In the Language Engineering course you will learn grammatical, statistical, and neural methods for analysis and generation of (written) human languages.
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Content and learning outcomes
- Levels for the analysis of written human language: Morphology, syntax, semantics and pragmatics
- Grammatical, statistical and neural methods for linguistic analysis and generation.
Intended learning outcomes
After passing the course, the student shall be able to
- explain and use concepts at the basic levels of linguistics: morphology, syntax, semantics, discourse and pragmatics,
- explain, implement and use standard methods of language engineering that are based on rules, statistics and machine learning,
- use basic language engineering tools, corpora and software libraries
- design and carry out simple evaluations of some language engineering system, and interpret the results,
in order to be able to
- work for language technology companies
- carry out a master's degree project in computer science with a specialisation in language engineering
- be an important link between systems designers, programmers, and interaction designers in industry as well as in research projects.
Literature and preparations
Completed courses in basic computer science equivalent to DD1338/DD1320/DD1321/DD1325/DD1327/ID1020/ID1021; and probability theory, equivalent to course SF1912/SF1914-SF1924.
Active participation in a course offering where the final examination is not yet reported in LADOK is considered equivalent to completion of the course.
Registering for a course is counted as active participation. The term 'final examination' encompasses both the regular examination and the first re-examination.
Knowledge of formal languages corresponding to DD2481 Principles of Programming Languages, DD2372/DD2373 Automata and Languages or DD1360/DD1361/DD1362 Programming paradigms is useful but not necessary.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- LAB1 - Computer assignments, 6.0 credits, grading scale: A, B, C, D, E, FX, F
- PRO1 - Project assignment, 1.5 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.Course web DD2417
Main field of study
Please discuss with the instructor.
DD2477 Search Engines and Information Retrieval Systems, and DT2112 Speech technology are possible follow-ups.
In this course, the EECS code of honor applies, see:
DD2417 is overlapping DD2418 and partly DD1418 and therefore cannot be combined with these courses.