I’m building a test bot using rasa nlu and core capabilities where in it captures two entities and summarize the entity values that were captured.
For this, I had around 2000 examples for 9 intents and 2 entities and tried with both ‘spacy_sklearn’ and ‘tensorflow_embedding’ pipelines. There is a problem while detecting entities for a value that is not present in the training dataset.
For example, Below is the intent where I detect ‘name’ as the entity. ## intent:name - My name is Alice, - I am Josh, - I’m Lucy, - People call me Greg, - It’s David, - name is Johny, - John is my name, - lucy is my name
Entities aren’t getting detected if I give any other name apart from the above trained sample. Failed utterances: ‘Kumar is my name’, ‘I am Rocky’ etc.
This suggests that entity detection is merely a string check or rule based check on the trained examples. Could you please suggest any way to extract entities accurately.