Text Mining for AI

Course Objective
Knowledge and understanding: at the end of the course, students will be familiar with basic knowledge of some of the core aspects of Natural Language Processing, Linguistics and Text Mining: Rule-based systems, machine learning, text classification, sentiment extraction, entity recognition and topic modeling of text.

Applying knowledge and understanding: students will be able to implement NLP processing systems and modules and evaluate these.

Making judgements: students will have a basic understanding of the ethical and societal implications of the developments in NLP.

Communication skills: students will be able to write a scientific reports about an original research question in a group of students.

Learning skills: students will be trained in acquiring a set of complex NLP and text mining topics in a restricted period of time, come up with an original research question and perform the necessary (empirical)
research.

Basic concepts from Linguistics and foundational concepts from Natural Language Processing. Skills to use, apply and critically assess text mining techniques. Adapt and build text mining techniques to specific
target domains and applications.

Course Content
Basic concepts from Linguistics and foundational concepts from Natural Language Processing. Skills to use, apply and critically assess text mining techniques. Adapt and build text mining techniques to specific
target domains and applications.

Additional Information Teaching Methods
Theoretical lectures and working group sessions

Method of Assessment
Multiple choice exam on theory 50% and the group project report 50%.

Recommended background knowledge
Programming in Python, using Github