The content of the course may vary with the lecturer, but will focus on theories and methods for the development of linguistic models and applications. Examples of relevant topics are knowledge representation, semantic networks, pattern recognition, search strategies, rule-based systems, machine learning, neural networks, quantitative models, and evolution. Examples of language technology applications are interfaces between human and machine, machine translation, proofreading, information retrieval, and information technology aids for persons with disabilities.
This course is also suitable for students in computer science, information science and humanistic informatics.
The aim of the course is to familiarize the student with modeling methods from artificial intelligence and machine learning, and applications.
Knowledge
The candidate can ...
¿ state principles and methods for modeling of scientific and technological problems in natural language processing
Skills
The candidate can...
¿ build and test models for language processes and for applications based on linguistic data
General competence
The candidate can...
¿ critically evaluate the utility of models for insight into human language processing and for practical applications in society.