The course belongs to the MA Programme Linguistic Diversity in the Digital Age
- study track: language technology
- modules: Studies in Language Technology (LDA-T3100), Essentials in Language Technology (LDA-TA500), Comprehensive specialization in Language Technology (LDA-TB500)
This is an optional course.
The course is available to students from other study tracks and degree programmes.
Students are advised to take this course in year 2 (semester 3). The course is offered every year during the autumn term in period I or II.
After successfully completing the course, students will be able to
- explain models and algorithms used in selected NLP applications
- describe properties of local prediction models and structural prediction models and methods that can be used to train them
- explain the differences between generative and discriminative models and between supervised and unsupervised learning
- describe the main components of a selected NLP application, for example a machine translation system
- train and evaluate a practical NLP model, for example a statistical or neural machine translation model
- present experimental results in a sound and scientific manner.
- Contact teaching (lectures, tutorials, seminars)
- Self studies and group work
- one or more of the following: flipped classroom activities, overview paper, written exam (part I)
- project report and presentation with peer-review (part II)
- Programming for linguists or equivalent (BA level)
- Mathematics for linguists or equivalent (BA level)
- Machine learning for linguists or equivalent (BA level)
|Recommended optional studies
- Linguistics in the digital age
- Computational syntax
- Computational semantics
- Computational morphology
Part I: Models and algorithms used in common NLP applications with focus on a selected application, for example machine translation
- common models and their components (with focus on the selected application)
- algorithms for training, tuning and evaluating NLP applications (with focus on the selected application).
Part II: Practical project work
- data collection
- training and tuning models
- evaluating and analysing.
|Study materials and literature
The literature depends on the selected application, for example Philipp Koehn: "Statistical Machine Translation" (Cambridge University Press) in case of machine translation.
Other recommended literature: Manning and Schütze: Foundations of Statistical Natural Language Processing (MIT Press).
Additional web material and literature distributed on the course.
|Activities and teaching methods in support of learning
- Lectures and tutorials
- Interactive sessions, for example flipped classroom activities
- Problem-based collaborative project work
- Seminars with peer-review
- Activities documented in Moodle
|Assessment practices and criteria
- Classroom activities (presentations and discussions)
- Written exam or term paper
- Individual and collaborative work
- Project report
- Seminar presentation and peer review.