Syllabus
- Part I: Foundations
- Introduction
- Natural Language Processing - Problems and perspectives
- Introduction/Recall to/of probability calculus
- Introduction to Deep Learning
- The evaluation of NLP applications
-
Corpora and Annotations
- Part II: Natural Language Processing
- Computational Phonetics and Speech Processing
- Speech samples: properties and acoustic measures
- Analysis in the frequency domain, Spectrograms
- Applications in the acoustic phonetic field.
- Speech recognition with Deep Neural Networks
- Tokenisation and Sentence splitting
- Computational Morphology
- Morphological operations
- Static lexica, Two-level morphology
- Computational Syntax
- Part-of-speech tagging
- Grammars for natural language
- Natural language Parsing
- Supplementary worksheet: formal grammars for NL
- Formal languages and Natural languages. Natural language complexity
- Phrase structure grammars, Dependency Grammars
- Treebanks
- Modern formalisms for parsing natural languages
- Computational Semantics
- Lexical semantics: WordNet and FrameNet
- Word Sense Disambiguation
- Distributional Semantics & Word-Space models
- Word/Sentence/Text embeddings
- Part
III: Applications and Extras:
- Solving Downstream Tasks fine-tuning Transformers
- Prompting Generative Large Language Models
- Text Generation & Prompt Engineering
- Agents