252-3005-00L  Natural Language Processing

SemesterAutumn Semester 2023
LecturersR. Cotterell
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
252-3005-00 VNatural Language Processing3 hrs
Mon12:15-14:00HG F 1 »
Tue13:15-14:00HG F 1 »
R. Cotterell
252-3005-00 UNatural Language Processing3 hrs
Wed16:15-19:00HG F 7 »
R. Cotterell
252-3005-00 ANatural Language Processing1 hrsR. Cotterell

Catalogue data

AbstractThis course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
ObjectiveThe objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques.
ContentThis course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
LiteratureLectures will make use of textbooks such as the one by Jurafsky and Martin where appropriate, but will also make use of original research and survey papers.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits7 credits
ExaminersR. Cotterell
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 180 minutes
Additional information on mode of examinationGrade: 70% exam, 30% mandatory project.
Written aidsTwo A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkInformation
LiteratureIntroduction to Natural Language Processing - Eisenstein
SPEECH and LANGUAGE PROCESSING - Jurafsky and Martin
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

Places600 at the most
Waiting listuntil 01.10.2023

Offered in

ProgrammeSectionType
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Cyber Security MasterElective CoursesWInformation
DAS in Data ScienceMachine Learning and Artificial IntelligenceWInformation
Data Science MasterSubject-Specific ElectivesWInformation
Data Science MasterCore ElectivesWInformation
Computer Science MasterElective CoursesWInformation
Computer Science MasterMinor in Machine LearningWInformation
Mathematics MasterMachine LearningWInformation
Computational Science and Engineering MasterRoboticsWInformation
Science, Technology, and Policy MasterData and Computer ScienceWInformation
Statistics MasterSubject Specific ElectivesWInformation