com.johnsnowlabs.nlp.annotators.spell.norvig
NorvigSweetingModel
Related Docs:
object NorvigSweetingModel
| package norvig
com.johnsnowlabs.nlp.annotators.spell.norvig
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
Sensitivity on spell checking.
Sensitivity on spell checking. Defaults to false. Might affect accuracy
variants of variants of a word
requirement for annotators copies
requirement for annotators copies
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
Increase search at cost of performance.
Increase search at cost of performance. Enables extra check for word combinations, More accuracy at performance
Maximum duplicate of characters in a word to consider.
Maximum duplicate of characters in a word to consider. Defaults to 2 .Maximum duplicate of characters to account for. Defaults to 2.
Override for additional custom schema checks
Override for additional custom schema checks
Applies frequency over hamming in intersections.
Applies frequency over hamming in intersections. When false hamming takes priority
Sensitivity on spell checking.
Sensitivity on spell checking. Defaults to false. Might affect accuracy
Increase search at cost of performance.
Increase search at cost of performance. Enables extra check for word combinations
Maximum duplicate of characters in a word to consider.
Maximum duplicate of characters in a word to consider. Defaults to 2 .Maximum duplicate of characters to account for. Defaults to 2.
Applies frequency over hamming in intersections.
Applies frequency over hamming in intersections. When false hamming takes priority
input annotations columns currently used
Hamming intersections to attempt.
Hamming intersections to attempt. Defaults to 10
Gets annotation column name going to generate
Gets annotation column name going to generate
Word reduction limit.
Word reduction limit. Defaults to 3
Increase performance at cost of accuracy.
Increase performance at cost of accuracy. Faster but less accurate mode
Vowel swap attempts.
Vowel swap attempts. Defaults to 6
Minimum size of word before ignoring.
Minimum size of word before ignoring. Defaults to 3 ,Minimum size of word before moving on. Defaults to 3.
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
Hamming intersections to attempt.
Hamming intersections to attempt. Defaults to 10
Output annotator type : TOKEN
Output annotator type : TOKEN
Word reduction limit.
Word reduction limit. Defaults to 3
Sensitivity on spell checking.
Sensitivity on spell checking. Defaults to false. Might affect accuracy
Increase search at cost of performance.
Increase search at cost of performance. Enables extra check for word combinations
Maximum duplicate of characters in a word to consider.
Maximum duplicate of characters in a word to consider. Defaults to 2 .Maximum duplicate of characters to account for. Defaults to 2.
Applies frequency over hamming in intersections.
Applies frequency over hamming in intersections. When false hamming takes priority
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Hamming intersections to attempt.
Hamming intersections to attempt. Defaults to 10
Overrides annotation column name when transforming
Overrides annotation column name when transforming
Word reduction limit.
Word reduction limit. Defaults to 3
Increase performance at cost of accuracy.
Increase performance at cost of accuracy. Faster but less accurate mode
Vowel swap attempts.
Vowel swap attempts. Defaults to 6
Minimum size of word before ignoring.
Minimum size of word before ignoring. Defaults to 3 ,Minimum size of word before moving on. Defaults to 3.
Increase performance at cost of accuracy.
Increase performance at cost of accuracy. Faster but less accurate mode
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
Vowel swap attempts.
Vowel swap attempts. Defaults to 6
Minimum size of word before ignoring.
Minimum size of word before ignoring. Defaults to 3 ,Minimum size of word before moving on. Defaults to 3.
Required input and expected output annotator types
This annotator retrieves tokens and makes corrections automatically if not found in an English dictionary. Inspired by Norvig model
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/spell/norvig/NorvigSweetingTestSpec.scala for further reference on how to use this API