c

com.johnsnowlabs.nlp.annotators.resolution

SentenceEntityResolverApproach

class SentenceEntityResolverApproach extends AnnotatorApproach[SentenceEntityResolverModel] with SentenceResolverParams with HasCaseSensitiveProperties with Licensed

Contains all the parameters and methods to train a SentenceEntityResolverModel. The model transforms a dataset with Input Annotation type SENTENCE_EMBEDDINGS, coming from e.g. BertSentenceEmbeddings and returns the normalized entity for a particular trained ontology / curated dataset. (e.g. ICD-10, RxNorm, SNOMED etc.)

To use pretrained models please use SentenceEntityResolverModel and see the Models Hub for available models.

Example

Training a SNOMED resolution model using BERT sentence embeddings

Define pre-processing pipeline for training data. It needs consists of columns for the normalized training data and their labels.

val documentAssembler = new DocumentAssembler()
   .setInputCol("normalized_text")
   .setOutputCol("document")
 val bertEmbeddings = BertSentenceEmbeddings.pretrained("sent_biobert_pubmed_base_cased")
   .setInputCols("sentence")
   .setOutputCol("bert_embeddings")
 val snomedTrainingPipeline = new Pipeline().setStages(Array(
   documentAssembler,
   bertEmbeddings
 ))
 val snomedTrainingModel = snomedTrainingPipeline.fit(data)
 val snomedData = snomedTrainingModel.transform(data).cache()

Then the Resolver can be trained with

val bertExtractor = new SentenceEntityResolverApproach()
  .setNeighbours(25)
  .setThreshold(1000)
  .setInputCols("bert_embeddings")
  .setNormalizedCol("normalized_text")
  .setLabelCol("label")
  .setOutputCol("snomed_code")
  .setDistanceFunction("EUCLIDIAN")
  .setCaseSensitive(false)

val snomedModel = bertExtractor.fit(snomedData)
See also

SentenceEntityResolverModel

Linear Supertypes
Licensed, HasCaseSensitiveProperties, ParamsAndFeaturesWritable, HasFeatures, SentenceResolverParams, AnnotatorApproach[SentenceEntityResolverModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[SentenceEntityResolverModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. SentenceEntityResolverApproach
  2. Licensed
  3. HasCaseSensitiveProperties
  4. ParamsAndFeaturesWritable
  5. HasFeatures
  6. SentenceResolverParams
  7. AnnotatorApproach
  8. CanBeLazy
  9. DefaultParamsWritable
  10. MLWritable
  11. HasOutputAnnotatorType
  12. HasOutputAnnotationCol
  13. HasInputAnnotationCols
  14. Estimator
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new SentenceEntityResolverApproach()
  2. new SentenceEntityResolverApproach(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. 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 _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): SentenceEntityResolverModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  10. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  11. val auxLabelCol: Param[String]

    Optional column with one extra label per document.

    Optional column with one extra label per document. This extra label will be outputted later on in an additional column (Default: "aux_label")

  12. val auxLabelMap: StructFeature[Map[String, String]]
  13. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  14. val caseSensitive: BooleanParam
    Definition Classes
    HasCaseSensitiveProperties
  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. final def clear(param: Param[_]): SentenceEntityResolverApproach.this.type
    Definition Classes
    Params
  17. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  18. val confidenceFunction: Param[String]
    Definition Classes
    SentenceResolverParams
  19. final def copy(extra: ParamMap): Estimator[SentenceEntityResolverModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  20. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  21. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  22. val description: String
    Definition Classes
    SentenceEntityResolverApproach → AnnotatorApproach
  23. val distanceFunction: Param[String]

    what distance function to use for KNN: 'EUCLIDEAN' or 'COSINE'

    what distance function to use for KNN: 'EUCLIDEAN' or 'COSINE'

    Definition Classes
    SentenceResolverParams
  24. lazy val embeddingsColumnName: String
  25. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  26. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  27. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  28. def explainParams(): String
    Definition Classes
    Params
  29. def extractAuxLabelMap(dataset: Dataset[_]): Map[String, String]

    Extracts a Map[String,String] where key=label and value=auxLabel from a dataset.

    Extracts a Map[String,String] where key=label and value=auxLabel from a dataset. If either of one columns does not exist, it will return an empty map

    dataset

    from which we extract the column

    returns

    a Map[String,String]

  30. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  31. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  32. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  33. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  34. final def fit(dataset: Dataset[_]): SentenceEntityResolverModel
    Definition Classes
    AnnotatorApproach → Estimator
  35. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[SentenceEntityResolverModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  36. def fit(dataset: Dataset[_], paramMap: ParamMap): SentenceEntityResolverModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  37. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): SentenceEntityResolverModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  38. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  39. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  40. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  41. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  42. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  43. def getAuxLabelCol(): Option[String]

    Optional column with one extra label per document.

    Optional column with one extra label per document. This extra label will be outputted later on in an additional column

  44. def getAuxLabelMap(): Map[String, String]

    Map[String,String] where key=label and value=auxLabel from a dataset.

  45. def getCaseSensitive: Boolean
    Definition Classes
    HasCaseSensitiveProperties
  46. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  47. def getConfidenceFunction: String
    Definition Classes
    SentenceResolverParams
  48. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  49. def getDistanceFunction: String
    Definition Classes
    SentenceResolverParams
  50. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  51. def getLabelCol: String

    column name for the value we are trying to resolve

  52. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  53. def getMissAsEmpty: Boolean
    Definition Classes
    SentenceResolverParams
  54. def getNeighbours: Int
    Definition Classes
    SentenceResolverParams
  55. def getNormalizedCol: String

    column name for the original, normalized description

  56. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  57. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  58. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  59. def getReturnAllKEmbeddings(): Boolean

    Whether to return all embeddings of all K candidates of the resolution.

    Whether to return all embeddings of all K candidates of the resolution. Embeddings will be in the metadata. Increase in RAM usage to be expected

  60. def getReturnCosineDistances: Boolean

    Whether to calculate and return cosine distances between a sentence and the k closest candidates.

    Whether to calculate and return cosine distances between a sentence and the k closest candidates. Can improve accuracy but increases computation.

  61. def getThreshold: Double
    Definition Classes
    SentenceResolverParams
  62. def getUseAuxLabel(): Boolean

    Whether to use Aux Label or not

  63. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  64. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  65. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  66. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  67. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. val inputAnnotatorTypes: Array[String]

    Input annotator types: SENTENCE_EMBEDDINGS

    Input annotator types: SENTENCE_EMBEDDINGS

    Definition Classes
    SentenceEntityResolverApproach → HasInputAnnotationCols
  69. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  70. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  71. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  72. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  73. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  74. val labelCol: Param[String]

    column name for the value we are trying to resolve (Default: "code")

  75. lazy val labelColumnName: String
  76. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  77. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  78. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  85. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  88. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  89. val missAsEmpty: BooleanParam

    whether or not to return an empty annotation on unmatched chunks

    whether or not to return an empty annotation on unmatched chunks

    Definition Classes
    SentenceResolverParams
  90. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  91. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  92. val neighbours: IntParam

    number of neighbours to consider in the KNN query to calculate WMD

    number of neighbours to consider in the KNN query to calculate WMD

    Definition Classes
    SentenceResolverParams
  93. val normalizedCol: Param[String]

    column name for the original, normalized description

  94. lazy val normalizedColumnName: String
  95. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  96. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  97. def onTrained(model: SentenceEntityResolverModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  98. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  99. val outputAnnotatorType: AnnotatorType

    Output annotator types: ENTITY

    Output annotator types: ENTITY

    Definition Classes
    SentenceEntityResolverApproach → HasOutputAnnotatorType
  100. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  101. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  102. val returnAllKEmbeddings: BooleanParam

    Whether to return all embeddings of all K candidates of the resolution.

    Whether to return all embeddings of all K candidates of the resolution. Embeddings will be in the metadata. Increase in RAM usage to be expected (Default: false)

  103. val returnCosineDistances: BooleanParam

    Whether to calculate and return cosine distances between a sentence and the k closest candidates.

    Whether to calculate and return cosine distances between a sentence and the k closest candidates. Can improve accuracy but increases computation (Default: true)

  104. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  105. def set[T](feature: StructFeature[T], value: T): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  106. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  107. def set[T](feature: SetFeature[T], value: Set[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  108. def set[T](feature: ArrayFeature[T], value: Array[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  109. final def set(paramPair: ParamPair[_]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  110. final def set(param: String, value: Any): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  111. final def set[T](param: Param[T], value: T): SentenceEntityResolverApproach.this.type
    Definition Classes
    Params
  112. def setAuxLabelCol(c: String): SentenceEntityResolverApproach.this.type

    Optional column with one extra label per document.

    Optional column with one extra label per document. This extra label will be outputted later on in an additional column

  113. def setAuxLabelMap(m: Map[String, String]): SentenceEntityResolverApproach.this.type

    Map[String,String] where key=label and value=auxLabel from a dataset.

  114. def setCaseSensitive(value: Boolean): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasCaseSensitiveProperties
  115. def setConfidenceFunction(v: String): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  116. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  117. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  118. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  119. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  120. final def setDefault(paramPairs: ParamPair[_]*): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  121. final def setDefault[T](param: Param[T], value: T): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  122. def setDistanceFunction(value: String): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  123. final def setInputCols(value: String*): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  124. final def setInputCols(value: Array[String]): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  125. def setLabelCol(value: String): SentenceEntityResolverApproach.this.type

    column name for the value we are trying to resolve

  126. def setLazyAnnotator(value: Boolean): SentenceEntityResolverApproach.this.type
    Definition Classes
    CanBeLazy
  127. def setMissAsEmpty(v: Boolean): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  128. def setNeighbours(k: Int): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  129. def setNormalizedCol(value: String): SentenceEntityResolverApproach.this.type

    column name for the original, normalized description

  130. final def setOutputCol(value: String): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  131. def setReturnAllKEmbeddings(b: Boolean): SentenceEntityResolverApproach.this.type

    Whether to return all embeddings of all K candidates of the resolution.

    Whether to return all embeddings of all K candidates of the resolution. Embeddings will be in the metadata. Increase in RAM usage to be expected

  132. def setReturnCosineDistances(value: Boolean): SentenceEntityResolverApproach.this.type

    Whether to calculate and return cosine distances between a sentence and the k closest candidates.

    Whether to calculate and return cosine distances between a sentence and the k closest candidates. Can improve accuracy but increases computation.

  133. def setThreshold(dist: Double): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  134. def setUseAuxLabel(b: Boolean): SentenceEntityResolverApproach.this.type

    Whether to use Aux Label or not

  135. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  136. val threshold: DoubleParam

    threshold value for the aggregated distance

    threshold value for the aggregated distance

    Definition Classes
    SentenceResolverParams
  137. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  138. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): SentenceEntityResolverModel

    Returns the SentenceEntityResolverModel Transformer, that can be used to transform input datasets

    Returns the SentenceEntityResolverModel Transformer, that can be used to transform input datasets

    The dataset provided to the fit method should have one sentence per row and contain the following columns: SentenceEmbeddings, ResolverLabel, ResolverNormalized

    The cardinality of the dataset should not exceed 100.000 data points since searching in such a big KD-tree becomes impractical

    This method is called inside the AnnotatorApproach's fit method

    dataset

    a Dataset containing SentenceEmbeddings, ResolverLabel, ResolverNormalized

    returns

    a trained SentenceEntityResolverModel

    Definition Classes
    SentenceEntityResolverApproach → AnnotatorApproach
  139. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  140. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  141. val uid: String
    Definition Classes
    SentenceEntityResolverApproach → Identifiable
  142. val useAuxLabel: BooleanParam

    Whether to use Aux Label or not (Default: false)

  143. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  144. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  145. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  146. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  147. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from Licensed

Inherited from HasCaseSensitiveProperties

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from SentenceResolverParams

Inherited from AnnotatorApproach[SentenceEntityResolverModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[SentenceEntityResolverModel]

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