takes a document and annotations and produces new annotations of this annotator's annotation type
requirement for annotators copies
Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column
Override for additional custom schema checks
input annotations columns currently used
Gets annotation column name going to generate
Annotator reference id.
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
Overrides required annotators column if different than default
Overrides annotation column name when transforming
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
requirement for pipeline transformation validation.
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