Hide menu

TDDE09 Natural Language Processing

Natural Language Processing (NLP) develops techniques for the analysis and interpretation of natural language – a key component of smart search engines, personal digital assistants, and many other innovative applications. The goal of this course is to provide you with a theoretical understanding of and practical experience with the advanced algorithms that power modern NLP. The course focuses on methods that involve machine learning on text data.

Learning objectives

On completion of the course, you should be able to:

  1. explain state-of-the-art NLP algorithms and analyse them theoretically
  2. implement NLP algorithms and apply them to practical problems
  3. design and carry out evaluations of NLP components and systems
  4. seek, assess, and use scientific information within the area of NLP

For each learning objective, there is a set of more specific knowledge requirements that outline what you need to demonstrate in order to earn a certain grade. These knowledge requirements are listed on the Examination page.

Course content

The course covers

  • state-of-the-art NLP algorithms for the analysis and interpretation of words, sentences, and texts
  • relevant machine learning methods
  • NLP applications
  • validation methods
  • NLP tools, software libraries, and data
  • NLP research and development

in the following areas: text segmentation, text classification, language modelling, part-of-speech tagging, syntactic analysis, and semantic analysis.

We have structured the course content into concepts and procedures. By concepts we mean terms and models that you should be able to explain and apply. By procedures we mean standard tasks that you should be able to perform. If a concept or procedure is classified as advanced, it is beyond what is being expected from you for a pass grade.

Teaching and working methods

The course is taught in the form of lectures, lab sessions, and seminars in connection with a minor project. You are also expected to study independently, both individually and in groups. When you plan your time for the course, you should calculate approximately

  • 53 hrs to prepare for, attend, and follow-up on the lectures
  • 53 hrs to prepare for, carry out, and follow-up on the labs
  • 53 hrs to plan, carry out, and follow-up on the project

The course is co-taught with 729A27 Natural Language Processing on the Master’s programme in cognitive science.

Course literature

There is no obligatory textbook for the course. Lecture notes for parts of the course will be made available in electronic form. For additional reading, we recommend the following books:

Feedback policy

What you can expect from us. We try our best to give you prompt, constructive, and meaningful feedback on how well you meet the knowledge requirements set out for the course. We offer feedback in various forms; you can find detailed information about this on the Examination page. Our focus is on non-examinatory, formative feedback, which you can use to improve your learning (and we can use to improve our teaching!) while the course is ongoing.

What we expect from you. We expect you to familiarise yourself with the knowledge requirements set out for the course, and to actively seek our feedback on how well you meet these requirements. We also expect you to reflect on the feedback that we provide, and to grasp opportunities to put it to good use.

Communication policy

What we expect from you. This webpage is the primary source of information about the course, and we expect you to keep yourself up-to-date with what we publish here. We also send out information via the University’s email list for the course, and we expect you to subscribe to this list and read your email on a regular basis while the course is ongoing. Check whether you are subscribed

What you can expect from us. When you contact us via email, you can expect an answer during standard working hours, 8–17. (We do not respond to email in the evening or on a weekend.) For a more personal contact, you can book an appointment with the examiner or simply drop by during office hours (during term time).

Page responsible: Marco Kuhlmann
Last updated: 2018-01-12