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Rasa NLU in Depth - Part 2: Entity Recognition
post by Tobias_Wochinger on Feb 28, 2019
Hi Rasa community!
we just published part 2 of our three-piece blog post series in which share our best practices and recommendations how to custom-tailor the Rasa NLU pipeline for your individual contextual AI assistant.
This part is about entity recognition and covers
- Which entity extractor to use for which component
- how tackle common problems: fuzzy entities, extraction of addresses, etc
Read it here: Rasa NLU in Depth: Entity Recognition.
Also check out the other parts of this series:
- Part 1: Intent Recognition: Choose the right classifier for your AI assistant project
- Part 2: Entity Extraction – Choose the right extractor for each entity
- Part 3: Hyperparameters – How to select and optimize them (to be released soon!)
Lets us know what your experiences and recommendations are for the perfect entity recognition with Rasa NLU
post by luofanghao on Jun 18, 2019
What if I have an entity called “label name” and the value are just some random strings? like: “5cb28asdubq”, “test111”, “829label”…
what is the best way to extract this entity? I guess maybe I should just restrict the format of user’s inputs so I can apply regex match? like: “the label number is: (*)”
And I do think it will be super useful for rasa to have a “input box” widget, which can let user type the information inside the box.
post by Tobias_Wochinger on Jun 19, 2019
Is it completely random? That’s indeed very hard. Maybe you could use Forms ?
post by luofanghao on Jun 19, 2019
yes. It could be anything. I am using Forms. But as I understand it is just for dialog management, no help for the entity extraction.
Theoratically yes it is hard to do entity extraction solely on the “name” itself, since it can be anything. However if think about it we should be able to extract them easily by looking at the structure of the whole sentence. In this case, a regex can catch it. I believe this can be really useful. I am not sure but I have not found a way in Rasa to do it. Maybe I will try custom entity extractor.
post by Tobias_Wochinger on Jun 20, 2019
Mhm, if the sentence has a certain structure you should be able to tag it with ner_crf, right?
post by luofanghao on Jun 20, 2019
yeah. you mean like here? I have tried but it is not stable. a lot of times entity extraction fails.
post by luofanghao on Jun 20, 2019
finally solved it. can be done by custom slot mapping as : Forms
post by Tobias_Wochinger on Jun 21, 2019
Great to hear
post by luofanghao on Jul 3, 2019
Hey, I really suggest we should add this conversation topic in the blog post as well. Since I believe it is a very common problem, and it can be solved in different ways. It would be a good thing to conclude them. (though it is involved stories and intent design as well)
post by Tobias_Wochinger on Jul 3, 2019
@luofanghao Thanks for your feedback. The mentioned topic is actually quite tricky.
post by pranay_raj on Jan 12, 2020
Hey! Can you please help me out , how were you able to solve dynamic and random entities.? Those entities which are never seen by the model. How by using custom slot mapping??
post by emeariaenea on Feb 13, 2023
@luofanghao Hello! Excuse me for bothering you after SO long. I’m trying to solve the issue related to dynamic and random entities. Could you, please, give some advice? Or explain to me how to use:
I’m sort of desperate!