class NerChunker extends AnnotatorModel[NerChunker] with HasSimpleAnnotate[NerChunker]
Extracts phrases that fits into a known pattern using the NER tags. Useful for entity groups with neighboring tokens when there is no pretrained NER model to address certain issues. A Regex needs to be provided to extract the tokens between entities.
Example
Defining pipeline stages for NER
val data= Seq("She has cystic cyst on her kidney.").toDF("text") val documentAssembler=new DocumentAssembler() .setInputCol("text") .setOutputCol("document") val sentenceDetector=new SentenceDetector() .setInputCols("document") .setOutputCol("sentence") .setUseAbbreviations(false) val tokenizer=new Tokenizer() .setInputCols(Array("sentence")) .setOutputCol("token") val embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models") .setInputCols("sentence","token") .setOutputCol("embeddings") .setCaseSensitive(false) val ner = MedicalNerModel.pretrained("ner_radiology", "en", "clinical/models") .setInputCols("sentence","token","embeddings") .setOutputCol("ner") .setIncludeConfidence(true)
Define the NerChunker to combine to chunks
val chunker = new NerChunker() .setInputCols(Array("sentence","ner")) .setOutputCol("ner_chunk") .setRegexParsers(Array("<ImagingFindings>.*<BodyPart>")) val pipeline=new Pipeline().setStages(Array( documentAssembler, sentenceDetector, tokenizer, embeddings, ner, chunker )) val result = pipeline.fit(data).transform(data)
Show results:
result.selectExpr("explode(arrays_zip(ner.metadata , ner.result))") .selectExpr("col['0'].word as word" , "col['1'] as ner").show(truncate=false) +------+-----------------+ |word |ner | +------+-----------------+ |She |O | |has |O | |cystic|B-ImagingFindings| |cyst |I-ImagingFindings| |on |O | |her |O | |kidney|B-BodyPart | |. |O | +------+-----------------+ result.select("ner_chunk.result").show(truncate=false) +---------------------------+ |result | +---------------------------+ |[cystic cyst on her kidney]| +---------------------------+
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def
_transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
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def
addRegexParser(value: String): NerChunker
adds a pattern to the current list of chunk patterns, for example: “‹DT›?‹JJ›*‹NN›”
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def
afterAnnotate(dataset: DataFrame): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
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def
annotate(annotations: Seq[Annotation]): Seq[Annotation]
- Definition Classes
- NerChunker → HasSimpleAnnotate
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final
def
asInstanceOf[T0]: T0
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def
beforeAnnotate(dataset: Dataset[_]): Dataset[_]
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final
def
checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
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- HasInputAnnotationCols
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final
def
clear(param: Param[_]): NerChunker.this.type
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def
clone(): AnyRef
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- protected[lang]
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- @throws( ... ) @native()
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def
copy(extra: ParamMap): NerChunker
- Definition Classes
- RawAnnotator → Model → Transformer → PipelineStage → Params
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def
copyValues[T <: Params](to: T, extra: ParamMap): T
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final
def
defaultCopy[T <: Params](extra: ParamMap): T
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def
dfAnnotate: UserDefinedFunction
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eq(arg0: AnyRef): Boolean
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equals(arg0: Any): Boolean
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explainParam(param: Param[_]): String
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def
explainParams(): String
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def
extraValidate(structType: StructType): Boolean
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def
extraValidateMsg: String
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def
extractParamMap(): ParamMap
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def
extractParamMap(extra: ParamMap): ParamMap
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features: ArrayBuffer[Feature[_, _, _]]
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finalize(): Unit
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def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
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def
getInputCols: Array[String]
- Definition Classes
- HasInputAnnotationCols
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def
getLazyAnnotator: Boolean
- Definition Classes
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final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
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final
def
getOutputCol: String
- Definition Classes
- HasOutputAnnotationCol
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def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
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def
getRegexParsers: Array[String]
A list of regex patterns to match chunks, for example: Array(“‹DT›?‹JJ›*‹NN›”)
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final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
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def
hasParam(paramName: String): Boolean
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def
hasParent: Boolean
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def
hashCode(): Int
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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val
inputAnnotatorTypes: Array[AnnotatorType]
Input annotator type : DOCUMENT, NAMED_ENTITY
Input annotator type : DOCUMENT, NAMED_ENTITY
- Definition Classes
- NerChunker → HasInputAnnotationCols
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final
val
inputCols: StringArrayParam
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- HasInputAnnotationCols
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isDefined(param: Param[_]): Boolean
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isSet(param: Param[_]): Boolean
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isTraceEnabled(): Boolean
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lazyAnnotator: BooleanParam
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def
log: Logger
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
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logDebug(msg: ⇒ String): Unit
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logError(msg: ⇒ String, throwable: Throwable): Unit
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logError(msg: ⇒ String): Unit
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logInfo(msg: ⇒ String, throwable: Throwable): Unit
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logInfo(msg: ⇒ String): Unit
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logName: String
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logWarning(msg: ⇒ String, throwable: Throwable): Unit
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logWarning(msg: ⇒ String): Unit
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def
msgHelper(schema: StructType): String
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def
ne(arg0: AnyRef): Boolean
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notify(): Unit
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notifyAll(): Unit
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def
onWrite(path: String, spark: SparkSession): Unit
- Attributes
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val
optionalInputAnnotatorTypes: Array[String]
- Definition Classes
- HasInputAnnotationCols
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val
outputAnnotatorType: AnnotatorType
Output annotator type : CHUNK
Output annotator type : CHUNK
- Definition Classes
- NerChunker → HasOutputAnnotatorType
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final
val
outputCol: Param[String]
- Attributes
- protected
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- HasOutputAnnotationCol
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lazy val
params: Array[Param[_]]
- Definition Classes
- Params
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var
parent: Estimator[NerChunker]
- Definition Classes
- Model
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val
regexParsers: StringArrayParam
An array of grammar based chunk parsers
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def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
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def
set[T](feature: StructFeature[T], value: T): NerChunker.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
set[K, V](feature: MapFeature[K, V], value: Map[K, V]): NerChunker.this.type
- Attributes
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- HasFeatures
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def
set[T](feature: SetFeature[T], value: Set[T]): NerChunker.this.type
- Attributes
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- Definition Classes
- HasFeatures
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def
set[T](feature: ArrayFeature[T], value: Array[T]): NerChunker.this.type
- Attributes
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- Definition Classes
- HasFeatures
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final
def
set(paramPair: ParamPair[_]): NerChunker.this.type
- Attributes
- protected
- Definition Classes
- Params
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final
def
set(param: String, value: Any): NerChunker.this.type
- Attributes
- protected
- Definition Classes
- Params
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final
def
set[T](param: Param[T], value: T): NerChunker.this.type
- Definition Classes
- Params
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def
setDefault[T](feature: StructFeature[T], value: () ⇒ T): NerChunker.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): NerChunker.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): NerChunker.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): NerChunker.this.type
- Attributes
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- Definition Classes
- HasFeatures
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final
def
setDefault(paramPairs: ParamPair[_]*): NerChunker.this.type
- Attributes
- protected
- Definition Classes
- Params
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final
def
setDefault[T](param: Param[T], value: T): NerChunker.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
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final
def
setInputCols(value: String*): NerChunker.this.type
- Definition Classes
- HasInputAnnotationCols
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def
setInputCols(value: Array[String]): NerChunker.this.type
- Definition Classes
- HasInputAnnotationCols
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def
setLazyAnnotator(value: Boolean): NerChunker.this.type
- Definition Classes
- CanBeLazy
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final
def
setOutputCol(value: String): NerChunker.this.type
- Definition Classes
- HasOutputAnnotationCol
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def
setParent(parent: Estimator[NerChunker]): NerChunker
- Definition Classes
- Model
-
def
setRegexParsers(value: Array[String]): NerChunker
A list of regex patterns to match chunks, for example: Array(“‹DT›?‹JJ›*‹NN›”)
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
- Definition Classes
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final
def
transform(dataset: Dataset[_]): DataFrame
- Definition Classes
- AnnotatorModel → Transformer
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def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
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def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
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final
def
transformSchema(schema: StructType): StructType
- Definition Classes
- RawAnnotator → PipelineStage
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def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
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- PipelineStage
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- @DeveloperApi()
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val
uid: String
- Definition Classes
- NerChunker → Identifiable
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def
validate(schema: StructType): Boolean
- Attributes
- protected
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- RawAnnotator
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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def
wait(arg0: Long): Unit
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def
wrapColumnMetadata(col: Column): Column
- Attributes
- protected
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- RawAnnotator
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def
write: MLWriter
- Definition Classes
- ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
Inherited from HasSimpleAnnotate[NerChunker]
Inherited from AnnotatorModel[NerChunker]
Inherited from CanBeLazy
Inherited from RawAnnotator[NerChunker]
Inherited from HasOutputAnnotationCol
Inherited from HasInputAnnotationCols
Inherited from HasOutputAnnotatorType
Inherited from ParamsAndFeaturesWritable
Inherited from HasFeatures
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from Model[NerChunker]
Inherited from Transformer
Inherited from PipelineStage
Inherited from Logging
Inherited from Params
Inherited from Serializable
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from Any
Parameters
Annotator types
Required input and expected output annotator types