Natural Language Processing, 6 credits

Språkteknologi, 6 hp

TDDE09

Course starting semester

Main field of study

Information Technology Computer Science and Engineering Computer Science

Course level

Second cycle

Course type

Programme course

Examiner

Marco Kuhlmann

Director of studies or equivalent

Jalal Maleki

Education components

Preliminary scheduled hours: 48 h
Recommended self-study hours: 112 h

Available for exchange students

Yes

Possible deviation from course syllabus/programme syllabus

ECV = Elective / Compulsory / Voluntary

Course offered for		Semester	Period	Timetable module	Language	Campus	ECV
6CDDD	Computer Science and Engineering, M Sc in Engineering	8 (Spring 2018)	1	2	English	Linköping, Valla	E
6CDDD	Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning)	8 (Spring 2018)	1	2	English	Linköping, Valla	E
6CDDD	Computer Science and Engineering, M Sc in Engineering (Programming and Algorithms)	8 (Spring 2018)	1	2	English	Linköping, Valla	E
6CMJU	Computer Science and Software Engineering, M Sc in Engineering	8 (Spring 2018)	1	2	English	Linköping, Valla	E
6CMJU	Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning)	8 (Spring 2018)	1	2	English	Linköping, Valla	E
6CMJU	Computer Science and Software Engineering, M Sc in Engineering (Programming and Algorithms Specialization)	8 (Spring 2018)	1	2	English	Linköping, Valla	E
6MDAV	Computer Science, Master's Programme	2 (Spring 2018)	1	2	English	Linköping, Valla	E
6MICS	Computer Science, Master's Programme	2 (Spring 2018)	1	2	English	Linköping, Valla	E
6MICS	Computer Science, Master's Programme (AI and Data Mining)	2 (Spring 2018)	1	2	English	Linköping, Valla	E
6CITE	Information Technology, M Sc in Engineering	8 (Spring 2018)	1	2	English	Linköping, Valla	E
6CITE	Information Technology, M Sc in Engineering (AI and Machine Learning)	8 (Spring 2018)	1	2	English	Linköping, Valla	E
6CITE	Information Technology, M Sc in Engineering (Programming and Algorithms)	8 (Spring 2018)	1	2	English	Linköping, Valla	E

Main field of study

Information Technology, Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course offered for

Computer Science, Master's Programme
Computer Science and Engineering, M Sc in Engineering
Information Technology, M Sc in Engineering
Computer Science and Software Engineering, M Sc in Engineering

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 supervision 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, 5

Other information

Supplementary courses

Text Mining

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Jalal Maleki

Examiner

Marco Kuhlmann

Course website and other links

http://www.ida.liu.se/~TDDE09/

Education components

Preliminary scheduled hours: 48 h
Recommended self-study hours: 112 h

Course 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.

Student rights and obligations (login required)
Linköping University common rules and regulations (in Swedish)

Compendia

Lecture notes provided by the department.

Download

Download course literature list as PDF

Note: The course matrix might contain more information in Swedish.

I = Introduce, U = Teach, A = Utilize

		I	U	A	Modules
1. DISCIPLINARY KNOWLEDGE AND REASONING
	1.1 Knowledge of underlying mathematics and science (G1X level)			X
	1.2 Fundamental engineering knowledge (G1X level)		X	X	LAB1
	1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)		X		TEN1
	1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level)
	1.5 Insight into current research and development work
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
	2.1 Analytical reasoning and problem solving		X		LAB1 UPG1
	2.2 Experimentation, investigation, and knowledge discovery		X		LAB1 UPG1
	2.3 System thinking
	2.4 Attitudes, thought, and learning		X		UPG1
	2.5 Ethics, equity, and other responsibilities
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
	3.1 Teamwork			X	LAB1 UPG1
	3.2 Communications			X	UPG1
	3.3 Communication in foreign languages			X
4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT
	4.1 External, societal, and environmental context	X
	4.2 Enterprise and business context
	4.3 Conceiving, system engineering and management
	4.4 Designing
	4.5 Implementing
	4.6 Operating
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
	5.2 Economic conditions for knowledge development
	5.3 Identification of needs, structuring and planning of research or development projects
	5.4 Execution of research or development projects
	5.5 Presentation and evaluation of research or development projects

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