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- NERExtractor(data=None, user_defined_data=None, rules_data=None, text_column=None, input_language='EN', show_context=0, accumulate=None, **generic_arguments)
- DESCRIPTION:
NERExtractor() performs Named Entity Recognition (NER) on input text
according to user-defined dictionary words or regular expression (regex) patterns.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
user_defined_data:
Required Argument.
Specifies the teradataml DataFrame which contains user defined words and the corresponding entity label.
Types: teradataml DataFrame
rules_data:
Required Argument.
Specifies the teradataml DataFrame which contains user-defined regex patterns and the corresponding entity label.
Types: teradataml DataFrame
text_column:
Required Argument.
Specifies the name of the teradataml DataFrame column that will be used for NER search.
Types: str
input_language:
Optional Argument.
Specifies the language of input text.
Default Value: "EN"
Types: str
show_context:
Optional Argument.
Specifies the number of words before and after the matched entity. If leading or trailing
words are less than "show_context", then ellipsis (...) are added. Must be a positive value
less than 10.
Default Value: 0
Types: int
accumulate:
Optional Argument.
Specifies the name(s) of input teradataml DataFrame column(s) to copy to the output.
table to output.
Types: str or list of str
**generic_arguments:
Optional Argument.
Specifies the generic keyword arguments SQLE functions accept. Below are the generic
keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the function in a table or not.
When set to True, results are persisted in a table; otherwise, results are
garbage collected at the end of the session.
Default Value: False
Types: bool
volatile:
Optional Argument.
Specifies whether to put the results of the function in a volatile table or not.
When set to True, results are stored in a volatile table; otherwise not.
Default Value: False
Types: bool
Function allows the user to partition, hash, order or local order the input
data. These generic arguments are available for each argument that accepts
teradataml DataFrame as input and can be accessed as:
* "<input_data_arg_name>_partition_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_hash_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_order_column" accepts str or list of str (Strings)
* "local_order_<input_data_arg_name>" accepts boolean
Note:
These generic arguments are supported by teradataml if the underlying SQLE Engine
function supports, else an exception is raised.
RETURNS:
Instance of NERExtractor.
Output teradataml DataFrames can be accessed using attribute references, such as TDNERExtractorObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in the example from teradataml.
# 3. Function will raise an error if not supported on the Vantage user is connected to.
# Load the example data.
load_example_data("tdnerextractor", ["ner_input_eng", "ner_dict", "ner_rule"])
# Create teradataml DataFrame objects.
df = DataFrame.from_table("ner_input_eng")
user_defined_words = DataFrame.from_table("ner_dict")
rules = DataFrame.from_table("ner_rule")
# Check the list of available analytic functions.
display_analytic_functions()
# Import function NERExtractor.
from teradataml import NERExtractor
# Example 1: Perform Named Entity Recognition (NER) using Rules and Dict with Accumulate.
NER_out = NERExtractor(data=df,
user_defined_data=user_defined_words,
rules_data=rules,
text_column=["txt"],
input_language="en",
show_context=3,
accumulate=["id"])
# Print the result DataFrame.
print(NER_out.result)
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