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Institute of Computational Perception
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Natural Language Processing

The course provides in depth knowledge on the essential elements of Natural Language Processing (NLP), particularly based on machine learning and neural networks. The lectures cover topics such as text processing, language modeling, word embeddings, and sequence embeddings, and studies the application of these to document classification, sentiment analysis, information retrieval, computational social science, and detection of societal biases.

Covered topics in lectures:

  • Text processing
  • Sentiment analysis with machine learning
  • Language modeling
  • Count-based and neural word embedding models (word2vec, GloVe, etc.)
  • Introduction to large language models (BERT) 
  • Neural Information Retrieval
  • Footprint of societal phenomena and biases in NLP

Information for the current semester (if available):

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Prerequisites:

  • For the lectures (VL), knowing about machine learning and neural networks methods is suggested, while the course also briefly covers these topics.
  • For the practical part (UE), good Python programming skill is required.

Teaching materials of the latest course (Winter Semester 2021/22):

Follow-on course: Special Topics: Natural Language Processing with Deep Learning (KV)