Theory:
The historical development and bases of language engineering, morphology, syntax, semantics, vector space models, evaluation methods, machine learning, information theory and Markov models.
Methods::
Morphological analysis, generation and language statistics and corpus processing, parsing, generation, part-of-speech tagging, named entity recognition, probabilistic parsing and statistical lexical semantics.
Application areas:
Spelling and grammar checking, information retrieval, word prediction for smart text entry, text clustering and text categorization, computer-aided language learning, dialogue systems, speech technology and machine translation.
After passing the course, the student shall be able to
- explain and use basic concepts in linguistics, language engineering and machine learning
- apply language engineering concepts, methods and tools to build language engineering systems as well as be able to explain the structure of such systems
- implement standard methods in language engineering
- design and carry out simple evaluations of a language engineering system as well as interpret the results,
- independently be able to solve a well delimited practical language engineering problem
in order to be able to
- work with a bachelor's degree project with a focus on language engineering or machine learning,
- be an important link between systems designers, programmers, and interaction designers in industry as well as in research projects.
