Content Server example project with Natural Language Processing NLP


Starting Page

Artificial Intelligence and Content Server

Example Linguistic Features of NLP

ContentServer Example Project with NLP

NLP application: AUTOCATEGORIZER – KI based categories

this is an Content Server example project with an autocategorizer. It follows these guidelines.

  • Definition of categories. Which attributes and categories should become components?
  • Avoiding prejudice. Do the new categories imply prejudice and do these have any impact?
  • What is good evaluation of a test run?
  • Choosing the Categorizer, Neural Network or Simple Algorithm? For simple algorithms, several should be selected and tested.
  • Pre-trained as open source or the categorizer still needs to be trained?
  • Selection of the training data set and the test data set. Although “the more the better”, about 10% of the data set for testing and training is enough to get started. It is important to consider the point of prejudice.
  • Data transfer.
  • Possibly pre-process data by using natural language processing (NLP) tools
  • Training and testing each selected algorithm. Assessing accuracy through framework evaluation tools.
  • Selection of the algorithm or neural network with the most favorable ratings from the test runs
  • Production run: New business workspace or document is transferred to Python. The categorizer categorizes the document/business workspace and enters the new categories/attributes in the content server for the documents/business workspaces.
  • Logs are generated as needed
  • A trained categorizer can be saved, retrained and used over and over again.