Modulbeschreibung

The students

• learn the theory and application of DL models used in NLP.
• understand the key concepts of the DL approach to NLP.
• get to know a wide sample of possible application examples.
• learn how to approach their own NLP projects.
• get the skills in Python and Tensorflow to work on Dl-based NLP projects.

Einleitung:

Natural Language Processing (NLP) develops statistical techniques and algorithms to automatically process natural languages (such as English). It includes a number of AI areas, such as text understanding and summarization, machine translation, and sentiment analysis. This course introduces the foundations of technologies in NLP and their application to practical problems. It brings together the state-of-the-art research and practical techniques in NLP, providing students with the knowledge and capacity to conduct NLP research and to develop NLP projects.

Lerninhalte:

• Handling and pre-processing of textual and speech data (regex)
• Bag-of-word models
• Word embeddings / text representations
• Basics of neural network and deep learning for NLP
• Basics of recurrent neural networks
• Study and apply theory and skills to problems such as:
  o text classification
  o semantic analysis
  o Language and topic modelling
  o Part-of-speech tagging
  o Sentiment Analysis
  o Machine Translation
• Study and work on a specific NLP project