Oral examination (written exam if there are a large number of participants), exercise points can be included as a grade bonus in the assessment of the exam

Natural Language Processing (NLP) is a sub-field of artificial intelligence that aims at understanding and automatic generation of texts for various applications, such as document classification, sentiment analysis, text summarization, speech recognition, etc. This course covers NLP topics including n-gram models, word embeddings, bag of word representations for document classification, classifiers, tokenization, part of speech tagging, matrix factorization and topic modeling, deep learning for language processing, transformers, language models and text generation, and finally applications such as document summarization, machine translation, or question answering.

Course participants will learn from basic to advanced topics in NLP. They will learn to analyze datasets of textual documents and uncover their various patterns, build text classification models, text generation models and a few modern applications of NLP. The course exercises will give students an opportunity to solve real-world NLP problems independently.

Verwendete Programmiersprache: Python