Within the conversation I am asking the user for his name. For this I am using the ‘ner_spacy’ extractor which extracts the name pretty good.
If the user answers: “My name is Bob” everything works fine. ner_spacy ectracts the name and tensorflow recognizes the intent “provide_name”
If however the users just answers with his name like: “Bob Miller” then ‘nerspacy’ still extracts the name as an entity but tensorflow can’t recognize the intent as “provide_name”
Is there a good solution for this?
I can think of two:
- Spamming my nlu file with huge name lists which makes using ‘ner_spacy’ redundant, probbably gives worse results and needs maintaing on my site.
- Using entities as features. If there was a featurizer which would add recognized entities to the feature vector tensorflow might be able to detect the right intent, if spacy recognizes a name.
Would the 2 solution be viable? Is there a featurizer that already does this? Or is there a different solution which works better?