Normalization regex patterns which match will be removed from token (Default: Array("[^\\pL+]")
)
Cleans out tokens
Cleans out tokens
Normalization regex patterns which match will be removed from token (Default: Array("[^\\pL+]")
)
input annotations columns currently used
Whether to convert strings to lowercase (Default: false
)
Set the maximum allowed length for each token
Set the minimum allowed length for each token (Default: 0
)
Gets annotation column name going to generate
Gets annotation column name going to generate
Whether or not to be case sensitive to match slangs (Default: false
)
Input Annotator Type : TOKEN
Input Annotator Type : TOKEN
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
Whether to convert strings to lowercase (Default: false
)
Set the maximum allowed length for each token
Set the minimum allowed length for each token (Default: 0
)
Output Annotator Type : TOKEN
Output Annotator Type : TOKEN
Normalization regex patterns which match will be removed from token (Default: Array("[^\\pL+]")
)
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Whether to convert strings to lowercase (Default: false
)
Set the maximum allowed length for each token
Set the minimum allowed length for each token (Default: 0
)
Overrides annotation column name when transforming
Overrides annotation column name when transforming
Delimited file with list of custom words to be manually corrected
Delimited file with list of custom words to be manually corrected
Whether or not to be case sensitive to match slangs (Default: false
)
Delimited file with list of custom words to be manually corrected
Whether or not to be case sensitive to match slangs (Default: false
)
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
required internal uid for saving annotator
required internal uid for saving annotator
takes a Dataset and checks to see if all the required annotation types are present.
takes a Dataset and checks to see if all the required annotation types are present.
to be validated
True if all the required types are present, else false
A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.
Required input and expected output annotator types
Annotator that cleans out tokens. Requires stems, hence tokens. Removes all dirty characters from text following a regex pattern and transforms words based on a provided dictionary
For extended examples of usage, see the Spark NLP Workshop.
Example