Natural Language Processing

Learning Outcomes

  1. identify computational complexity of NLP problems
  2. evaluate open source NLP tools
  3. manipulate text and speech corpora
  4. participate in speech synthesis projects
  5. participate in speech recognition projects
  6. participate in machine translation projects

Forms of Teaching

Lectures

Independent assignments

Week by Week Schedule

  1. Computational semantics (formal semantics, semantic role labeling)
  2. Computational morphology
  3. Part of speech tagging
  4. Deterministic and stochastic grammars, constituency and dependency grammars (CFG, PCFG)
  5. Parsing algorithms (CYK, Chart), lexicalized parsing, dependency parsing
  6. Distributional semantic models
  7. Corpus-based methods, n-grams, collocations
  8. Language models, smoothing, evaluation
  9. Midterm exam
  10. Machine translation
  11. Graph based NLP: semantics; syntax and applications
  12. Natural language processing applications with regional or social dimensions
  13. Applications of social network analysis to understand language variation and change
  14. Project
  15. Final exam

Study Programmes

University graduate
Audio Technologies and Electroacoustics (profile)
Free Elective Courses (3. semester)
Communication and Space Technologies (profile)
Free Elective Courses (3. semester)
Computational Modelling in Engineering (profile)
Free Elective Courses (3. semester)
Computer Engineering (profile)
Free Elective Courses (3. semester)
Computer Science (profile)
Free Elective Courses (3. semester)
Control Systems and Robotics (profile)
Free Elective Courses (3. semester)
Data Science (profile)
Free Elective Courses (3. semester)
Electrical Power Engineering (profile)
Free Elective Courses (3. semester)
Electric Machines, Drives and Automation (profile)
Free Elective Courses (3. semester)
Electronic and Computer Engineering (profile)
Free Elective Courses (3. semester)
Electronics (profile)
Free Elective Courses (3. semester)
Information and Communication Engineering (profile)
Free Elective Courses (3. semester)
Network Science (profile)
Free Elective Courses (3. semester)
Software Engineering and Information Systems (profile)
Elective Course of the profile (3. semester)

Literature

(.), Daniel Jurafsky, James H. Martin (2019.), Speech and Language Processing (3nd edition), Prentice Hall,
(.), Christopher D. Manning, Hinrich Schütze (1999.), Foundations of Statistical Natural Language Processing, MIT Press,
(.), Ruslan Mitkov (ed.) (2005.), The Oxford Handbook of Computational Linguistics, Oxford University Press, USA,
(.), Shrikanth Narayanan, Abeer Alwan (2004.), Text to Speech Synthesis: New Paradigms and Advances, Prentice Hall PTR,

For students

General

ID 222553
  Winter semester
5 ECTS
L3 English Level
L1 e-Learning
30 Lectures
6 Exercises
15 Laboratory exercises