com.johnsnowlabs.nlp.annotators.spell.symmetric
SymmetricDeleteModel
Related Docs:
object SymmetricDeleteModel
| package symmetric
com.johnsnowlabs.nlp.annotators.spell.symmetric
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
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
requirement for annotators copies
requirement for annotators copies
minimum frequency of corrections a word needs to have to be considered from training.
minimum frequency of corrections a word needs to have to be considered from training. Increase if training set is LARGE. Defaults to 0
Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column
Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column
udf function to be applied to inputCols using this annotator's annotate function as part of ML transformation
maximum duplicate of characters in a word to consider.
maximum duplicate of characters in a word to consider. Defaults to 2
Override for additional custom schema checks
Override for additional custom schema checks
minimum frequency of words to be considered from training.
minimum frequency of words to be considered from training. Increase if training set is LARGE. Defaults to 0.
minimum frequency of corrections a word needs to have to be considered from training.
minimum frequency of corrections a word needs to have to be considered from training. Increase if training set is LARGE. Defaults to 0
maximum duplicate of characters in a word to consider.
maximum duplicate of characters in a word to consider. Defaults to 2
minimum frequency of words to be considered from training.
minimum frequency of words to be considered from training. Increase if training set is LARGE. Defaults to 0.
input annotations columns currently used
max edit distance characters to derive strings from a word
max edit distance characters to derive strings from a word
Gets annotation column name going to generate
Gets annotation column name going to generate
Return list of suggested corrections for potentially incorrectly spelled word
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
length of longest word in corpus
length of longest word in corpus
max edit distance characters to derive strings from a word
max edit distance characters to derive strings from a word
maximum frequency of a word in the corpus
maximum frequency of a word in the corpus
minimum frequency of a word in the corpus
minimum frequency of a word in the corpus
Output annotator type: TOKEN
Output annotator type: TOKEN
minimum frequency of corrections a word needs to have to be considered from training.
minimum frequency of corrections a word needs to have to be considered from training. Increase if training set is LARGE. Defaults to 0
maximum duplicate of characters in a word to consider.
maximum duplicate of characters in a word to consider. Defaults to 2
minimum frequency of words to be considered from training.
minimum frequency of words to be considered from training. Increase if training set is LARGE. Defaults to 0.
Overrides required annotators column if different than default
Overrides required annotators column if different than default
length of longest word in corpus
length of longest word in corpus
max edit distance characters to derive strings from a word
max edit distance characters to derive strings from a word
maximum frequency of a word in the corpus
maximum frequency of a word in the corpus
minimum frequency of a word in the corpus
minimum frequency of a word in the corpus
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
Required input and expected output annotator types
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.
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/spell/symmetric/SymmetricDeleteModelTestSpec.scala for further reference.