Natural Language Processing (NLP) develops techniques for the analysis and interpretation of natural language, a key component of smart search engines, personal digital assistants, and many other innovative applications. The goal of this course is to provide students with a theoretical understanding of and practical experience with the advanced algorithms that power modern NLP. The course focuses on methods that involve machine learning on text data.
Main field of studyCognitive Science
Course typeSingle subject and programme course
Course coordinatorMarco Kuhlmann
Director of studies or equivalentJalal Maleki
Available for exchange studentsYes
|Course offered for||Semester||Weeks||Language||Campus||VOF|
|Single subject course (Half-time, Day-time)||Spring 2020||v202004-202013||English||Linköping|
|F7MKS||Master Programme in Cognitive Science||2 (Spring 2020)||v202004-202013||Swedish||Linköping||v|
Main field of studyCognitive Science
Course levelSecond cycle
Course offered for
- Master Programme in Cognitive Science
For admission to the course, the specific entry requirements that apply for the Master’s Programme in Cognitive Science must be met. In addition, the student must have successfully completed a course in language technology worth at least 6 ECTS credits, or courses in programming, data structures, and algorithms worth at least 18 ECTS credits.
Intended learning outcomes
After completion of the course, the student should on an advanced level be able to:
- explain state-of-the-art natural language processing algorithms and analyse them theoretically
- implement natural language processing algorithms and apply them to practical problems
- design and carry out evaluations of natural language processing components and systems
- seek, assess and use scientific information within the area of natural language processing
Natural Language Processing (NLP) develops techniques for the analysis and interpretation of natural language, a key component of smart search engines, personal digital assistants, and many other innovative applications. The goal of this course is to provide students with a theoretical understanding of and practical experience with the advanced algorithms that power modern NLP.
The course focuses on methods that involve machine learning on text data. The course covers the following areas: State-of-the-art NLP algorithms for the analysis and interpretation of words, sentences, and texts. Relevant machine learning methods based on statistical modelling, combinatorial
optimisation, and neural networks. NLP applications. Validation methods. NLP tools, software libraries, and data. NLP research and development.
Teaching and working methods
The course is taught in the form of lectures, lab sessions, and seminars in connection with a minor project. The student is expected to study independently, individually and in groups. The course is given in English.
The course is examined by lab assignments, project assignments, and a written exam. Detailed information can be found in the study guidelines.
Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.
Students who have passed an examination may not retake it in order to improve their grades.
GradesThree-grade scale, U, G, VG
Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus.
The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.
DepartmentInstitutionen för datavetenskap
|KTR1||Optional tests||U, G||0 credits|
|TEN1||Written exam||U, G, VG||2 credits|
|LAB1||Laboratory work||U, G, VG||2 credits|
|UPG1||Project work||U, G, VG||2 credits|
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