Machine learning and natural language processing

3 Distributional semantics, LSA, word embeddings

Slidy k prednasce (veskere reference apod. inline):

Pa164 lecture 03

READINGS

Word embeddings in 2020. Review with code examples


QUESTIONS AND TASKS:

  • 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

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