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Methods defined here:
- __init__(self, data=None, word_column=None, pos_column=None, data_sequence_column=None, data_partition_column=None, data_order_column=None)
- DESCRIPTION:
The TextChunker function divides text into phrases and assigns each
phrase a tag that identifies its type.
PARAMETERS:
data:
Required Argument.
Specifies the teradataml DataFrame that contains the text to be
scanned.
data_partition_column:
Required Argument.
Specifies Partition By columns for data.
Values to this argument can be provided as list, if multiple
columns are used for partition.
Types: str OR list of Strings (str)
data_order_column:
Required Argument.
Specifies Order By columns for data.
Values to this argument can be provided as list, if multiple
columns are used for ordering.
Types: str OR list of Strings (str)
word_column:
Required Argument.
Specifies the name of the input teradataml DataFrame column that
contains the words to chunk into phrases. Typically, this is the
word column of the output teradataml DataFrame of the "PosTagger"
function.
Types: str
pos_column:
Required Argument.
Specifies the name of the input teradataml DataFrame column the
part-of-speech (POS) tag of words. Typically, this is the pos_tag
column of the output teradataml DataFrame of the "PosTagger"
function
Types: str
data_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each
row of the input argument "data". The argument is used to ensure
deterministic results for functions which produce results that
vary from run to run.
Types: str OR list of Strings (str)
RETURNS:
Instance of TextChunker.
Output teradataml DataFrames can be accessed using attribute
references, such as TextChunkerObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load example data.
load_example_data("textchunker", "posttagger_output")
# Create teradataml DataFrame objects.
# "posttagger_output" is the ouput of POSTagger function.
posttagger_output = DataFrame.from_table("posttagger_output")
# Example 1 - This example uses the persisted output of POSTagger
# as Input.
textchunker_out =TextChunker(data=posttagger_output,
data_order_column=['paraid','word_sn'],
word_column='word',
pos_column='pos_tag',
data_sequence_column='paraid',
data_partition_column='paraid'
)
# Print the result DataFrame
print(textchunker_out)
# Load the data to run the example.
load_example_data("postagger","paragraphs_input")
# Create input teradataml dataframe.
paragraphs_input = DataFrame.from_table("paragraphs_input")
# Example 2 - This example uses output of SentenceExtractor and POSTagger
# as Input.
sentenceextractor_out = SentenceExtractor(data=paragraphs_input,
text_column='paratext',
accumulate='paraid'
)
se_res = sentenceextractor_out.result
sentenceextractor_out = se_res.assign(True,sentence_id = se_res.paraid*1000+se_res.sentence_sn,
sentence = se_res.sentence)
pos_tagger_out = POSTagger(data=sentenceextractor_out,
text_column='sentence',
accumulate='sentence_id')
textchunker_out =TextChunker(data=pos_tagger_out.result,
data_partition_column='word_sn',
data_order_column='word_sn',
word_column='word',
pos_column='pos_tag')
# Print the result DataFrame
print(textchunker_out)
- __repr__(self)
- Returns the string representation for a TextChunker class instance.
- get_build_time(self)
- Function to return the build time of the algorithm in seconds.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_prediction_type(self)
- Function to return the Prediction type of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_target_column(self)
- Function to return the Target Column of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- show_query(self)
- Function to return the underlying SQL query.
When model object is created using retrieve_model(), then None is returned.
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