- No Elective choice
- No Contractonderwijs
- No Exchange
- No Study Abroad
- No Evening course
- No A la Carte
- No Honours Class
Bachelor Computer Science.
Text mining, also known as 'knowledge discovery from text', is an ICT research and development field that has gained increasing focus in the last decade, attracting researchers from data science, computational linguistics, and machine learning. Example key applications text categorization, information extraction, social media mining and automatic summarization. This course gives an overview of the field from both a theoretical angle (underlying models) and a practical angle (applications). In addition to the lectures, the students work on practical assignments.
After successful completion of this course, students have an understanding, both at the conceptual and the technical level, of the application of natural language processing (NLP) in the text mining area. Students can build models for a text mining task using machine learning algorithms and language data, and they can evaluate and report on the developed models and modules. Also, students understand, from a theoretical perspective, which tools are applicable in which situations, and which real-world challenges prevent the application of certain techniques (such as language variation and noise due to document processing errors).
The most recent timetable can be found on the students' website.
(subject to changes)
Week 1. Introduction
Week 2. the NLP pipeline
Week 3. text categorization
Week 4. distributional semantics (word embeddings and topic modeling)
Week 5. information extraction
Week 6. sentiment analysis
Week 7. information retrieval & question answering
Week 8. authorship detection
Week 9. summarization
Week 10. biomedical text mining
Week 11. industrial text mining
Week 12. conclusions/future developments
Mode of instruction
- a written exam (60% of course grade)
- practical assignments (40% of course grade)
- four smaller assignments (5% each) during the course
- one more substantial assignment (20%) at the end of the course
The literature will be available on Blackboard week by week.
- You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and class numbers.
- Please also register for the course in Blackboard.
- Due to limited capacity, external students can only register after consultation with the programme coordinator/study adviser (mailto:firstname.lastname@example.org).
|Is part of||Programme type||Semester||Block|
|Computer Science: Computer Science and Advanced Data Analytics||Master||1|