I am using RASA NLU for intent identification and entities are custom and having few doubts on training pattern. Please find the details below.
- If your question consist like 6 words, suppose if we have mapped all words to single custom entity or majority of words are mapped to entity and my question is will NLU consider even entity words are also to identify intents or it will ignore if we mapped them as entity. 2 . And also generally, NLU will prefer only some kind of lengthy statements or we can add like below example.
RAM Failure as an Intent and statement is very directly RAM down. Do you think, any wrong here or it’s absolutely fine and my question is it compulsory we need to make the statement is lengthy like My system is not working and may be RAM problem etc. Can you please comment on that and if first example also fine means RAM down and then if mapped two words are an entity and am interested how the training will perform and will i loose anything in future or it’s not a general practice.
- Is it correct, if we have good number of examples then we can use n Number of intents and not needed to limit and we can extend like 10 or 100 intents also?
And if our dataset is imbalanced how to handle them while the training.
- How to train the custom entities, example if we have mapped few words are entities and in future even some near by words are also won’t recognize and how to resolve them smartly because if we do everything as dataset and it would be like one to one pattern and where is learning scope on NLU.
Thanks and let me know if you need any other information and also am not sure whether any questions are irrelevant to you but these came with curiosity
Regards Sharath email id:email@example.com