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Three Natural-Language Processing Course Modules in Oslo

If you live in Oslo these and have an interest in language technology these course modules may be of interest

Oslo is my hometown, so I decided to have a quick look at what kind of modules that are being offered within natural-language processing in Oslo the year 2020. Within this there might be more, however I found three course models mainly based within the institute of informatics at the University of Oslo:

  • IN4080 — Natural Language Processing.
  • IN5550 — Neural Methods in Natural Language Processing & IN9550 — Neural Methods in Natural Language Processing.

IN4080 — Natural Language Processing

Course content

Mean to be an overview course for NLP, emphasis on probabilistic and machine learning (ML) techniques. Mainly based on ML applied to language data. Additionally it shows steps in a typical NLP system like tagging, parsing, named entity recognition, relation extraction will be considered. This course is meant to prepare the students for a master’s thesis in Informatics: Language Technology.

Learning outcome

“After completing IN4080:

  • You are familiar with the central research methods and technologies used in NLP
  • You can carry out NLP experiments involving machine learning and evaluate the results
  • You are familiar with the steps in a typical NLP system and you are able to select and apply tools for these steps
  • You are familiar with the concept of probability and how it is applied in NLP methods and in evaluation”

IN5550 — Neural Methods in Natural Language Processing

Course content

Advanced techniques in NLP with recent and current research literature. Machine learning and specifically ‘deep’ neural network approaches to the automated analysis of natural language text. Classification using Convolutional Neural Networks, and applications of various types of Recurrent Neural Networks to sequence labeling and the analysis of grammatical or semantic structure.

Learning outcome

“Upon completion of this course you:

  • understand the basics of various types of neural networks and their applications to natural language processing;
  • can apply off-the-shelf NLP tools in meaningful ways to the data preparation for representation learning;
  • have basic knowledge of the concepts of transfer and multi-task learning in application to natural language problems;
  • can design, excecute, analyze, and summarize large-scale experiments in common neural network toolkits;
  • know how to assess the benefits and challenges of neural learning in contrast to other common approaches in NLP;
  • are able to identify and critically read relevant NLP research literature.”

Activities and lectures

Within these courses you can find a list of activities for the specific lectures with links to ‘screen’ and ‘print’.

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Written by

AI Policy and Ethics at www.nora.ai. Student at University of Copenhagen MSc in Social Data Science. All views are my own. twitter.com/AlexMoltzau

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