Natural language processing (NLP) is the study of computational models of human language, with the ultimate goal of enabling
machines to understand and use human language. Due to the
presumed connection between human intelligence and human language
use, NLP is a core field within artificial intelligence (AI) and
currently the focus of significant scientific research, technology
development and public interest. The advent of deep learning
has seen progress in NLP accelerate over the past years, with numerous major scientific breakthroughs.
This class provides an introductory overview of NLP. We will introduce a range of different NLP tasks such as information extraction, document classification, sequence labeling, machine translation and question-answering, and use these tasks to discuss common challenges and solutions in NLP. This will include methods to learn word and sentence representations, as well as neural architectures for NLP. Since deep learning is now crucial to NLP, the course will include an introduction into the deep learning framework PyTorch. Students will put the covered topics into practice in weekly implementation assignments in Python.
IMPORTANT:
- Students must be enrolled in the practical exercise ("Übung") and successfully participate in exercises and the final exam to pass the course.
- The course starts April 19th, 2024!
- Kursverantwortliche/r: Alan Akbik
- Kursverantwortliche/r: Ansar Aynetdinov
- Kursverantwortliche/r: Conrad Dobberstein
- Kursverantwortliche/r: Lukas Garbaciauskas
- Kursverantwortliche/r: Jonas Max Golde
- Kursverantwortliche/r: Patrick Haller
- Kursverantwortliche/r: Elena Merdjanovska
- Kursverantwortliche/r: Max Ploner
- Kursverantwortliche/r: Susanna Dorothea Ruecker