com.johnsnowlabs.nlp.annotators.spell.symmetric
SymmetricDeleteModel
Related Docs:
object SymmetricDeleteModel
| package symmetric
com.johnsnowlabs.nlp.annotators.spell.symmetric
takes a document and annotations and produces new annotations of this annotator's annotation type
takes a document and annotations and produces new annotations of this annotator's annotation type
Annotations that correspond to inputAnnotationCols generated by previous annotators if any
any number of annotations processed for every input annotation. Not necessary one to one relationship
Annotator reference id.
Annotator reference id. Used to identify elements in metadata or to refer to this annotator type
requirement for annotators copies
requirement for annotators copies
input annotations columns currently used
Gets annotation column name going to generate
Gets annotation column name going to generate
Return list of suggested corrections for potentially incorrectly spelled word
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
Computes Levenshtein distance : Metric of measuring difference between two sequences (edit distance) Source: https://rosettacode.org/wiki/Levenshtein_distance
Annotator reference id.
Annotator reference id. Used to identify elements in metadata or to refer to this annotator type
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Overrides annotation column name when transforming
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
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
Dataset[Row]
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
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
Created by danilo 16/04/2018, inspired on https://github.com/wolfgarbe/SymSpell
The Symmetric Delete spelling correction algorithm reduces the complexity of edit candidate generation and dictionary lookup for a given Damerau-Levenshtein distance. It is six orders of magnitude faster (than the standard approach with deletes + transposes + replaces + inserts) and language independent.