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 sentence embeddings, and studies the application of these to document classification, sentiment analysis, information retrieval, computational social science, and detection of societal biases.

Covered topics:

  • Text processing
  • Sentiment analysis with machine learning
  • Language modeling with neural networks
  • Word embedding models (word2vec, GloVe, etc.)
  • Learning compositional embeddings
  • Contextualized word embeddings (practical walkthrough) 
  • Neural Information Retrieval
  • Footprint of societal phenomena and biases in NLP

Information for the current semester (if available):

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Prerequisites:

  • Python programming skill is mandatory.
  • Having prior knowledge on machine learning and neural networks is suggested but not mandatory – the course provides a brief overview on these topics.

Follow-up course:

Special Topics: Natural Language Processing with Deep Learning LVA Nr. 344.063, KV, 4.5 ECTS

Teaching Materials (Winter 2020):

Lecture 1: Principles of Text Processing slides

Lecture 2: Sentiment Analysis with Machine Learning slides

Lecture 3: Word Embedding with Matrix Factorization slides

Lecture 4: Neural Networks – a Walkthrough slides

Lecture 5: Language Modeling and Neural Word Embedding slides

Lecture 6: Information Retrieval with Neural Networks slides

Lecture 7: Footprint of Societal Biases in NLP slides

Lecture 8: Compositional Representations slides