Hochschule Darmstadt - Fb Informatik

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Module:Natural Language Processing
Module numbers:41.5086 [PVL 41.5087; Module 41.50860]
Study programme:Dualer Master 2021 - Katalog AS: Anwendungs- und systemorientierte Module
Master 2021 - Katalog AS: Anwendungs- und systemorientierte Module
Dualer Master 2013 - Katalog AS: Anwendungs- und systemorientierte Module
Master 2013 - Katalog AS: Anwendungs- und systemorientierte Module
MN Data Science 2016 - Katalog DS-I: Data Science - Informatik
Type of course:V+P = Lecture+Practical
Weekly hours:2+2
Credit Points:6
Exam:written exam
Registering for examexplicitly and independent of booking
PVL (e.g. Practical):graded (Group project from one of the subfields of NLP - incl. Documentation; graded individual assignments)
PVL percentage:50%
Frequency of offering:each year (lastly in SS 2022)
Required knowledge:Basic concepts and ways of thinking in the field of artificial intelligence (Bachelor level)
Learning objectives:The students will
  • understand the relevance of Natural Language Processing (NLP) as a sub-field of Artificial Intelligence
  • understand the complexity of NLP applications, and on the basis of a detailed analysis, point at the problem and become sensible w.r.t a solution
  • get familiar with NLP tools and apply them
  • acquire knowledge in the subfields of NLP: morphology, Tokenization, Tagging, electronic dictionaries, Syntax, Semantics, Machine Translation (rule-based and statistical), Text Mining, and Speech Recognition
  • understand the connection between NLP and Computational Linguistics, i.e. different views on the same field
  • become sensible to problems in the NLP field - focusing on disambiguation on different levels (word-, sentence-, text-, web)
  • have acquired theoretical skills across the entire field of NLP and will be able to apply them
  • be able to analyze an NLP problem, design & implement a prototypical solution and document the work
Content:This course will cover the following aspects of Natural Language Processing (NLP): tokenization, tagging, parsing, morphology, electronic dictionaries, problems in homonyms and disambiguation in general, machine translation, syntax, grammatical theories, CD structures, RTNs, ATNs, electronic grammar checking, statistical language processing: Bayes Rules and Hidden Markov Models.
  • Jurafsky, Daniel. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall, 2008.
  • Manning/Schütze. Foundations of Statistical Language Processing. Foundations of Statistical Natural Language Processing. MIT Press. 1999.
  • Pierre Nugues. An Introduction to Language Processing with Perl and Prolog: An Outline of Theories, Implementation and Application with Special Consideration of English, French, and German (Cognitive Technologies). Springer Berlin Heidelberg, 2009.
Lecture style / Teaching aids:Seminaristic Lecture; Videos of the Lectures; Slides
Responsibility:Bettina Harriehausen
Released:WS 2021/2022
Offered in SS 22:LN: Harriehausen
Professional competencies:
  • formal, algorithmic, mathematical competencies: medium
  • analytical, design and implementation competencies: medium
  • technological competencies: medium
  • capability for scientific work: high
Interdisciplinary competencies:
  • project related competencies: medium
  • interdisciplinary expertise: basic technical and natural scientific competence

[Fachbereich Informatik] [Hochschule Darmstadt]
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