The core lecture for Deep Learning (DL) for Natural Language Processing (NLP).
Topics will include DL foundations specific to NLP (such as word embeddings, recurrent networks, convolutional networks, or transformer architectures) and their application to various NLP tasks (such as Part-of-Speech Tagging, Argument Mining, Sentiment Analysis, Dependency Parsing, among others.
- Dozent*in: Ivan Habernal