Is there any document/paper/description on how the padatious intent parser is working under the hood? I could not find anything except the official documentation which doesn’t cover this topic very much.
I’m interested in how the intent and entity classification works.
I can see that it is based on the FANN library and uses simple feedforward nets but it would be great to get some more details about that
Funny, was talking exactly about this in mattermost, i can nearly copy paste an answer for you
padaos ensures exact matches by using regex
each padatious intent is a neural network model that tells you if for a piece f text you should trigger it, with a probability
padatious engine chooses the highest scoring intent, or None if all probabilities are bellow a threshold
Specifically, it uses one-hot encoding via local vocabularies (and a few extras like an unknown ratio input) into a series of shallow feed forward networks to perform intent recognition and entity extraction.
Unfortunately there isn’t a lot of extra documentation on Padatious - although @Wolfgange might have some comments to add when he has a spare few minutes.
Yeah, what @Jarbas_Ai said pretty much sums it up. If you have any specific questions, or want more details/clarification, feel free to ask me here or on Mattermost (@matthew-mycroft)