Hi @pkras!
Your approach sounds reasonable. If it's a new label scheme, definitely start off with some manual annotation and train your model on the new data as you intend to do. 
Now, ner.correct becomes handy if you already have a model the predicts something, and you just need to correct its predictions.
- You can use the
--updateparameter so that it updates the model in the loop with the collected annotations, so that the predictions adjust as you annotate, easing you in the process.
However at the end, you should always train a new model with all of the annotations (both from ner.manual and ner.correct). You can make multiple passes on your data to get better results, you won't be able to do this via the model-in-the-loop (it just helps you with annotation). You should always train your model afterwards.