Packages

class WordSegmenterModel extends AnnotatorModel[WordSegmenterModel] with HasSimpleAnnotate[WordSegmenterModel] with PerceptronPredictionUtils

WordSegmenter which tokenizes non-english or non-whitespace separated texts.

Many languages are not whitespace separated and their sentences are a concatenation of many symbols, like Korean, Japanese or Chinese. Without understanding the language, splitting the words into their corresponding tokens is impossible. The WordSegmenter is trained to understand these languages and plit them into semantically correct parts.

This annotator is based on the paper Chinese Word Segmentation as Character Tagging. Word segmentation is treated as a tagging problem. Each character is be tagged as on of four different labels: LL (left boundary), RR (right boundary), MM (middle) and LR (word by itself). The label depends on the position of the word in the sentence. LL tagged words will combine with the word on the right. Likewise, RR tagged words combine with words on the left. MM tagged words are treated as the middle of the word and combine with either side. LR tagged words are words by themselves.

Example (from [1], Example 3(a) (raw), 3(b) (tagged), 3(c) (translation)):

  • 上海 计划 到 本 世纪 末 实现 人均 国内 生产 总值 五千 美元
  • 上/LL 海/RR 计/LL 划/RR 到/LR 本/LR 世/LL 纪/RR 末/LR 实/LL 现/RR 人/LL 均/RR 国/LL 内/RR 生/LL 产/RR 总/LL 值/RR 五/LL 千/RR 美/LL 元/RR
  • Shanghai plans to reach the goal of 5,000 dollars in per capita GDP by the end of the century.

This is the instantiated model of the WordSegmenterApproach. For training your own model, please see the documentation of that class.

Pretrained models can be loaded with pretrained of the companion object:

val wordSegmenter = WordSegmenterModel.pretrained()
  .setInputCols("document")
  .setOutputCol("words_segmented")

The default model is "wordseg_pku", default language is "zh", if no values are provided. For available pretrained models please see the Models Hub.

For extended examples of usage, see the Examples and the WordSegmenterTest.

References:

  • [1] Xue, Nianwen. “Chinese Word Segmentation as Character Tagging.” International Journal of Computational Linguistics & Chinese Language Processing, Volume 8, Number 1, February 2003: Special Issue on Word Formation and Chinese Language Processing, 2003, pp. 29-48. ACLWeb, https://aclanthology.org/O03-4002.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.WordSegmenterModel
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val wordSegmenter = WordSegmenterModel.pretrained()
  .setInputCols("document")
  .setOutputCol("token")

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  wordSegmenter
))

val data = Seq("然而，這樣的處理也衍生了一些問題。").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("token.result").show(false)
+--------------------------------------------------------+
|result                                                  |
+--------------------------------------------------------+
|[然而, ，, 這樣, 的, 處理, 也, 衍生, 了, 一些, 問題, 。    ]|
+--------------------------------------------------------+
Linear Supertypes
PerceptronPredictionUtils, PerceptronUtils, HasSimpleAnnotate[WordSegmenterModel], AnnotatorModel[WordSegmenterModel], CanBeLazy, RawAnnotator[WordSegmenterModel], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[WordSegmenterModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. WordSegmenterModel
  2. PerceptronPredictionUtils
  3. PerceptronUtils
  4. HasSimpleAnnotate
  5. AnnotatorModel
  6. CanBeLazy
  7. RawAnnotator
  8. HasOutputAnnotationCol
  9. HasInputAnnotationCols
  10. HasOutputAnnotatorType
  11. ParamsAndFeaturesWritable
  12. HasFeatures
  13. DefaultParamsWritable
  14. MLWritable
  15. Model
  16. Transformer
  17. PipelineStage
  18. Logging
  19. Params
  20. Serializable
  21. Serializable
  22. Identifiable
  23. AnyRef
  24. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new WordSegmenterModel()

    Annotator reference id.

    Annotator reference id. Used to identify elements in metadata or to refer to this annotator type

  2. new WordSegmenterModel(uid: String)

    uid

    required uid for storing annotator to disk

Type Members

  1. type AnnotationContent = Seq[Row]

    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

    Attributes
    protected
    Definition Classes
    AnnotatorModel
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. def annotate(annotations: Seq[Annotation]): Seq[Annotation]

    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

    Annotations that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every input annotation. Not necessary one to one relationship

    Definition Classes
    WordSegmenterModel → HasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  14. def buildWordSegments(taggedSentences: Array[TaggedSentence]): Seq[Annotation]
  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. final def clear(param: Param[_]): WordSegmenterModel.this.type
    Definition Classes
    Params
  17. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  18. def copy(extra: ParamMap): WordSegmenterModel

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  19. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  20. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  21. def dfAnnotate: UserDefinedFunction

    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

    returns

    udf function to be applied to inputCols using this annotator's annotate function as part of ML transformation

    Definition Classes
    HasSimpleAnnotate
  22. val enableRegexTokenizer: BooleanParam
  23. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  24. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  25. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  26. def explainParams(): String
    Definition Classes
    Params
  27. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  28. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  29. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  30. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  31. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  32. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  33. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  34. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  35. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  36. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  37. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  38. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  39. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  40. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  41. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  42. def getModel: AveragedPerceptron

  43. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  44. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  45. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  46. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  47. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  48. def hasParent: Boolean
    Definition Classes
    Model
  49. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  50. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  51. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. val inputAnnotatorTypes: Array[String]

    Input Annotator Types: DOCUMENT

    Input Annotator Types: DOCUMENT

    Definition Classes
    WordSegmenterModel → HasInputAnnotationCols
  53. 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
  54. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  55. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  56. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  57. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  58. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  59. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  67. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. val model: StructFeature[AveragedPerceptron]

    POS model

  72. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  73. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  74. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  75. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  76. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  77. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  78. val outputAnnotatorType: AnnotatorType

    Output Annotator Types: TOKEN

    Output Annotator Types: TOKEN

    Definition Classes
    WordSegmenterModel → HasOutputAnnotatorType
  79. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  80. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  81. var parent: Estimator[WordSegmenterModel]
    Definition Classes
    Model
  82. val pattern: Param[String]

    Regex pattern used to match delimiters (Default: "\\s+")

  83. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  84. def set[T](feature: StructFeature[T], value: T): WordSegmenterModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  85. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): WordSegmenterModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  86. def set[T](feature: SetFeature[T], value: Set[T]): WordSegmenterModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  87. def set[T](feature: ArrayFeature[T], value: Array[T]): WordSegmenterModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  88. final def set(paramPair: ParamPair[_]): WordSegmenterModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  89. final def set(param: String, value: Any): WordSegmenterModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  90. final def set[T](param: Param[T], value: T): WordSegmenterModel.this.type
    Definition Classes
    Params
  91. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): WordSegmenterModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  92. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): WordSegmenterModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  93. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): WordSegmenterModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): WordSegmenterModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  95. final def setDefault(paramPairs: ParamPair[_]*): WordSegmenterModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  96. final def setDefault[T](param: Param[T], value: T): WordSegmenterModel.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  97. def setEnableRegexTokenizer(value: Boolean): WordSegmenterModel.this.type

  98. final def setInputCols(value: String*): WordSegmenterModel.this.type
    Definition Classes
    HasInputAnnotationCols
  99. def setInputCols(value: Array[String]): WordSegmenterModel.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  100. def setLazyAnnotator(value: Boolean): WordSegmenterModel.this.type
    Definition Classes
    CanBeLazy
  101. def setModel(targetModel: AveragedPerceptron): WordSegmenterModel.this.type

  102. final def setOutputCol(value: String): WordSegmenterModel.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  103. def setParent(parent: Estimator[WordSegmenterModel]): WordSegmenterModel
    Definition Classes
    Model
  104. def setPattern(value: String): WordSegmenterModel.this.type

  105. def setToLowercase(value: Boolean): WordSegmenterModel.this.type

  106. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  107. def tag(model: AveragedPerceptron, tokenizedSentences: Array[TokenizedSentence]): Array[TaggedSentence]

    Tags a group of sentences into POS tagged sentences The logic here is to create a sentence context, run through every word and evaluate its context Based on how frequent a context appears around a word, such context is given a score which is used to predict Some words are marked as non ambiguous from the beginning

    Tags a group of sentences into POS tagged sentences The logic here is to create a sentence context, run through every word and evaluate its context Based on how frequent a context appears around a word, such context is given a score which is used to predict Some words are marked as non ambiguous from the beginning

    tokenizedSentences

    Sentence in the form of single word tokens

    returns

    A list of sentences which have every word tagged

    Definition Classes
    PerceptronPredictionUtils
  108. val toLowercase: BooleanParam

    Indicates whether to convert all characters to lowercase before tokenizing (Default: false).

  109. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  110. final def transform(dataset: Dataset[_]): DataFrame

    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

    Dataset[Row]

    Definition Classes
    AnnotatorModel → Transformer
  111. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  112. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  113. final def transformSchema(schema: StructType): StructType

    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    RawAnnotator → PipelineStage
  114. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  115. val uid: String
    Definition Classes
    WordSegmenterModel → Identifiable
  116. 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
    RawAnnotator
  117. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  118. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  119. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  120. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  121. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from PerceptronPredictionUtils

Inherited from PerceptronUtils

Inherited from HasSimpleAnnotate[WordSegmenterModel]

Inherited from AnnotatorModel[WordSegmenterModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[WordSegmenterModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[WordSegmenterModel]

Inherited from Transformer

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

Members

Parameter setters

Parameter getters