com.johnsnowlabs.nlp.annotators.spell.symmetric
SymmetricDeleteApproach
Companion object SymmetricDeleteApproach
class SymmetricDeleteApproach extends AnnotatorApproach[SymmetricDeleteModel] with SymmetricDeleteParams
Trains a Symmetric Delete spelling correction algorithm. Retrieves tokens and utilizes distance metrics to compute possible derived words.
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
either in the form of a text file or directly as an
ExternalResource, where each word is parsed
by a regex pattern.
Inspired by SymSpell.
For instantiated/pretrained models, see SymmetricDeleteModel.
See SymmetricDeleteModelTestSpec for further reference.
Example
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.
import com.johnsnowlabs.nlp.base.DocumentAssembler import com.johnsnowlabs.nlp.annotators.Tokenizer import com.johnsnowlabs.nlp.annotators.spell.symmetric.SymmetricDeleteApproach import org.apache.spark.ml.Pipeline val documentAssembler = new DocumentAssembler() .setInputCol("text") .setOutputCol("document") val tokenizer = new Tokenizer() .setInputCols("document") .setOutputCol("token") val spellChecker = new SymmetricDeleteApproach() .setInputCols("token") .setOutputCol("spell") .setDictionary("src/test/resources/spell/words.txt") val pipeline = new Pipeline().setStages(Array( documentAssembler, tokenizer, spellChecker )) val pipelineModel = pipeline.fit(trainingData)
- See also
NorvigSweetingApproach for an alternative approach to spell checking
ContextSpellCheckerApproach for a DL based approach
- Grouped
- Alphabetic
- By Inheritance
- SymmetricDeleteApproach
- SymmetricDeleteParams
- AnnotatorApproach
- CanBeLazy
- DefaultParamsWritable
- MLWritable
- HasOutputAnnotatorType
- HasOutputAnnotationCol
- HasInputAnnotationCols
- Estimator
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Type Members
-
type
AnnotatorType = String
- Definition Classes
- HasOutputAnnotatorType
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
$[T](param: Param[T]): T
- Attributes
- protected
- Definition Classes
- Params
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
_fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): SymmetricDeleteModel
- Attributes
- protected
- Definition Classes
- AnnotatorApproach
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
beforeTraining(spark: SparkSession): Unit
- Definition Classes
- AnnotatorApproach
-
final
def
checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
final
def
clear(param: Param[_]): SymmetricDeleteApproach.this.type
- Definition Classes
- Params
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
final
def
copy(extra: ParamMap): Estimator[SymmetricDeleteModel]
- Definition Classes
- AnnotatorApproach → Estimator → PipelineStage → Params
-
def
copyValues[T <: Params](to: T, extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
-
final
def
defaultCopy[T <: Params](extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
-
val
deletesThreshold: IntParam
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 (Default:
0
).- Definition Classes
- SymmetricDeleteParams
-
def
derivedWordDistances(wordFrequencies: List[(String, Long)], maxEditDistance: Int): Map[String, (List[String], Long)]
Computes derived words from a frequency of words
-
val
description: String
Spell checking algorithm inspired on Symmetric Delete algorithm
Spell checking algorithm inspired on Symmetric Delete algorithm
- Definition Classes
- SymmetricDeleteApproach → AnnotatorApproach
-
val
dictionary: ExternalResourceParam
Optional dictionary of properly written words.
Optional dictionary of properly written words. If provided, significantly boosts spell checking performance.
Needs
"tokenPattern"
(Default:\S+
) for parsing the resource.Example
... gummy gummic gummier gummiest gummiferous ...
-
val
dupsLimit: IntParam
Maximum duplicate of characters in a word to consider (Default:
2
).Maximum duplicate of characters in a word to consider (Default:
2
).- Definition Classes
- SymmetricDeleteParams
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
explainParam(param: Param[_]): String
- Definition Classes
- Params
-
def
explainParams(): String
- Definition Classes
- Params
-
final
def
extractParamMap(): ParamMap
- Definition Classes
- Params
-
final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
- Params
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
final
def
fit(dataset: Dataset[_]): SymmetricDeleteModel
- Definition Classes
- AnnotatorApproach → Estimator
-
def
fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[SymmetricDeleteModel]
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" )
-
def
fit(dataset: Dataset[_], paramMap: ParamMap): SymmetricDeleteModel
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" )
-
def
fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): SymmetricDeleteModel
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" ) @varargs()
-
val
frequencyThreshold: IntParam
Minimum frequency of words to be considered from training.
Minimum frequency of words to be considered from training. Increase if training set is LARGE (Default:
0
).- Definition Classes
- SymmetricDeleteParams
-
final
def
get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getDeletes(word: String, med: Int): List[String]
Given a word, derive strings with up to maxEditDistance characters deleted
-
def
getDeletesThreshold: Int
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 (Default:
0
).- Definition Classes
- SymmetricDeleteParams
-
def
getDupsLimit: Int
Maximum duplicate of characters in a word to consider (Default:
2
).Maximum duplicate of characters in a word to consider (Default:
2
).- Definition Classes
- SymmetricDeleteParams
-
def
getFrequencyThreshold: Int
Minimum frequency of words to be considered from training.
Minimum frequency of words to be considered from training. Increase if training set is LARGE (Default:
0
).- Definition Classes
- SymmetricDeleteParams
-
def
getInputCols: Array[String]
- returns
input annotations columns currently used
- Definition Classes
- HasInputAnnotationCols
-
def
getLazyAnnotator: Boolean
- Definition Classes
- CanBeLazy
-
def
getMaxEditDistance: Int
Max edit distance characters to derive strings from a word
Max edit distance characters to derive strings from a word
- Definition Classes
- SymmetricDeleteParams
-
final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
- Params
-
final
def
getOutputCol: String
Gets annotation column name going to generate
Gets annotation column name going to generate
- Definition Classes
- HasOutputAnnotationCol
-
def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
-
final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
- Definition Classes
- Params
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
val
inputAnnotatorTypes: Array[AnnotatorType]
Input annotator type : TOKEN
Input annotator type : TOKEN
- Definition Classes
- SymmetricDeleteApproach → HasInputAnnotationCols
-
final
val
inputCols: StringArrayParam
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
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
final
def
isDefined(param: Param[_]): Boolean
- Definition Classes
- Params
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
isSet(param: Param[_]): Boolean
- Definition Classes
- Params
-
def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
val
lazyAnnotator: BooleanParam
- Definition Classes
- CanBeLazy
-
def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logName: String
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
val
longestWordLength: IntParam
Length of longest word in corpus
Length of longest word in corpus
- Definition Classes
- SymmetricDeleteParams
-
val
maxEditDistance: IntParam
Max edit distance characters to derive strings from a word (Default:
3
)Max edit distance characters to derive strings from a word (Default:
3
)- Definition Classes
- SymmetricDeleteParams
-
val
maxFrequency: LongParam
Maximum frequency of a word in the corpus
Maximum frequency of a word in the corpus
- Definition Classes
- SymmetricDeleteParams
-
val
minFrequency: LongParam
Minimum frequency of a word in the corpus
Minimum frequency of a word in the corpus
- Definition Classes
- SymmetricDeleteParams
-
def
msgHelper(schema: StructType): String
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
onTrained(model: SymmetricDeleteModel, spark: SparkSession): Unit
- Definition Classes
- AnnotatorApproach
-
val
optionalInputAnnotatorTypes: Array[String]
- Definition Classes
- HasInputAnnotationCols
-
val
outputAnnotatorType: AnnotatorType
Output annotator type : TOKEN
Output annotator type : TOKEN
- Definition Classes
- SymmetricDeleteApproach → HasOutputAnnotatorType
-
final
val
outputCol: Param[String]
- Attributes
- protected
- Definition Classes
- HasOutputAnnotationCol
-
lazy val
params: Array[Param[_]]
- Definition Classes
- Params
-
def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
final
def
set(paramPair: ParamPair[_]): SymmetricDeleteApproach.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set(param: String, value: Any): SymmetricDeleteApproach.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set[T](param: Param[T], value: T): SymmetricDeleteApproach.this.type
- Definition Classes
- Params
-
final
def
setDefault(paramPairs: ParamPair[_]*): SymmetricDeleteApproach.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): SymmetricDeleteApproach.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
-
def
setDeletesThreshold(value: Int): SymmetricDeleteApproach.this.type
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 (Default:
0
).- Definition Classes
- SymmetricDeleteParams
-
def
setDictionary(path: String, tokenPattern: String = "\\S+", readAs: Format = ReadAs.TEXT, options: Map[String, String] = Map("format" -> "text")): SymmetricDeleteApproach.this.type
Path to file with properly spelled words,
tokenPattern
is the regex pattern to identify them in text, readAs can beReadAs.TEXT
orReadAs.SPARK
, with options passed to Spark reader if the latter is set.Path to file with properly spelled words,
tokenPattern
is the regex pattern to identify them in text, readAs can beReadAs.TEXT
orReadAs.SPARK
, with options passed to Spark reader if the latter is set. Dictionary needstokenPattern
regex for separating words. -
def
setDictionary(value: ExternalResource): SymmetricDeleteApproach.this.type
External dictionary already in the form of ExternalResource, for which the Map member
options
has an entry defined for"tokenPattern"
.External dictionary already in the form of ExternalResource, for which the Map member
options
has an entry defined for"tokenPattern"
.Example
val resource = ExternalResource( "src/test/resources/spell/words.txt", ReadAs.TEXT, Map("tokenPattern" -> "\\S+") ) val spellChecker = new SymmetricDeleteApproach() .setInputCols("token") .setOutputCol("spell") .setDictionary(resource)
-
def
setDupsLimit(value: Int): SymmetricDeleteApproach.this.type
Maximum duplicate of characters in a word to consider (Default:
2
)Maximum duplicate of characters in a word to consider (Default:
2
)- Definition Classes
- SymmetricDeleteParams
-
def
setFrequencyThreshold(value: Int): SymmetricDeleteApproach.this.type
Minimum frequency of words to be considered from training.
Minimum frequency of words to be considered from training. Increase if training set is LARGE (Default:
0
)- Definition Classes
- SymmetricDeleteParams
-
final
def
setInputCols(value: String*): SymmetricDeleteApproach.this.type
- Definition Classes
- HasInputAnnotationCols
-
def
setInputCols(value: Array[String]): SymmetricDeleteApproach.this.type
Overrides required annotators column if different than default
Overrides required annotators column if different than default
- Definition Classes
- HasInputAnnotationCols
-
def
setLazyAnnotator(value: Boolean): SymmetricDeleteApproach.this.type
- Definition Classes
- CanBeLazy
-
def
setLongestWordLength(value: Int): SymmetricDeleteApproach.this.type
Length of longest word in corpus
Length of longest word in corpus
- Definition Classes
- SymmetricDeleteParams
-
def
setMaxEditDistance(value: Int): SymmetricDeleteApproach.this.type
Max edit distance characters to derive strings from a word
Max edit distance characters to derive strings from a word
- Definition Classes
- SymmetricDeleteParams
-
def
setMaxFrequency(value: Long): SymmetricDeleteApproach.this.type
Maximum frequency of a word in the corpus
Maximum frequency of a word in the corpus
- Definition Classes
- SymmetricDeleteParams
-
def
setMinFrequency(value: Long): SymmetricDeleteApproach.this.type
Minimum frequency of a word in the corpus
Minimum frequency of a word in the corpus
- Definition Classes
- SymmetricDeleteParams
-
final
def
setOutputCol(value: String): SymmetricDeleteApproach.this.type
Overrides annotation column name when transforming
Overrides annotation column name when transforming
- Definition Classes
- HasOutputAnnotationCol
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
def
train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): SymmetricDeleteModel
- Definition Classes
- SymmetricDeleteApproach → AnnotatorApproach
-
final
def
transformSchema(schema: StructType): StructType
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
- Definition Classes
- AnnotatorApproach → PipelineStage
-
def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
-
val
uid: String
- Definition Classes
- SymmetricDeleteApproach → Identifiable
-
def
validate(schema: StructType): Boolean
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.
- schema
to be validated
- returns
True if all the required types are present, else false
- Attributes
- protected
- Definition Classes
- AnnotatorApproach
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
def
write: MLWriter
- Definition Classes
- DefaultParamsWritable → MLWritable
Inherited from SymmetricDeleteParams
Inherited from AnnotatorApproach[SymmetricDeleteModel]
Inherited from CanBeLazy
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from HasOutputAnnotatorType
Inherited from HasOutputAnnotationCol
Inherited from HasInputAnnotationCols
Inherited from Estimator[SymmetricDeleteModel]
Inherited from PipelineStage
Inherited from Logging
Inherited from Params
Inherited from Serializable
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from Any
Parameters
A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.
Annotator types
Required input and expected output annotator types