Hi, i am currently developing a chat bot for movie ticket booking.
I want to use a lookup table for “language” entity. But my chatbot is not predicting the languages which are there in lookup table, but it is working fine with the training examples.
I think my config.yml is not correct. I tried so many pipeline configurations, but none of them were working. I also tried looking in this blog but it is a outdated blog, the configurations now we have were changed compared to it.
In the first message it is correctly picking the “language” entity.
But in the second message it is picking “bodo” language as a movie_name.
Eventhough i didn’t added urdu in my training data it is working fine, but “bodo” is not working fine.
I don’t even understand whether my lookup table is working or not.
# Insert normal .txt/.csv path here
file = open(r"C:\Users\User\Desktop\Cities in the World.csv", "r")
# Splits each row
lines = file.read().splitlines()
n = 0
# Insert target output path here
file2 = open(r"C:\Users\User\Desktop\cities.yml", "w")
# Writes the headers(?), remember to change the lookup name
file2.write("version: \"2.0\"\nnlu:\n - lookup: city \n examples: |\n")
# Adds indent and dash to each line
for line in lines:
file2.write(" - " + str(line) + "\n")
# Closes the files
@fkoerner I already watched the session, she implemented it in a form to check the user messages with some mistakes, but i want to implement it as a custom component which will have to pickup entites with misspells
@saimanoj2826 you can look at the RegexEntityExtractor for inspiration. Basically, a FuzzyEntityExtractor can work similarly, except that this line will need to allow for fuzzy matches. Does that help?
@fkoerner so suppose I have a list of 20 colors… For two colors: red and black I have training examples in my nlu file… Rest of the colors I have added in lookup table… With the above pipeline it’s picking up red and black only because I have training examples for them in my nlu file but not the ones I have included in my lookup table