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CS 539 — Introduction to Natural Language Processing
(dt. Einführung in die natürliche Sprachverarbeitung (NLP))

Level, degree of commitment Specialization module, compulsory elective module
Forms of teaching and learning,
Lecture (2 SWS), recitation class (2 SWS) or internship mit hohem Computer Scienceanteil (insg. 180 Std),
180 hours (60 h attendance, 120 h private study)
Credit points,
formal requirements
6 CP
Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
Examination type: Two partial examinations: Written examination (3 cp) and written elaboration (3 cp)
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Data Science.
One semester,
each summer semester
Person in charge of the module's outline Prof. Dr. Lucie Flek


  • An overview of the goals, challenges, and applications of NLP.
  • Web data processing, conversion of words into their basic forms (tokenization, stemming, lemmatization).
  • text representation (words, sentences, paragraphs, documents), word embeddings, word similarity
  • algorithms for text classification and methods for measuring and evaluating the performance of these algorithms
  • Use of lexical resources in NLP
  • Syntactic analysis (part-of-speech tagging, chunking, and parsing)
  • Techniques for extracting meaning from text (semantic analysis)
  • NLP applications (e.g., document similarity, sentiment analysis,
  • Named entity detection, question answering, summaries, fake news detection, plagiarism detection,
  • Abusive language detection, opinion mining...).

Qualification Goals

Students will

  • Know the technical perspective on Natural Language Processing (NLP), the field of Artificial Intelligence that deals with the processing and understanding of human language.
  • Know methods for developing computer software that understands and processes human language.
  • know modern data-driven approaches, with an emphasis on machine learning techniques.
  • Are able to apply their knowledge in group work on real NLP projects.
  • Are able to develop their own systems that interpret written language. Applications covered vary in complexity and include, for example, Entity Recognition, Sentiment Analysis, Semantic Similarity, and Question Answering.


None. The competences taught in the following modules are recommended: Machine Learning and Introduction to Statistics or Elementary Probability and Statistics or Elementary Stochastics.


The module can be attended at FB12 in study program(s)

  • B.Sc. Data Science
  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Business Informatics

When studying M.Sc. Data Science, this module can be attended in the study area Application Area Modules.

The module can also be used in other study programs (export module).

The module is assigned to the focus area Languages. Further information on eligibility can be found in the description of the study area.

Recommended Reading

  • Eisenstein, Jacob. "Introduction to natural language processing. MIT press", (2019).
  • Jurafsky, Daniel, and James H. Martin. "Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition." (2000).

Please note:

This page describes a module according to the latest valid module guide in Winter semester 2023/24. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

  • Winter 2016/17 (no corresponding element)
  • Summer 2018 (no corresponding element)
  • Winter 2018/19 (no corresponding element)
  • Winter 2019/20 (no corresponding element)
  • Winter 2020/21 (no corresponding element)
  • Summer 2021 (no corresponding element)
  • Winter 2021/22 (no corresponding element)
  • Winter 2022/23 (no corresponding element)
  • Winter 2023/24

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.