Statistical natural language processing
Form of learning:
Target group and prerequisites
Many core applications in modern information society such as search engines, social media, machine translation, speech processing and text mining for business intelligence apply statistical and adaptive methods. This course provides information on these methods and teaches basic skills on how they are applied on natural language data. Each topic is handled by a high level expert in the area.
After attending the course, the student
- knows how statistical and adaptive methods are used in information retrieval, machine translation, text mining, speech processing and related areas to process natural language contents.
- can apply the basic methods and techniques used for statistical natural language modeling including, for instance, clustering, classification, Hidden markov models and Bayesian models.
- C. Manning, H. Schütze, 1999. Foundations of Statistical Natural Language Processing. The MIT Press
- Lecture notes
Lectures on Tuesdays at 12-14, exercises on Thursdays at 14-16.
The recorded lectures will be available on the course platform. Exercise sessions will be held on campus, but they are not mandatory.
Exercises need to be submitted around 2 weeks after the corresponding lectures. Parts of the project work need to be submitted every few weeks. The final schedule will be decided at the beginning of the course.
Examination and exercise work.
Application deadline: 3.1.2022.