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. The course will cover tasks such as language modeling and word/sentence representations, text classification, sequence tagging for finding parts of speech or morphological features, information extraction such as named entity recognition, finding the important structural parts of a sentence as well as some higher level tasks such as machine translation.
During recent years, the NLP field has more and more started to use deep neural models. Thus, in this course we will look at various deep neural models that are nowadays commonly used for NLP: recurrent networks for modeling sequential data, convolutional networks for text classification, static and contextual word embeddings, attention mechanism for finding alignment between different inputs or inputs and outputs.
Course info
- Lectures: Tuesdays at 12:15
- Delta building (Narva mnt 18), room 1019
- Labs: Tuesdays at 16:15
- Delta building (Narva mnt 18), room 1019
- Lecturer: Kairit Sirts (kairit.sirts@ut.ee)
- TA: Kirill Milintsevich (kirill.milintsevich@unicaen.fr)
The course will take place face-to-face - so you are all welcome to attend in person. All lectures and practice sessions will be recorded and made available via moodle. Maybe we'll also experiment with hybrid.
Assessment
| Type | Points | Comment |
|---|---|---|
| Reading tests | 40 points | 10 reading tests, max 4 points each |
| Practical homeworks | 40 points | 10 practical homeworks, max 4 points each |
| Project | 25 points | |
| Total | 105 points |
Deadlines for homeworks and reading tests are on Mondays at 23:59.
In order to pass the course, you have to:
- Obtain half of the points from each assessment type:
- 20 from reading tests,
- 20 from homeworks,
- 11 from the project.
- Submit all project milestones.
Prerequisites
This course assumes knowledge from various areas. In Study Information System, the required prerequisite course is Machine Learning (MTAT.03.227) and 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 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.