• Syllabus
• Readings/Bibliography
• Assessment
• Projects

Syllabus

• Part I: Foundations
• Introduction
• Natural Language Processing - Problems and perspectives
• Introduction/Recall to/of probability calculus
• N-gram Language Models
• Introduction to Deep Learning
• The evaluation of NLP applications


• Corpora and Annotations
• Vector Representations (a.k.a. Embeddings)
• - Word, Sentence and Document embeddings.
• - Transfer Learning

• 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
• Formal languages and Natural languages. Natural language complexity
• Phrase structure grammars, Dependency Grammars, Treebanks
• Modern formalisms for parsing natural languages
• Natural language Dependency Parsing
• Computational Semantics
• Lexical semantics resources: WordNet and FrameNet
• Word Sense Disambiguation

• Part III: Applications and Large Language Models:
• Solving Downstream Tasks fine-tuning Transformers
  
• Generative Transformers
  
• Prompting Large Language Models
  
• Agents