Interactive Syllabus

QUESTIONS AND TASKS:

• Natural language (pre)processing techniques and their relevance for building machine learning models applicable to text

• Bag of words representation of text - pros and cons

• Text representations. When the ordering of words (does not) matter-s. Mining web
  

• Main text mining tasks

• ML for disambiguation

• CNN for NLP

• Distributional hypothesis - historical context, linguistic motivations and practical implementations

• Distributional vs. formal semantics

• Latent semantic analysis - basic principles, pros and cons

• Word embedings - comparison of selected popular approaches

• Basic principles of language models and their training process

• Techniques for data augmentation

• NN for machine translation

• Text clustering

• Recurrent NN for NLP. Describe one task.

• Outliers in text data.

• Methods for outlier detection in text

• Why we need LSTM. Describe a task where LSTM areuseful

• What are transformers? How do they differ from "vanilla" recurrent neural networks?

• Describe the basic intuition behind gradient descent and back-propagation

• ILP. why we need it. Describe a task where ILP is useful

• Key words, keyness. How to compute.

• Relational learning  (ILP) for key words (key phrases) detection

• Text sumarization.  Two main approaches.

• Extractive sumarization. ROUGE-n.

• Sentiment analysis - the definition of the field, justification of its practical relevance and main challenges

• Detailed overview of a selected lexicon-based approach to sentiment analysis

• Detailed overview of a selected classical machine learning approach to sentiment analysis

• Detailed overview of a selected deep learning approach to sentiment analysis

• Comparison of lexicon-based, classical machine learning and deep learning approaches to sentiment analysis

• Basic principles of knowledge representation

• Ontologies vs. knowledge graphs - pros and cons of each approach to knowledge representation

• The stack of typical tasks in ontology learning

• Main challenges and open problems of ontology learning

• Techniques used for term extraction, synonym discovery and concept formation

• Techniques used for taxonomy extraction

• Techniques used for relation, rule and axiom extraction

• Overview of a selected deep learning approach to knowledge extraction