Best to use the following (worked for me):
- Use a lookup table
- Use the approach described here Providing conversation context to the NLU using microervices
For our NLU model for the name microservice we used NER_Spacy and NER_CRF and the combination worked for all the cases with pretty basic nlu training data. Spacy would sometimes understand things as ORG and such instead of PERSON, but since we knew from the context of the conversation (that’s the whole point of using microservices) that we’re dealing with a user giving us a name, we could just ignore Spacy’s entity definition and use whatever entity value it gave, regardless of whether Spacy thought it was PERSON or ORG or ENTITY.
Hope this helps.