WordWolf is an iOS framework built on top of Apple's Natural Language framework, providing an extra layer of abstraction for performing more specialized natural language processing tasks than what is available from Apple's framework.
It allows users to obtain a larger amount of linguistic feature data , which in turn can be used for generating training and test set data that can be used in conjunction with CreateML or Turi Create.
If you have an interest in computational linguistics and are interested in helping to develop this framework, please contact me.
The framework defines custom wrappers for the NLTokenizer and NLTagger classes provided by Apple, and uses custom models based on a more theoretical computational linguistic perspective to extend the functionality already provided by Apple's classes. Since some of this linguistic feature data may not be so intuitive to those not trained in computational linguistics, it is hoped that ordinary developers might gain further insights into the potential for linguistic feature data in inspiring more detailed and varied approaches to machine learning.
I've also provided links to a series of tutorials that can introduce basic Swift and iOS programming in the context of Natural Langauge processing. In addition, many common data structures (such as linked lists, graphs, and trees) are introduced in the context of linguistic data.