com.johnsnowlabs.nlp.annotators.spell.symmetric
SymmetricDeleteApproach
Related Docs:
object SymmetricDeleteApproach
| package symmetric
com.johnsnowlabs.nlp.annotators.spell.symmetric
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
Created by danilo 26/04/2018 Computes derived words from a frequency of words
Spell checking algorithm inspired on Symmetric Delete algorith
Spell checking algorithm inspired on Symmetric Delete algorith
file with a list of correct words
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.
Created by danilo 14/04/2018 Given a word, derive strings with up to maxEditDistance characters deleted
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
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
Optional dictionary of properly written words.
Optional dictionary of properly written words. If provided, significantly boosts spell checking performance
Optional dictionary of properly written words.
Optional dictionary of properly written words. If provided, significantly boosts spell checking performance
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
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, Symmetric Delete spelling correction algorithm. It retrieves tokens and utilizes distance metrics to compute possible derived words.
Inspired by https://github.com/wolfgarbe/SymSpell
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/spell/symmetric/SymmetricDeleteModelTestSpec.scala for further reference.