c

com.johnsnowlabs.nlp.annotators.re

RelationExtractionApproach

class RelationExtractionApproach extends GenericClassifierApproach

Trains a TensorFlow model for relation extraction. The Tensorflow graph in .pb format needs to be specified with setModelFile. The result is a RelationExtractionModel. To start training, see the parameters that need to be set in the Parameters section.

Example

Defining pipeline stages to extract entities first

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

val tokenizer = new Tokenizer()
  .setInputCols(Array("document"))
  .setOutputCol("tokens")

val embedder = WordEmbeddingsModel
  .pretrained("embeddings_clinical", "en", "clinical/models")
  .setInputCols(Array("document", "tokens"))
  .setOutputCol("embeddings")

val posTagger = PerceptronModel
  .pretrained("pos_clinical", "en", "clinical/models")
  .setInputCols(Array("document", "tokens"))
  .setOutputCol("posTags")

val nerTagger = MedicalNerModel
  .pretrained("ner_events_clinical", "en", "clinical/models")
  .setInputCols(Array("document", "tokens", "embeddings"))
  .setOutputCol("ner_tags")

val nerConverter = new NerConverter()
  .setInputCols(Array("document", "tokens", "ner_tags"))
  .setOutputCol("nerChunks")

val depencyParser = DependencyParserModel
  .pretrained("dependency_conllu", "en")
  .setInputCols(Array("document", "posTags", "tokens"))
  .setOutputCol("dependencies")

Then define RelationExtractionApproach and training parameters

val re = new RelationExtractionApproach()
  .setInputCols(Array("embeddings", "posTags", "train_ner_chunks", "dependencies"))
  .setOutputCol("relations_t")
  .setLabelColumn("target_rel")
  .setEpochsNumber(300)
  .setBatchSize(200)
  .setlearningRate(0.001f)
  .setModelFile("path/to/graph_file.pb")
  .setFixImbalance(true)
  .setValidationSplit(0.05f)
  .setFromEntity("from_begin", "from_end", "from_label")
  .setToEntity("to_begin", "to_end", "to_label")

val finisher = new Finisher()
  .setInputCols(Array("relations_t"))
  .setOutputCols(Array("relations"))
  .setCleanAnnotations(false)
  .setValueSplitSymbol(",")
  .setAnnotationSplitSymbol(",")
  .setOutputAsArray(false)

Define complete pipeline and start training

val pipeline = new Pipeline()
  .setStages(Array(
    documentAssembler,
    tokenizer,
    embedder,
    posTagger,
    nerTagger,
    nerConverter,
    depencyParser,
    re,
    finisher))

val model = pipeline.fit(trainData)
See also

RelationExtractionModel for pretrained models and how to use it

Linear Supertypes
GenericClassifierApproach, Licensed, AnnotatorApproach[GenericClassifierModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[GenericClassifierModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. RelationExtractionApproach
  2. GenericClassifierApproach
  3. Licensed
  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
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

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

    uid

    a unique identifier for the instantiated AnnotatorModel

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]): GenericClassifierModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. val batchSize: IntParam

    Batch size

    Batch size

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

    Trains TensorFlow model for multi-class text classification

    Trains TensorFlow model for multi-class text classification

    Definition Classes
    RelationExtractionApproach → GenericClassifierApproach → AnnotatorApproach
  16. val dropout: FloatParam

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierApproach
  17. val epochsN: IntParam

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierApproach
  18. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  20. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  21. def explainParams(): String
    Definition Classes
    Params
  22. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  23. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  24. val featureScaling: Param[String]

    Feature scaling method.

    Feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

    Definition Classes
    GenericClassifierApproach
  25. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  26. final def fit(dataset: Dataset[_]): GenericClassifierModel
    Definition Classes
    AnnotatorApproach → Estimator
  27. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[GenericClassifierModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  28. def fit(dataset: Dataset[_], paramMap: ParamMap): GenericClassifierModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  29. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GenericClassifierModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  30. val fixImbalance: BooleanParam

    Fix the imbalance in the training set by replicating examples of under represented categories

    Fix the imbalance in the training set by replicating examples of under represented categories

    Definition Classes
    GenericClassifierApproach
  31. val fromEntityBeginCol: Param[String]

    Column for beginning of 'from' entity

  32. val fromEntityEndCol: Param[String]

    Column for end of 'from' entity

  33. val fromEntityLabelCol: Param[String]

    Column for 'from' entity label

  34. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  35. def getBatchSize: Int

    Batch size

    Batch size

    Definition Classes
    GenericClassifierApproach
  36. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  37. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  38. def getDropout: Float

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierApproach
  39. def getFeatureScaling: String

    Get feature scaling method

    Get feature scaling method

    Definition Classes
    GenericClassifierApproach
  40. def getFixImbalance: Boolean

    Fix imbalance in training set

    Fix imbalance in training set

    Definition Classes
    GenericClassifierApproach
  41. def getFromEntityBeginCol: String

    Column for beginning of 'from' entity

  42. def getFromEntityEndCol: String

    Column for end of 'from' entity

  43. def getFromEntityLabelCol: String

    Column for 'from' entity label

  44. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  45. def getLabelColumn: String

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierApproach
  46. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  47. def getLearningRate: Float

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierApproach
  48. def getMaxEpochs: Int

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierApproach
  49. def getModelFile: String

    Model file name

    Model file name

    Definition Classes
    GenericClassifierApproach
  50. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  51. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  52. def getOutputLogsPath: String

    Get output logs path

    Get output logs path

    Definition Classes
    GenericClassifierApproach
  53. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  54. def getToEntityBeginCol: String

    Column for beginning of 'to' entity

  55. def getToEntityEndCol: String

    Column for end of 'to' entity

  56. def getToEntityLabelCol: String

    Column for 'to' entity label

  57. def getValidationSplit: Float

    Choose the proportion of training dataset to be validated against the model on each Epoch.

    Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

    Definition Classes
    GenericClassifierApproach
  58. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  59. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  60. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  61. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  62. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : WORD_EMBEDDINGS, POS, CHUNK, DEPENDENCY

    Input annotator type : WORD_EMBEDDINGS, POS, CHUNK, DEPENDENCY

    Definition Classes
    RelationExtractionApproach → GenericClassifierApproach → HasInputAnnotationCols
  64. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  65. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  66. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  67. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  68. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  69. val labelColumn: Param[String]

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierApproach
  70. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  71. val learningRate: FloatParam

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierApproach
  72. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  73. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  80. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. val modelFile: Param[String]

    Location of file of the model used for classification

    Location of file of the model used for classification

    Definition Classes
    GenericClassifierApproach
  85. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  86. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  87. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  88. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  89. def onTrained(model: GenericClassifierModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  90. val outputAnnotatorType: String

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    GenericClassifierApproach → HasOutputAnnotatorType
  91. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  92. val outputLogsPath: Param[String]

    Path to folder to output logs.

    Path to folder to output logs. If no path is specified, no logs are generated

    Definition Classes
    GenericClassifierApproach
  93. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  94. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  95. final def set(paramPair: ParamPair[_]): RelationExtractionApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  96. final def set(param: String, value: Any): RelationExtractionApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  97. final def set[T](param: Param[T], value: T): RelationExtractionApproach.this.type
    Definition Classes
    Params
  98. def setBatchSize(batch: Int): RelationExtractionApproach.this.type

    Batch size

    Batch size

    Definition Classes
    GenericClassifierApproach
  99. final def setDefault(paramPairs: ParamPair[_]*): RelationExtractionApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  100. final def setDefault[T](param: Param[T], value: T): RelationExtractionApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  101. def setDropout(dropout: Float): RelationExtractionApproach.this.type

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierApproach
  102. def setEpochsNumber(epochs: Int): RelationExtractionApproach.this.type

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierApproach
  103. def setFeatureScaling(featureScaling: String): RelationExtractionApproach.this.type

    Set the feature scaling method.

    Set the feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

    Definition Classes
    GenericClassifierApproach
  104. def setFixImbalance(fix: Boolean): RelationExtractionApproach.this.type

    Fix imbalance of training set

    Fix imbalance of training set

    Definition Classes
    GenericClassifierApproach
  105. def setFromEntity(beginCol: String, endCol: String, labelCol: String): RelationExtractionApproach.this.type

    Set from entity

  106. final def setInputCols(value: String*): RelationExtractionApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  107. final def setInputCols(value: Array[String]): RelationExtractionApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  108. def setLabelColumn(column: String): RelationExtractionApproach.this.type

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierApproach
  109. def setLazyAnnotator(value: Boolean): RelationExtractionApproach.this.type
    Definition Classes
    CanBeLazy
  110. def setModelFile(modelFile: String): RelationExtractionApproach.this.type

    Set the model file name

    Set the model file name

    Definition Classes
    GenericClassifierApproach
  111. final def setOutputCol(value: String): RelationExtractionApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  112. def setOutputLogsPath(outputLogsPath: String): RelationExtractionApproach.this.type

    Set the output log path

    Set the output log path

    Definition Classes
    GenericClassifierApproach
  113. def setToEntity(beginCol: String, endCol: String, labelCol: String): RelationExtractionApproach.this.type

    Set to entity

  114. def setValidationSplit(validationSplit: Float): RelationExtractionApproach.this.type

    Choose the proportion of training dataset to be validated against the model on each Epoch.

    Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

    Definition Classes
    GenericClassifierApproach
  115. def setlearningRate(lr: Float): RelationExtractionApproach.this.type

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierApproach
  116. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  117. val toEntityBeginCol: Param[String]

    Column for beginning of 'to' entity

  118. val toEntityEndCol: Param[String]

    Column for end of 'to' entity

  119. val toEntityLabelCol: Param[String]

    Column for 'to' entity label

  120. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  121. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): RelationExtractionModel
    Definition Classes
    RelationExtractionApproach → GenericClassifierApproach → AnnotatorApproach
  122. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  123. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  124. val uid: String
    Definition Classes
    RelationExtractionApproach → GenericClassifierApproach → Identifiable
  125. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  126. val validationSplit: FloatParam

    Choose the proportion of training dataset to be validated against the model on each Epoch.

    Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

    Definition Classes
    GenericClassifierApproach
  127. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  128. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  129. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  130. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from GenericClassifierApproach

Inherited from Licensed

Inherited from AnnotatorApproach[GenericClassifierModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[GenericClassifierModel]

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

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters