LTAT.01.001 Natural language processing
This course aims to provide an overview of the main tasks in the field of natural language processing and to introduce the contemporary methods to address them. We will focus on methods that operate with feature vectors such as log-linear models and deep neural networks. We will look how these models can be applied to various natural language processing tasks such as part-of-speech tagging, syntactic parsing, named entity recognition, sentiment analysis etc.
- Lectures: Thursdays at 16:00, Ülikooli 17, room 218
- Practicals: Fridays at 10:00, Ülikooli 17, room 220 (bring your own computer)
- 4 practical homeworks (10% each)
- seminar presentation on a research article (10%)
- final project (50%)
This course assumes knowledge from various areas. In õis, the recommended prerequisite courses are Language Technology (MTAT.06.045) and Artificial Intelligence I (MTAT.06.008). In practice, we also assume the basic knowledge of machine learning, optimisation, higher math (calculus, linear algebra, probabilities) and computer programming (python). If you lack some of the required knowledge then it is your responsibility to acquire it at the level necessary for advancing on this course. We can help to find suitable materials for obtaining the necessary background.