sparknlp.annotator.NorvigSweetingApproach

class sparknlp.annotator.NorvigSweetingApproach[source]

Bases: sparknlp.common.AnnotatorApproach

Trains annotator, that retrieves tokens and makes corrections automatically if not found in an English dictionary.

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. A dictionary of correct spellings must be provided with setDictionary() in the form of a text file, where each word is parsed by a regex pattern.

For instantiated/pretrained models, see NorvigSweetingModel.

For extended examples of usage, see the Spark NLP Workshop.

Input Annotation types

Output Annotation type

TOKEN

TOKEN

Parameters
dictionary

Dictionary needs ‘tokenPattern’ regex in dictionary for separating words

caseSensitive

Whether to ignore case sensitivity, by default False

doubleVariants

Whether to use more expensive spell checker, by default False

Increase search at cost of performance. Enables extra check for word combinations.

shortCircuit

Whether to use faster mode, by default False

Increase performance at cost of accuracy. Faster but less accurate.

frequencyPriority

Applies frequency over hamming in intersections, when false hamming takes priority, by default True

wordSizeIgnore

Minimum size of word before ignoring, by default 3

dupsLimit

Maximum duplicate of characters in a word to consider, by default 2

reductLimit

Word reductions limit, by default 3

intersections

Hamming intersections to attempt, by default 10

vowelSwapLimit

Vowel swap attempts, by default 6

References

Inspired by Norvig model and SymSpell.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline

In this example, the dictionary "words.txt" has the form of:

...
gummy
gummic
gummier
gummiest
gummiferous
...

This dictionary is then set to be the basis of the spell checker.

>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> spellChecker = NorvigSweetingApproach() \
...     .setInputCols(["token"]) \
...     .setOutputCol("spell") \
...     .setDictionary("src/test/resources/spell/words.txt")
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     tokenizer,
...     spellChecker
... ])
>>> pipelineModel = pipeline.fit(trainingData)

Methods

__init__()

clear(param)

Clears a param from the param map if it has been explicitly set.

copy([extra])

Creates a copy of this instance with the same uid and some extra params.

explainParam(param)

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap([extra])

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

fit(dataset[, params])

Fits a model to the input dataset with optional parameters.

fitMultiple(dataset, paramMaps)

Fits a model to the input dataset for each param map in paramMaps.

getInputCols()

Gets current column names of input annotations.

getLazyAnnotator()

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param)

Gets the value of a param in the user-supplied param map or its default value.

getOutputCol()

Gets output column name of annotations.

getParam(paramName)

Gets a param by its name.

getParamValue(paramName)

Gets the value of a parameter.

hasDefault(param)

Checks whether a param has a default value.

hasParam(paramName)

Tests whether this instance contains a param with a given (string) name.

isDefined(param)

Checks whether a param is explicitly set by user or has a default value.

isSet(param)

Checks whether a param is explicitly set by user.

load(path)

Reads an ML instance from the input path, a shortcut of read().load(path).

read()

Returns an MLReader instance for this class.

save(path)

Save this ML instance to the given path, a shortcut of 'write().save(path)'.

set(param, value)

Sets a parameter in the embedded param map.

setCaseSensitive(value)

Sets whether to ignore case sensitivity, by default False.

setDictionary(path[, token_pattern, ...])

Sets dictionary which needs 'tokenPattern' regex for separating words.

setDoubleVariants(value)

Sets whether to use more expensive spell checker, by default False.

setFrequencyPriority(value)

Sets whether to consider frequency over hamming in intersections, when false hamming takes priority, by default True.

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setOutputCol(value)

Sets output column name of annotations.

setParamValue(paramName)

Sets the value of a parameter.

setShortCircuit(value)

Sets whether to use faster mode, by default False.

write()

Returns an MLWriter instance for this ML instance.

Attributes

caseSensitive

dictionary

doubleVariants

dupsLimit

frequencyPriority

getter_attrs

inputCols

intersections

lazyAnnotator

outputCol

params

Returns all params ordered by name.

reductLimit

shortCircuit

vowelSwapLimit

wordSizeIgnore

clear(param)

Clears a param from the param map if it has been explicitly set.

copy(extra=None)

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

extra – Extra parameters to copy to the new instance

Returns

Copy of this instance

explainParam(param)

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra=None)

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters

extra – extra param values

Returns

merged param map

fit(dataset, params=None)

Fits a model to the input dataset with optional parameters.

Parameters
  • dataset – input dataset, which is an instance of pyspark.sql.DataFrame

  • params – an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns

fitted model(s)

New in version 1.3.0.

fitMultiple(dataset, paramMaps)

Fits a model to the input dataset for each param map in paramMaps.

Parameters
  • dataset – input dataset, which is an instance of pyspark.sql.DataFrame.

  • paramMaps – A Sequence of param maps.

Returns

A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

New in version 2.3.0.

getInputCols()

Gets current column names of input annotations.

getLazyAnnotator()

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param)

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol()

Gets output column name of annotations.

getParam(paramName)

Gets a param by its name.

getParamValue(paramName)

Gets the value of a parameter.

Parameters
paramNamestr

Name of the parameter

hasDefault(param)

Checks whether a param has a default value.

hasParam(paramName)

Tests whether this instance contains a param with a given (string) name.

isDefined(param)

Checks whether a param is explicitly set by user or has a default value.

isSet(param)

Checks whether a param is explicitly set by user.

classmethod load(path)

Reads an ML instance from the input path, a shortcut of read().load(path).

property params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

classmethod read()

Returns an MLReader instance for this class.

save(path)

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)

Sets a parameter in the embedded param map.

setCaseSensitive(value)[source]

Sets whether to ignore case sensitivity, by default False.

Parameters
valuebool

Whether to ignore case sensitivity

setDictionary(path, token_pattern='\\S+', read_as='TEXT', options={'format': 'text'})[source]

Sets dictionary which needs ‘tokenPattern’ regex for separating words.

Parameters
pathstr

Path to the source file

token_patternstr, optional

Pattern for token separation, by default \S+

read_asstr, optional

How to read the file, by default ReadAs.TEXT

optionsdict, optional

Options to read the resource, by default {“format”: “text”}

setDoubleVariants(value)[source]

Sets whether to use more expensive spell checker, by default False.

Increase search at cost of performance. Enables extra check for word combinations.

Parameters
valuebool

[description]

setFrequencyPriority(value)[source]

Sets whether to consider frequency over hamming in intersections, when false hamming takes priority, by default True.

Parameters
valuebool

Whether to consider frequency over hamming in intersections

setInputCols(*value)

Sets column names of input annotations.

Parameters
*valuestr

Input columns for the annotator

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters
valuebool

Whether Annotator should be evaluated lazily in a RecursivePipeline

setOutputCol(value)

Sets output column name of annotations.

Parameters
valuestr

Name of output column

setParamValue(paramName)

Sets the value of a parameter.

Parameters
paramNamestr

Name of the parameter

setShortCircuit(value)[source]

Sets whether to use faster mode, by default False.

Increase performance at cost of accuracy. Faster but less accurate.

Parameters
valuebool

Whether to use faster mode

uid

A unique id for the object.

write()

Returns an MLWriter instance for this ML instance.