Natural Language Processing, 6 credits (TDDE09)

Språkteknologi, 6 hp

Main field of study

Information Technology Computer Science and Engineering Computer Science

Level

Second cycle

Course type

Programme course

Examiner

Marco Kuhlmann

Director of studies or equivalent

Jalal Maleki
Course offered for Semester Period Timetable module Language Campus VOF
6CDDD Computer Science and Engineering, M Sc in Engineering 8 (Spring 2017) 1 2 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (Programming and Algorithms) 8 (Spring 2017) 1 2 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2017) 1 2 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering 8 (Spring 2017) 1 2 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering (Programming and Algorithms Specialization) 8 (Spring 2017) 1 2 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2017) 1 2 English Linköping v
6CITE Information Technology, M Sc in Engineering 8 (Spring 2017) 1 2 English Linköping v
6CITE Information Technology, M Sc in Engineering (Programming and Algorithms) 8 (Spring 2017) 1 2 English Linköping v
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2017) 1 2 English Linköping v
6MDAV Computer Science, Master's programme 2 (Spring 2017) 1 2 English Linköping v
6MICS Computer Science, Master's programme 2 (Spring 2017) 1 2 English Linköping v

Main field of study

Information Technology, Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course 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

TEN1Written examinationU, 3, 4, 52 credits
LAB1Practical assignmentsU, 3, 4, 52 credits
UPG1Project assignmentsU, 3, 4, 52 credits
KTR1Optional written testsU, G0 credits
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

Education components

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

Course literature

Additional literature

Compendiums

Lecture notes provided by the department.

Additional literature

Compendia

Lecture notes provided by the department.

TEN1 Written examination U, 3, 4, 5 2 credits
LAB1 Practical assignments U, 3, 4, 5 2 credits
UPG1 Project assignments U, 3, 4, 5 2 credits
KTR1 Optional written tests U, G 0 credits
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.

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Page responsible: Study information, bilda@uf.liu.se