c

com.johnsnowlabs.finance.chunk_classification.resolution

SentenceEntityResolverApproach

class SentenceEntityResolverApproach extends nlp.annotators.resolution.SentenceEntityResolverApproach

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
nlp.annotators.resolution.SentenceEntityResolverApproach, CheckLicense, HandleExceptionParams, HasCaseSensitiveProperties, ParamsAndFeaturesWritable, HasFeatures, SentenceResolverParams, AnnotatorApproach[nlp.annotators.resolution.SentenceEntityResolverModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[nlp.annotators.resolution.SentenceEntityResolverModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. SentenceEntityResolverApproach
  2. SentenceEntityResolverApproach
  3. CheckLicense
  4. HandleExceptionParams
  5. HasCaseSensitiveProperties
  6. ParamsAndFeaturesWritable
  7. HasFeatures
  8. SentenceResolverParams
  9. AnnotatorApproach
  10. CanBeLazy
  11. DefaultParamsWritable
  12. MLWritable
  13. HasOutputAnnotatorType
  14. HasOutputAnnotationCol
  15. HasInputAnnotationCols
  16. Estimator
  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 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]): nlp.annotators.resolution.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")

    Definition Classes
    SentenceEntityResolverApproach
  12. val auxLabelMap: StructFeature[Map[String, String]]
    Definition Classes
    SentenceEntityResolverApproach
  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. def checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String]): Unit
    Definition Classes
    CheckLicense
  17. def checkValidScope(scope: String): Unit
    Definition Classes
    CheckLicense
  18. def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  19. def checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  20. final def clear(param: Param[_]): SentenceEntityResolverApproach.this.type
    Definition Classes
    Params
  21. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  22. val confidenceFunction: Param[String]
    Definition Classes
    SentenceResolverParams
  23. final def copy(extra: ParamMap): Estimator[nlp.annotators.resolution.SentenceEntityResolverModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  24. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. val datasetInfo: Param[String]

    Descriptive information about the dataset being used.

    Descriptive information about the dataset being used.

    Definition Classes
    SentenceResolverParams
  26. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  27. val description: String
    Definition Classes
    SentenceEntityResolverApproach → AnnotatorApproach
  28. 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
  29. val doExceptionHandling: BooleanParam

    If true, exceptions are handled.

    If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

    Definition Classes
    HandleExceptionParams
  30. val dropCodesList: StringArrayParam

    List of codes in a pretrained model to leave out when continue training with new data.

    List of codes in a pretrained model to leave out when continue training with new data.

    Definition Classes
    SentenceEntityResolverApproach
  31. lazy val embeddingsColumnName: String
    Definition Classes
    SentenceEntityResolverApproach
  32. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  33. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  34. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  35. def explainParams(): String
    Definition Classes
    Params
  36. 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]

    Definition Classes
    SentenceEntityResolverApproach
  37. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  38. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  39. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  40. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  41. final def fit(dataset: Dataset[_]): nlp.annotators.resolution.SentenceEntityResolverModel
    Definition Classes
    AnnotatorApproach → Estimator
  42. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[nlp.annotators.resolution.SentenceEntityResolverModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  43. def fit(dataset: Dataset[_], paramMap: ParamMap): nlp.annotators.resolution.SentenceEntityResolverModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  44. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): nlp.annotators.resolution.SentenceEntityResolverModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  45. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  46. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  47. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  48. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  49. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  50. 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

    Definition Classes
    SentenceEntityResolverApproach
  51. def getAuxLabelMap(): Map[String, String]

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

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

    Definition Classes
    SentenceEntityResolverApproach
  52. def getCaseSensitive: Boolean
    Definition Classes
    HasCaseSensitiveProperties
  53. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  54. def getConfidenceFunction: String
    Definition Classes
    SentenceResolverParams
  55. def getDatasetInfo: String

    get descriptive information about the dataset being used

    get descriptive information about the dataset being used

    Definition Classes
    SentenceResolverParams
  56. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  57. def getDistanceFunction: String
    Definition Classes
    SentenceResolverParams
  58. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  59. def getLabelCol: String

    column name for the value we are trying to resolve

    column name for the value we are trying to resolve

    Definition Classes
    SentenceEntityResolverApproach
  60. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  61. def getMissAsEmpty: Boolean
    Definition Classes
    SentenceResolverParams
  62. def getNeighbours: Int
    Definition Classes
    SentenceResolverParams
  63. def getNormalizedCol: String

    column name for the original, normalized description

    column name for the original, normalized description

    Definition Classes
    SentenceEntityResolverApproach
  64. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  65. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  66. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  67. 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

    Definition Classes
    SentenceEntityResolverApproach
  68. 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.

    Definition Classes
    SentenceEntityResolverApproach
  69. def getThreshold: Double
    Definition Classes
    SentenceResolverParams
  70. def getUseAuxLabel(): Boolean

    Whether to use Aux Label or not

    Whether to use Aux Label or not

    Definition Classes
    SentenceEntityResolverApproach
  71. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  72. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  73. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  74. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  75. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. val inputAnnotatorTypes: Array[String]

    Input annotator types: SENTENCE_EMBEDDINGS

    Input annotator types: SENTENCE_EMBEDDINGS

    Definition Classes
    SentenceEntityResolverApproach → HasInputAnnotationCols
  77. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  78. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  79. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  80. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  81. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  82. val labelCol: Param[String]

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

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

    Definition Classes
    SentenceEntityResolverApproach
  83. lazy val labelColumnName: String
    Definition Classes
    SentenceEntityResolverApproach
  84. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  85. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  86. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  88. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  89. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  91. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  92. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  93. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  94. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  95. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  96. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  97. 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
  98. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  99. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  100. 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
  101. val normalizedCol: Param[String]

    column name for the original, normalized description

    column name for the original, normalized description

    Definition Classes
    SentenceEntityResolverApproach
  102. lazy val normalizedColumnName: String
    Definition Classes
    SentenceEntityResolverApproach
  103. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  104. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  105. def onTrained(model: nlp.annotators.resolution.SentenceEntityResolverModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  106. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  107. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  108. val outputAnnotatorType: AnnotatorType

    Output annotator types: ENTITY

    Output annotator types: ENTITY

    Definition Classes
    SentenceEntityResolverApproach → HasOutputAnnotatorType
  109. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  110. val overrideExistingCodes: BooleanParam

    Whether to override the existing codes with new data while continue the training from a pretrained model.

    Whether to override the existing codes with new data while continue the training from a pretrained model. Default value is false(keep all the codes).

    Definition Classes
    SentenceEntityResolverApproach
  111. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  112. val pretrainedModelPath: Param[String]

    Path to an already trained SentenceEntityResolverModel.

    Path to an already trained SentenceEntityResolverModel.

    This pretrained model will be used as a starting point for training the new one. The path can be a local file path, a distributed file path (HDFS, DBFS), or a cloud storage (S3).

    Definition Classes
    SentenceEntityResolverApproach
  113. 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)

    Definition Classes
    SentenceEntityResolverApproach
  114. 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)

    Definition Classes
    SentenceEntityResolverApproach
  115. val returnResolvedTextEmbeddings: BooleanParam

    Whether to include embeddings for resolved text embeddings.(Default : false)

    Whether to include embeddings for resolved text embeddings.(Default : false)

    Definition Classes
    SentenceEntityResolverApproach
  116. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  117. def set[T](feature: StructFeature[T], value: T): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  118. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  119. def set[T](feature: SetFeature[T], value: Set[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  120. def set[T](feature: ArrayFeature[T], value: Array[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  121. final def set(paramPair: ParamPair[_]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  122. final def set(param: String, value: Any): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  123. final def set[T](param: Param[T], value: T): SentenceEntityResolverApproach.this.type
    Definition Classes
    Params
  124. 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

    Definition Classes
    SentenceEntityResolverApproach
  125. def setAuxLabelMap(m: Map[String, String]): SentenceEntityResolverApproach.this.type

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

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

    Definition Classes
    SentenceEntityResolverApproach
  126. def setCaseSensitive(value: Boolean): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasCaseSensitiveProperties
  127. def setConfidenceFunction(v: String): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  128. def setDatasetInfo(value: String): SentenceEntityResolverApproach.this.type

    set descriptive information about the dataset being used

    set descriptive information about the dataset being used

    Definition Classes
    SentenceResolverParams
  129. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  130. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  131. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  132. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  133. final def setDefault(paramPairs: ParamPair[_]*): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  134. final def setDefault[T](param: Param[T], value: T): SentenceEntityResolverApproach.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  135. def setDistanceFunction(value: String): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  136. def setDoExceptionHandling(value: Boolean): SentenceEntityResolverApproach.this.type

    If true, exceptions are handled.

    If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

    Definition Classes
    HandleExceptionParams
  137. def setDropCodesList(v: Array[String]): SentenceEntityResolverApproach.this.type

    Sets a list of codes that will be left out in a pretrained model when continue training with new data.

    Sets a list of codes that will be left out in a pretrained model when continue training with new data.

    Definition Classes
    SentenceEntityResolverApproach
  138. final def setInputCols(value: String*): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  139. def setInputCols(value: Array[String]): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  140. def setLabelCol(value: String): SentenceEntityResolverApproach.this.type

    column name for the value we are trying to resolve

    column name for the value we are trying to resolve

    Definition Classes
    SentenceEntityResolverApproach
  141. def setLazyAnnotator(value: Boolean): SentenceEntityResolverApproach.this.type
    Definition Classes
    CanBeLazy
  142. def setMissAsEmpty(v: Boolean): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  143. def setNeighbours(k: Int): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  144. def setNormalizedCol(value: String): SentenceEntityResolverApproach.this.type

    column name for the original, normalized description

    column name for the original, normalized description

    Definition Classes
    SentenceEntityResolverApproach
  145. final def setOutputCol(value: String): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  146. def setOverrideExistingCodes(v: Boolean): SentenceEntityResolverApproach.this.type

    Whether to override the existing codes with new data while continue the training from a pretrained model.

    Whether to override the existing codes with new data while continue the training from a pretrained model. Default value is false(keep all the codes).

    Definition Classes
    SentenceEntityResolverApproach
  147. def setPretrainedModelPath(path: String): SentenceEntityResolverApproach.this.type

    Set the location of an already trained SentenceEntityResolverModel, which is used as a starting point for training the new model.

    Set the location of an already trained SentenceEntityResolverModel, which is used as a starting point for training the new model.

    Definition Classes
    SentenceEntityResolverApproach
  148. 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

    Definition Classes
    SentenceEntityResolverApproach
  149. 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.

    Definition Classes
    SentenceEntityResolverApproach
  150. def setReturnResolvedTextEmbeddings(value: Boolean): SentenceEntityResolverApproach.this.type

    Whether to include embeddings for resolved text embeddings.(Default : false)

    Whether to include embeddings for resolved text embeddings.(Default : false)

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

    Whether to use Aux Label or not

    Whether to use Aux Label or not

    Definition Classes
    SentenceEntityResolverApproach
  153. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  154. val threshold: DoubleParam

    threshold value for the aggregated distance

    threshold value for the aggregated distance

    Definition Classes
    SentenceResolverParams
  155. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  156. 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 → SentenceEntityResolverApproach → AnnotatorApproach
  157. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  158. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  159. val uid: String
    Definition Classes
    SentenceEntityResolverApproach → SentenceEntityResolverApproach → Identifiable
  160. val useAuxLabel: BooleanParam

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

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

    Definition Classes
    SentenceEntityResolverApproach
  161. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  162. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  163. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  164. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  165. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from nlp.annotators.resolution.SentenceEntityResolverApproach

Inherited from CheckLicense

Inherited from HandleExceptionParams

Inherited from HasCaseSensitiveProperties

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from SentenceResolverParams

Inherited from AnnotatorApproach[nlp.annotators.resolution.SentenceEntityResolverModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[nlp.annotators.resolution.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