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Course module: INFOMNLP
INFOMNLP
Natural language processing
Course infoSchedule
Course codeINFOMNLP
ECTS Credits7.5
Category / LevelM (M (Master))
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Science; Graduate School of Natural Sciences; Graduate School of Natural Sciences;
Contact persondr. R.W.F. Nouwen
E-mailR.W.F.Nouwen@uu.nl
Lecturers
Lecturer
dr. T. Deoskar
Other courses by this lecturer
Lecturer
dr. R.W.F. Nouwen
Other courses by this lecturer
Course contact
dr. R.W.F. Nouwen
Other courses by this lecturer
Teaching period
4  (24/04/2023 to 07/07/2023)
Teaching period in which the course begins
4
Time slotD: D (WED-afternoon, Friday)
Study mode
Full-time
Enrolment periodfrom 30/01/2023 up to and including 24/02/2023
Enrolling through OSIRISYes
Enrolment open to students taking subsidiary coursesYes
Pre-enrolmentNo
Post-registrationYes
Post-registration openfrom 03/04/2023 up to and including 04/04/2023
Waiting listNo
Course goals
After completing this course the student is able to:
  • build models of linguistic structure for language processing
  • understand and implement supervised (and unsupervised) estimation methods for these models
  • understand and implement search strategies and algorithms to process these structures (e.g. syntactic parsing)
  • use these techniques in NLP applications
Assessment
  • exam (50% of the final mark)
  • assignments (50%)
A repair test requires at least a 4 for the original test.
Content
This course is an advanced introduction to the study of language from a computational perspective, and to the fields of computational linguistics (CL)/Natural Language processing (NLP).  
It synthesizes research from linguistics and computer science and covers formal models for representing and analyzing words, sentences and documents.
Students will learn how to analyze sentences algorithmically, and how to build interpretable semantic representations, emphasizing data-driven and machine learning approaches and algorithms.  
The course will cover a number of standard models and algorithms  (language models, HMMs, chart and transition based syntactic parsing distributed semantic models, various neural network models) that are used throughout NLP and applications of these methods in tasks such as machine translation or text summarization.

Course form
Lectures, tutorials.

Literature
To be announced.
Competencies
-
Entry requirements
-
Prerequisite knowledge
Math, Probability theory, Programming skills
Required materials
-
Instructional formats
Lecture

Seminar

Tests
Final result
Test weight100
Minimum grade-

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Kies de Nederlandse taal