • 57
  • Practical exercises
  • 57
  • Project work
  • 50
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 1
No limit
The number of seats may be reduced in the late registration period
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Computer Science
Contracting faculty
  • Faculty of Science
Course Coordinators
  • Daniel Hershcovich   (2-71754d71763b78823b7178)
  • Anders Søgaard   (8-757167696363746642666b306d7730666d)

Daniel Hershcovich
Anders Søgaard
Desmond Elliot

Saved on the 24-08-2023

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Courseinformation of students