def parse_all(user_input: str, culture: str) -> List[ModelResult]:
return [
# Number recognizer - This function will find any number from the input
# E.g "I have two apples" will return "2".
Recognizers.recognize_number(user_input, culture),
# Ordinal number recognizer - This function will find any ordinal number
# E.g "eleventh" will return "11".
Recognizers.recognize_ordinal(user_input, culture),
# Percentage recognizer - This function will find any number presented as percentage
# E.g "one hundred percents" will return "100%"
Recognizers.recognize_percentage(user_input, culture),
# Age recognizer - This function will find any age number presented
# E.g "After ninety five years of age, perspectives change" will return "95 Year"
Recognizers.recognize_age(user_input, culture),
# Currency recognizer - This function will find any currency presented
# E.g "Interest expense in the 1988 third quarter was $ 75.3 million" will return "75300000 Dollar"
Recognizers.recognize_currency(user_input, culture),
# Dimension recognizer - This function will find any dimension presented
# E.g "The six-mile trip to my airport hotel that had taken 20 minutes earlier in the day took more than three hours." will return "6 Mile"
Recognizers.recognize_dimension(user_input, culture),
# Temperature recognizer - This function will find any temperature presented
# E.g "Set the temperature to 30 degrees celsius" will return "30 C"
Recognizers.recognize_temperature(user_input, culture),
# DateTime recognizer - This function will find any Date even if its write in colloquial language -
# E.g "I'll go back 8pm today" will return "2017-10-04 20:00:00"
Recognizers.recognize_datetime(user_input, culture)
]
def _parse_all_entities(user_input: str,
culture: str) -> List[Dict[Text, Any]]:
"""
This is the main method that does the entity extraction work.
For more details: https://github.com/Microsoft/Recognizers-Text/tree/master/Python#api-documentation
"""
return [
# Number recognizer - This function will find any number from the input
# E.g "I have two apples" will return "2".
Recognizers.recognize_number(user_input, culture),
# Ordinal number recognizer - This function will find any ordinal number
# E.g "eleventh" will return "11".
Recognizers.recognize_ordinal(user_input, culture),
# Percentage recognizer - This function will find any number presented as percentage
# E.g "one hundred percents" will return "100%"
Recognizers.recognize_percentage(user_input, culture),
# Age recognizer - This function will find any age number presented
# E.g "After ninety five years of age, perspectives change" will return
# "95 Year"
Recognizers.recognize_age(user_input, culture),
# Currency recognizer - This function will find any currency presented
# E.g "Interest expense in the 1988 third quarter was $ 75.3 million"
# will return "75300000 Dollar"
Recognizers.recognize_currency(user_input, culture),
# Temperature recognizer - This function will find any temperature presented
# E.g "Set the temperature to 30 degrees celsius" will return "30 C"
Recognizers.recognize_temperature(user_input, culture),
# DateTime recognizer - This function will find any Date even if its write in colloquial language -
# E.g "I'll go back 8pm today" will return "2017-10-04 20:00:00"
Recognizers.recognize_datetime(user_input, culture),
# PhoneNumber recognizer will find any phone number presented
# E.g "My phone number is ( 19 ) 38294427."
Recognizers.recognize_phone_number(user_input, culture),
# Email recognizer will find any phone number presented
# E.g "Please write to me at [email protected] for more information on task
# #A1"
Recognizers.recognize_email(user_input, culture),
]