Hi,
I have searched for some clarification regarding the use of look-up tables in this forum with no success. I would like to have a better understanding of the way look-up tables work and I hope this topic helps others as well.
For example, I want my extractor to get the names of 100 countries. Do I have to provide an example for each country in “common_examples” and also provide the list of 100 countries in the NLU data: { “rasa_nlu_data”:{ “common_examples”:[], “lookup_tables”:[] } }
If yes, then I will have to provide 100 examples/sentences. So, what’s the advantage of using the lookup table? Does it reduce the number of training examples? (ner_crf would need around 5 examples for each country name. So, I would end up providing 500 examples for 100 countries).
Is there a keyword based entity extractor just like keyword_intent_classifier? For e.g, do string match of all the words in a sentence with the list provided in lookup_tables. In this way, I don’t have to provide any training examples and the accuracy will be better.
I can implement this myself but I just want to know whether RASA_NLU already has this feature. Also, will there be any drawbacks with this string match method?
Cheers,
Achinta