Natural Language Processing, 6 credits

Språkteknologi, 6 hp

TDDE09

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
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Period Timetable module Language Campus ECV
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering 8 (Spring 2019) 1 2 English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering 8 (Spring 2019) 1 2 English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering 8 (Spring 2019) 1 2 English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering 8 (Spring 2019) 1 2 English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering 8 (Spring 2019) 1 2 English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering (Signal and Image Processing) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering (Signal and Image Processing) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering (Signal and Image Processing) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering (Signal and Image Processing) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, M Sc in Engineering (Signal and Image Processing) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CYYY Applied Physics and Electrical Engineering, M Sc in Engineering 8 (Spring 2019) 1 2 English Linköping, Valla E
6CYYY Applied Physics and Electrical Engineering, M Sc in Engineering (Signal and Image Processing) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CDDD Computer Science and Engineering, M Sc in Engineering 8 (Spring 2019) 1 2 English Linköping, Valla E
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CDDD Computer Science and Engineering, M Sc in Engineering (Programming and Algorithms) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CDDD Computer Science and Engineering, M Sc in Engineering (Signal and Image Processing) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CMJU Computer Science and Software Engineering, M Sc in Engineering 8 (Spring 2019) 1 2 English Linköping, Valla E
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CMJU Computer Science and Software Engineering, M Sc in Engineering (Programming and Algorithms Specialization) 8 (Spring 2019) 1 2 English Linköping, Valla E
6MDAV Computer Science, Master's Programme 2 (Spring 2019) 1 2 English Linköping, Valla E
6MICS Computer Science, Master's Programme 2 (Spring 2019) 1 2 English Linköping, Valla E
6MICS Computer Science, Master's Programme (AI and Data Mining) 2 (Spring 2019) 1 2 English Linköping, Valla E
6CITE Information Technology, M Sc in Engineering 8 (Spring 2019) 1 2 English Linköping, Valla E
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CITE Information Technology, M Sc in Engineering (Programming and Algorithms) 8 (Spring 2019) 1 2 English Linköping, Valla E
6CITE Information Technology, M Sc in Engineering (Signal and Image Processing) 8 (Spring 2019) 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

  • Master's Programme in Computer Science
  • Computer Science and Engineering, M Sc in Engineering
  • Information Technology, M Sc in Engineering
  • Computer Science and Software Engineering, M Sc in Engineering
  • Applied Physics and Electrical Engineering, M Sc in Engineering
  • Applied Physics and Electrical Engineering - International, 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:

  1. explain state-of-the-art NLP algorithms and analyse them theoretically
  2. implement NLP algorithms and apply them to practical problems
  3. design and carry out evaluations of NLP components and systems
  4. 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

KTR1Optional written tests0 creditsU, G
UPG1Project assignments2 creditsU, 3, 4, 5
LAB1Practical assignments2 creditsU, 3, 4, 5
TEN1Written examination2 creditsU, 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.

Course syllabus

A syllabus has been established for each course. The syllabus specifies the aim and contents of the course, and the prior knowledge that a student must have in order to be able to benefit from the course.

Timetabling

Courses are timetabled after a decision has been made for this course concerning its assignment to a timetable module. A central timetable is not drawn up for courses with fewer than five participants. Most project courses do not have a central timetable.

Interrupting a course

The vice-chancellor’s decision concerning regulations for registration, deregistration and reporting results (Dnr LiU-2015-01241) states that interruptions in study are to be recorded in Ladok. Thus, all students who do not participate in a course for which they have registered must record the interruption, such that the registration on the course can be removed. Deregistration from a course is carried out using a web-based form: www.lith.liu.se/for-studenter/kurskomplettering?l=sv. 

Cancelled courses

Courses with few participants (fewer than 10) may be cancelled or organised in a manner that differs from that stated in the course syllabus. The board of studies is to deliberate and decide whether a course is to be cancelled or changed from the course syllabus. 

Regulations relating to examinations and examiners 

Details are given in a decision in the university’s rule book: http://styrdokument.liu.se/Regelsamling/VisaBeslut/622678.

Forms of examination

Examination

Written and oral examinations are held at least three times a year: once immediately after the end of the course, once in August, and once (usually) in one of the re-examination periods. Examinations held at other times are to follow a decision of the board of studies.

Principles for examination scheduling for courses that follow the study periods:

  • courses given in VT1 are examined for the first time in March, with re-examination in June and August
  • courses given in VT2 are examined for the first time in May, with re-examination in August and October
  • courses given in HT1 are examined for the first time in October, with re-examination in January and August
  • courses given in HT2 are examined for the first time in January, with re-examination at Easter and in August.

The examination schedule is based on the structure of timetable modules, but there may be deviations from this, mainly in the case of courses that are studied and examined for several programmes and in lower grades (i.e. 1 and 2). 

  • Examinations for courses that the board of studies has decided are to be held in alternate years are held only three times during the year in which the course is given.
  • Examinations for courses that are cancelled or rescheduled such that they are not given in one or several years are held three times during the year that immediately follows the course, with examination scheduling that corresponds to the scheduling that was in force before the course was cancelled or rescheduled.
  • If teaching is no longer given for a course, three examination occurrences are held during the immediately subsequent year, while examinations are at the same time held for any replacement course that is given, or alternatively in association with other re-examination opportunities. Furthermore, an examination is held on one further occasion during the next subsequent year, unless the board of studies determines otherwise.
  • If a course is given during several periods of the year (for programmes, or on different occasions for different programmes) the board or boards of studies determine together the scheduling and frequency of re-examination occasions.

Registration for examination

In order to take an examination, a student must register in advance at the Student Portal during the registration period, which opens 30 days before the date of the examination and closes 10 days before it. Candidates are informed of the location of the examination by email, four days in advance. Students who have not registered for an examination run the risk of being refused admittance to the examination, if space is not available.

Symbols used in the examination registration system:

  ** denotes that the examination is being given for the penultimate time.

  * denotes that the examination is being given for the last time.

Code of conduct for students during examinations

Details are given in a decision in the university’s rule book: http://styrdokument.liu.se/Regelsamling/VisaBeslut/622682.

Retakes for higher grade

Students at the Institute of Technology at LiU have the right to retake written examinations and computer-based examinations in an attempt to achieve a higher grade. This is valid for all examination components with code “TEN” and "DAT". The same right may not be exercised for other examination components, unless otherwise specified in the course syllabus.

Retakes of other forms of examination

Regulations concerning retakes of other forms of examination than written examinations and computer-based examinations are given in the LiU regulations for examinations and examiners, http://styrdokument.liu.se/Regelsamling/VisaBeslut/622678.

Plagiarism

For examinations that involve the writing of reports, in cases in which it can be assumed that the student has had access to other sources (such as during project work, writing essays, etc.), the material submitted must be prepared in accordance with principles for acceptable practice when referring to sources (references or quotations for which the source is specified) when the text, images, ideas, data, etc. of other people are used. It is also to be made clear whether the author has reused his or her own text, images, ideas, data, etc. from previous examinations.

A failure to specify such sources may be regarded as attempted deception during examination.

Attempts to cheat

In the event of a suspected attempt by a student to cheat during an examination, or when study performance is to be assessed as specified in Chapter 10 of the Higher Education Ordinance, the examiner is to report this to the disciplinary board of the university. Possible consequences for the student are suspension from study and a formal warning. More information is available at https://www.student.liu.se/studenttjanster/lagar-regler-rattigheter?l=sv.

Grades

The grades that are preferably to be used are Fail (U), Pass (3), Pass not without distinction (4) and Pass with distinction (5). Courses under the auspices of the faculty board of the Faculty of Science and Engineering (Institute of Technology) are to be given special attention in this regard.

  1. Grades U, 3, 4, 5 are to be awarded for courses that have written examinations.
  2. Grades Fail (U) and Pass (G) may be awarded for courses with a large degree of practical components such as laboratory work, project work and group work.

Examination components

  1. Grades U, 3, 4, 5 are to be awarded for written examinations (TEN).
  2. Grades Fail (U) and Pass (G) are to be used for undergraduate projects and other independent work.
  3. Examination components for which the grades Fail (U) and Pass (G) may be awarded are laboratory work (LAB), project work (PRA), preparatory written examination (KTR), oral examination (MUN), computer-based examination (DAT), home assignment (HEM), and assignment (UPG).
  4. Students receive grades either Fail (U) or Pass (G) for other examination components in which the examination criteria are satisfied principally through active attendance such as other examination (ANN), tutorial group (BAS) or examination item (MOM).

The examination results for a student are reported at the relevant department.

Regulations (apply to LiU in its entirety)

The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.

LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva. 

Compendia

Lecture notes provided by the department.

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

I = Introduce, U = Teach, A = Utilize
I U A Modules Comment
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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