Natural Language Processing, 6 credits
Språkteknologi, 6 hp
TDDE09
Main field of study
Information Technology Computer Science and Engineering Computer ScienceCourse level
Second cycleCourse type
Programme courseExaminer
Marco KuhlmannDirector of studies or equivalent
Jalal MalekiEducation components
Preliminary scheduled hours: 48 hRecommended self-study hours: 112 h
Main field of study
Information Technology, Computer Science and Engineering, Computer ScienceCourse level
Second cycleAdvancement level
A1XCourse offered for
- Computer Science and Engineering, M Sc in Engineering
- Computer Science and Software Engineering, M Sc in Engineering
- Information Technology, M Sc in Engineering
- Computer Science, Master's programme
Entry requirements
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.
Prerequisites
Discrete mathematics. Good knowledge of programming, data structures, and algorithms. Basic knowledge of probability theory and optimisation. Previous courses in machine learning are recommended but no requirement for the course.
Intended learning outcomes
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. On completion of the course, the student should be able to:
- explain state-of-the-art NLP algorithms and analyse them theoretically
- implement NLP algorithms and apply them to practical problems
- design and carry out evaluations of NLP components and systems
- seek, assess and use scientific information within the area of NLP
Course content
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 given in the form of lectures, lab sessions, and seminars in connection with a minor project.
Examination
KTR1 | Optional written tests | 0 credits | U, G |
UPG1 | Project assignments | 2 credits | U, 3, 4, 5 |
LAB1 | Practical assignments | 2 credits | U, 3, 4, 5 |
TEN1 | Written examination | 2 credits | U, 3, 4, 5 |
The optional written tests give bonus points for the first attempt at the written examination. The final grade for the course is the median of the grades awarded for LAB1, TEN1, and UPG1. |
Grades
Four-grade scale, LiU, U, 3, 4, 5Other information
Supplementary courses:
Text Mining
Department
Institutionen för datavetenskapDirector of Studies or equivalent
Jalal MalekiExaminer
Marco KuhlmannEducation components
Preliminary scheduled hours: 48 hRecommended self-study hours: 112 h
Course literature
Additional literature
Compendia
Lecture notes provided by the department.
Code | Name | Scope | Grading scale |
---|---|---|---|
KTR1 | Optional written tests | 0 credits | U, G |
UPG1 | Project assignments | 2 credits | U, 3, 4, 5 |
LAB1 | Practical assignments | 2 credits | U, 3, 4, 5 |
TEN1 | Written examination | 2 credits | U, 3, 4, 5 |
The optional written tests give bonus points for the first attempt at the written examination. The final grade for the course is the median of the grades awarded for LAB1, TEN1, and UPG1. |
Additional literature
Compendia
Lecture notes provided by the department.
Note: The course matrix might contain more information in Swedish.
I | U | A | Modules | Comment | ||
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1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
1.1 Knowledge of underlying mathematics and science (G1X level) |
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X
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1.2 Fundamental engineering knowledge (G1X level) |
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X
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X
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LAB1
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1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
|
X
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TEN1
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1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
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1.5 Insight into current research and development work |
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2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
2.1 Analytical reasoning and problem solving |
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X
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LAB1
UPG1
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2.2 Experimentation, investigation, and knowledge discovery |
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X
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LAB1
UPG1
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2.3 System thinking |
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2.4 Attitudes, thought, and learning |
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X
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UPG1
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2.5 Ethics, equity, and other responsibilities |
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3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
3.1 Teamwork |
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X
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LAB1
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3.2 Communications |
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X
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UPG1
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3.3 Communication in foreign languages |
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X
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4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
4.1 External, societal, and environmental context |
X
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4.2 Enterprise and business context |
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4.3 Conceiving, system engineering and management |
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4.4 Designing |
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4.5 Implementing |
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4.6 Operating |
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5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
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5.2 Economic conditions for knowledge development |
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5.3 Identification of needs, structuring and planning of research or development projects |
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5.4 Execution of research or development projects |
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5.5 Presentation and evaluation of research or development projects |
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