- Basics of text processing: segmentation, tokenization, decompounding, stemming, lemmatization; regular expressions
- N-gram language modeling, simple classification tasks in NLP
- Part-of-speech tagging, named entity recognition, and shallow parsing with Hidden Markov Models
- Syntactic representations and syntactic parsing
- Basics of natural language semantics
- Neural network basics. Feed forward networks and recurrent neural networks
- Sequence modeling and sequence-to-sequence models.
- Neural language modeling. Word vectors and contextualized language models.
- Information extraction tasks: entity recognition, relation extraction, knowledge base population
- Information extraction applications: summarization, question answering, chatbots
The link to the online lectures is in TUWEL.
Workload for Students (in hours):
- Lectures: 24
- Homework (2 Exercises): 16
- Final Project: 35
<p>2 assignments, 1 term project</p>