Practical natural language processing

Education facts

Code: LT2114

This course gives a practical introduction to different problems encountered within natural language processing, and some solutions. Students will gain practical experience in programming while solving these problems. The course is divided into four main topics: one covering basic concepts and three covering subfields of NLP – words, syntax or morphology and semantics/pragmatics. 1\. Basic concepts: Basic concepts in NLP.<br> Automata theory and mathematical linguistics.<br> Probability theory and machine learning.<br> Evaluation measurement, correctness, precision, and recall.<br> 2\. Words: Corpora and corpus annotation.<br> Finite-state methods for segmentation and morphological analysis.<br> Statistical language modeling with n-gram markov models.<br> 4\. Syntax: Part-of-speech tagging and chunking/partial parsing, making use of methods within machine learning or/and finite-state technology.<br> Common formal grammars, such as feature based and probabilistic context-free grammars.<br> Syntactic parsing.<br> 5\. Semantics and Pragmatics: Lexical semantics, lexica, Wordnet and FrameNet.<br> Word sense disambiguation with machine learning.<br> Text classification with machine learning.

Entry requirements

Successful completion of at least 7.5 credits in programming courses such as: Programming, DIT948; Imperative Programming with Basic Object-orientation, DIT012; Functional programming, DIT142; Introduction to programming, LT2111; or equivalent.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Practical natural language processing

  University of Gothenburg

  Start date: 5 November 2025

  End date: 18 January 2026

  Pace of study: 50 %