class PerceptronApproach extends AnnotatorApproach[PerceptronModel] with PerceptronTrainingUtils

Trains an averaged Perceptron model to tag words part-of-speech. Sets a POS tag to each word within a sentence.

For pretrained models please see the PerceptronModel.

The training data needs to be in a Spark DataFrame, where the column needs to consist of Annotations of type POS. The Annotation needs to have member result set to the POS tag and have a "word" mapping to its word inside of member metadata. This DataFrame for training can easily created by the helper class POS.

POS().readDataset(spark, datasetPath).selectExpr("explode(tags) as tags").show(false)
+---------------------------------------------+
|tags                                         |
+---------------------------------------------+
|[pos, 0, 5, NNP, [word -> Pierre], []]       |
|[pos, 7, 12, NNP, [word -> Vinken], []]      |
|[pos, 14, 14, ,, [word -> ,], []]            |
|[pos, 31, 34, MD, [word -> will], []]        |
|[pos, 36, 39, VB, [word -> join], []]        |
|[pos, 41, 43, DT, [word -> the], []]         |
|[pos, 45, 49, NN, [word -> board], []]       |
                      ...

For extended examples of usage, see the Examples and PerceptronApproach tests.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.SentenceDetector
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.training.POS
import com.johnsnowlabs.nlp.annotators.pos.perceptron.PerceptronApproach
import org.apache.spark.ml.Pipeline

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

val sentence = new SentenceDetector()
  .setInputCols("document")
  .setOutputCol("sentence")

val tokenizer = new Tokenizer()
  .setInputCols("sentence")
  .setOutputCol("token")

val datasetPath = "src/test/resources/anc-pos-corpus-small/test-training.txt"
val trainingPerceptronDF = POS().readDataset(spark, datasetPath)

val trainedPos = new PerceptronApproach()
  .setInputCols("document", "token")
  .setOutputCol("pos")
  .setPosColumn("tags")
  .fit(trainingPerceptronDF)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  sentence,
  tokenizer,
  trainedPos
))

val data = Seq("To be or not to be, is this the question?").toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("pos.result").show(false)
+--------------------------------------------------+
|result                                            |
+--------------------------------------------------+
|[NNP, NNP, CD, JJ, NNP, NNP, ,, MD, VB, DT, CD, .]|
+--------------------------------------------------+
Linear Supertypes
PerceptronTrainingUtils, PerceptronUtils, AnnotatorApproach[PerceptronModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[PerceptronModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. PerceptronApproach
  2. PerceptronTrainingUtils
  3. PerceptronUtils
  4. AnnotatorApproach
  5. CanBeLazy
  6. DefaultParamsWritable
  7. MLWritable
  8. HasOutputAnnotatorType
  9. HasOutputAnnotationCol
  10. HasInputAnnotationCols
  11. Estimator
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new PerceptronApproach()
  2. new PerceptronApproach(uid: String)

    uid

    internal uid required to generate writable annotators

Type Members

  1. 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. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): PerceptronModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  6. val ambiguityThreshold: DoubleParam

    How much percentage of total amount of words are covered to be marked as frequent (Default: 0.97)

  7. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  8. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  9. def buildTagBook(taggedSentences: Array[TaggedSentence], frequencyThreshold: Int, ambiguityThreshold: Double): Map[String, String]

    Finds very frequent tags on a word in training, and marks them as non ambiguous based on tune parameters ToDo: Move such parameters to configuration

    Finds very frequent tags on a word in training, and marks them as non ambiguous based on tune parameters ToDo: Move such parameters to configuration

    taggedSentences

    Takes entire tagged sentences to find frequent tags

    frequencyThreshold

    How many times at least a tag on a word to be marked as frequent

    ambiguityThreshold

    How much percentage of total amount of words are covered to be marked as frequent

    Definition Classes
    PerceptronTrainingUtils
  10. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  11. final def clear(param: Param[_]): PerceptronApproach.this.type
    Definition Classes
    Params
  12. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  13. final def copy(extra: ParamMap): Estimator[PerceptronModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  14. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  15. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  16. val description: String

    Averaged Perceptron model to tag words part-of-speech

    Averaged Perceptron model to tag words part-of-speech

    Definition Classes
    PerceptronApproach → AnnotatorApproach
  17. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  18. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  19. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  20. def explainParams(): String
    Definition Classes
    Params
  21. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  22. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  23. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. final def fit(dataset: Dataset[_]): PerceptronModel
    Definition Classes
    AnnotatorApproach → Estimator
  25. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[PerceptronModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  26. def fit(dataset: Dataset[_], paramMap: ParamMap): PerceptronModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  27. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): PerceptronModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  28. val frequencyThreshold: IntParam

    How many times at least a tag on a word to be marked as frequent (Default: 20)

  29. def generatesTagBook(dataset: Dataset[_]): Array[TaggedSentence]

    Generates TagBook, which holds all the word to tags mapping that are not ambiguous

    Generates TagBook, which holds all the word to tags mapping that are not ambiguous

    Definition Classes
    PerceptronTrainingUtils
  30. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  31. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  32. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  33. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  34. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  35. def getNIterations: Int

    Number of iterations for training.

    Number of iterations for training. May improve accuracy but takes longer (Default: 5)

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  38. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  39. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  40. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  41. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  42. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  43. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  44. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type: TOKEN, DOCUMENT

    Input annotator type: TOKEN, DOCUMENT

    Definition Classes
    PerceptronApproach → HasInputAnnotationCols
  45. 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
  46. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  47. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  48. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  49. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  50. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  51. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  52. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  59. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  64. val nIterations: IntParam

    Number of iterations in training, converges to better accuracy (Default: 5)

  65. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  66. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  67. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  68. def onTrained(model: PerceptronModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  69. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  70. val outputAnnotatorType: AnnotatorType

    Output annotator type: POS

    Output annotator type: POS

    Definition Classes
    PerceptronApproach → HasOutputAnnotatorType
  71. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  72. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  73. val posCol: Param[String]

    Column of Array of POS tags that match tokens

  74. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  75. final def set(paramPair: ParamPair[_]): PerceptronApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  76. final def set(param: String, value: Any): PerceptronApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  77. final def set[T](param: Param[T], value: T): PerceptronApproach.this.type
    Definition Classes
    Params
  78. def setAmbiguityThreshold(value: Double): PerceptronApproach.this.type

    "How much percentage of total amount of words are covered to be marked as frequent

  79. final def setDefault(paramPairs: ParamPair[_]*): PerceptronApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  80. final def setDefault[T](param: Param[T], value: T): PerceptronApproach.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  81. def setFrequencyThreshold(value: Int): PerceptronApproach.this.type

    "How many times at least a tag on a word to be marked as frequent

  82. final def setInputCols(value: String*): PerceptronApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  83. def setInputCols(value: Array[String]): PerceptronApproach.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  84. def setLazyAnnotator(value: Boolean): PerceptronApproach.this.type
    Definition Classes
    CanBeLazy
  85. def setNIterations(value: Int): PerceptronApproach.this.type

    Number of iterations for training.

    Number of iterations for training. May improve accuracy but takes longer. Default 5.

  86. final def setOutputCol(value: String): PerceptronApproach.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  87. def setPosColumn(value: String): PerceptronApproach.this.type

    Column containing an array of POS Tags matching every token on the line.

  88. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  89. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  90. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): PerceptronModel

    Trains a model based on a provided CORPUS

    Trains a model based on a provided CORPUS

    returns

    A trained averaged model

    Definition Classes
    PerceptronApproach → AnnotatorApproach
  91. def trainPerceptron(nIterations: Int, initialModel: TrainingPerceptronLegacy, taggedSentences: Array[TaggedSentence], taggedWordBook: Map[String, String]): AveragedPerceptron

    Iterates for training

    Iterates for training

    Definition Classes
    PerceptronTrainingUtils
  92. 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
  93. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  94. val uid: String
    Definition Classes
    PerceptronApproach → Identifiable
  95. 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
  96. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  97. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  98. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  99. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from PerceptronTrainingUtils

Inherited from PerceptronUtils

Inherited from AnnotatorApproach[PerceptronModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[PerceptronModel]

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