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

Level, degree of commitment Specialization module, depends on importing study program
Forms of teaching and learning,
workload		 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)
Language,
Grading		 English,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Data Science.
Origin M.Sc. Data Science, M.Sc. Data Science
Duration,
frequency		 One semester,
each summer semester
Person in charge of the module's outline Prof. Dr. Daniel Braun

Contents

  • 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

Translation is missing, sorry. German original:

Die Studierenden

  • können die technische Perspektive auf Natural Language Processing (NLP) beschreiben, also auf den Bereich der Künstlichen Intelligenz, der sich mit der Verarbeitung und dem Verständnis menschlicher Sprache beschäftigt,
  • können Methoden zur Entwicklung von Computersoftware erklären, die menschliche Sprache versteht und verarbeitet,
  • können moderne datengesteuerte Ansätze erläutern, wobei der Schwerpunkt auf Techniken des maschinellen Lernens liegt,
  • können ihre Kenntnisse in Gruppenarbeit an realen NLP-Projekten anwenden,
  • sind in der Lage, ihre eigenen Systeme zu entwickeln, die geschriebene Sprache interpretieren. Die behandelten Anwendungen variieren in ihrer Komplexität und umfassen zum Beispiel Entity Recognition, Sentiment Analysis, Semantic Similarity und Question Answering.

Prerequisites

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


Applicability

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 Free Compulsory Elective Modules.

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

The module is assigned to Computer Science. 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 2025/26. 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
  • Winter 2025/26

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.