how can i use the auto word spelling correcter library on input text on user in rasa core.
You can try adding it to the message preprocessor
I would like to know how you did that if you are successful!
@datistiquo nope, the spelling corrector libraries performance is not good.
Yeahh I tested the one week free bing corrector and this perforsm not very well too! What librariers did you try?
1- SpellChecker 2- textblob 3- autocorrector
which library you have tested?
The Bing API!
I’d suggest just adding common spelling errors to your training data tbh (if you’re using the tensorflow embedding pipeline)
@akelad here is pipline. There could be many errors during the typing, so should I put all the possible errors ? is not be duplication ?
eg: 1- i am hapy for being alone. 2- i am happyfor being alone. 3- i am thapy being a lone .
should I insert all of them in my training files ??
language: "en"
pipeline:
- name: "nlp_spacy"
model: "en"
- name: "tokenizer_spacy"
- name: "ner_spacy"
- name: "ner_duckling"
dimensions:
- "number"
- name: "ner_crf"
BILOU_flag: true
features:
# features for word before token
- ["low", "title", "upper", "pos", "pos2"]
# features of token itself
- ["bias", "low", "upper","prefix5", "prefix2", "suffix3", "suffix2", "digit", "title", "digit", "pos", "pos2", "pattern"]
# features for word after the token we want to tag
- ["low", "title", "upper", "pos", "pos2"]
max_iterations: 50
L1_c: 1
L2_c: 1e-3
- name: "ner_synonyms"
- name: "intent_featurizer_count_vectors"
- name: "intent_classifier_tensorflow_embedding"
How? Can you show any example?