DH2418 Language Engineering 6.0 credits
This course has been cancelled.
The course in language technology treats different methods for analysis, generation, and filtering of human language especially text. Rule-based and statistical methods are used and studied in applications such as information retrieval, spelling- and grammar checking, and text summarization.
The course covers theory, methods, and application areas within language technology. The course requirements are an examination, laboratory assignments, and a home assignment.
Education cycleSecond cycle
Main field of studyComputer Science and Engineering
Information and Communication Technology
Grading scaleA, B, C, D, E, FX, F
Last planned examination: autumn 14.
At present this course is not scheduled to be offered.
Intended learning outcomes
The students should after the course have the knowledge to:
- Explain and use general concepts within the following levels of linguistics: morphology, syntax, semantics, discourse, and pragmatics.
- Use the knowledge about morphology, syntax, and lexical semantics in order to develop systems, and explain existing systems using these levels.
- Clarify the differences between analysis, generation, and filtering in text-based systems.
- Use general language technology tools and resources, such as part-of-speech taggers, chunkers, corpora, and lexica in order to build new applications.
- Explain and use standard methods based on rules, statistics, and machine learning.
- Apply methods based on finite automata/transducers, context-free grammars, word frequencies, n-grams, co-occurrence statistics, Markov models, and vector space models.
- Analyze and explain which problems within language technology that could be solved with usable results, and which could not be solved.
- Give details of how spelling- and grammar checkers, taggers based on machine learning, stemmers, and an algorithm for semantic content acquisition work.
- Design and carry out a simpler evaluation of a language technology system, and interpret the results.
- Independently solve a well-defined practical language technology problem, or analyze a problem theoretically.
To be able to:
- Work for a language technology company.
- Continue with studies in language technology.
- Work with a master’s project in computer science or human-computer interaction with a focus on language technology.
- Be an important link between systems designers, programmers, and interaction designers in industry as well as in research projects.
Course main content
The history and basics of language technology, morphology, syntax, and semantics, vector space models, evaluation methods, the principles and methods of terminology work, machine learning, information theory and Markov models, algorithms and data structures for efficient lexicon handling.
Morphological analysis and generation, statistical methods in corpus linguistics, parsing and generation, part-of-speech tagging, named entity recognition and probabilistic parsing, statistical lexical semantics.
Spelling- and grammar checking, information retrieval, word prediction for smart text entry, text clustering and text categorization, computer assisted language learning, dialogue systems, text summarization, speech technology, localization and internationalization.
Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology. Swedish B or equivalent and English A or equivalent.
One of the courses DD1320/DD1321 Applied Computer Science, DD1340 Introduction to Computer Science, DD1343 Computer Science and Numerical Methods, part 1, DD1344 Fundamentals of Computer Science, DD1346 Object-Oriented Program Construction plus SF1906 Mathematical Statistics or equivalent. Knowledge of formal languages corresponding to DD2488 Compiler Construction or DD1361 Programming paradigms is useful but not necessary.
Course literature will be announced at course home page not later than 4 weeks before the course starts. Previous year: Jurafsky & Martin, Speech and language processing and material produced at the department.
- INLA - Assignment, 1.5, grading scale: A, B, C, D, E, FX, F
- LAB2 - Laboratory Assignments, 1.5, grading scale: P, F
- TEN2 - Examination, 3.0, grading scale: A, B, C, D, E, FX, F
In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.
Requirements for final grade
Examination (TEN2; 3 university credits.).
Laboratory assignments (LAB2; 1,5 university credits.).
Home assignment (INLA; 1,5 university credits).
CSC/Media Technology and Interaction Design
Johan Boye, e-post: email@example.com
Johan Boye <firstname.lastname@example.org>
The course is replaced by DD2418 with the same name as this course.
Please discuss with the instructor.
DT2112 Speech technology is a possible follow-up.
Course syllabus valid from: Autumn 2009.
Examination information valid from: Autumn 2007.