sparknlp_jsl.annotator.ner.ner_chunker#

Classes#

class NerChunker(classname='com.johnsnowlabs.nlp.annotators.ner.NerChunker', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

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.

Input Annotation types	Output Annotation type
DOCUMENT, CHUNK	NAMED_ENTITY

Parameters:

setRegexParsers – A list of regex patterns to match chunks, for example: [“‹DT›?‹JJ›*‹NN”]

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp_jsl.base import *
>>> from sparknlp.annotator import *
>>> from sparknlp_jsl.annotator import *
>>> from sparknlp.training import *
>>> from pyspark.ml import Pipeline

>>> document_assembler = DocumentAssembler() \
...    .setInputCol("text") \
...    .setOutputCol("document")
...
>>> sentence_detector = SentenceDetector() \
...    .setInputCol("document") \
...    .setOutputCol("sentence")
...
>>> tokenizer = Tokenizer() \
...    .setInputCols(["sentence"]) \
...    .setOutputCol("token")
...
>>> embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models") \
...    .setInputCols(["sentence", "token"]) \
...    .setOutputCol("embeddings")     ...    .setCaseSensitive(False)
...
>>> ner = MedicalNerModel.pretrained("ner_radiology", "en", "clinical/models") \
...    .setInputCols(["sentence", "token","embeddings"]) \
...    .setOutputCol("ner")     ...    .setCaseSensitive(False)
...
>>> chunker = NerChunker() \
...    .setInputCols(["sentence","ner"]) \
...    .setChunkCol("ner_chunk") \
...    .setOutputCol("chunk")
...    .setRegexParsers(Array("<ImagingFindings>.*<BodyPart>"))
...
...
>>> pipeline = Pipeline(stages=[
...    document_assembler,
...    sentence_detector,
...    tokenizer,
...    embeddings,
...    ner,
...    chunker
...])
>>> result = pipeline.fit.fit(dataset).transform(dataset)

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = NerChunker#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

regexParsers#

skipLPInputColsValidation = True#

clear(param)#

Clears a param from the param map if it has been explicitly set.

copy(extra=None)#

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters:

extra (dict, optional) – Extra parameters to copy to the new instance

Returns:

Copy of this instance

Return type:

JavaParams

explainParam(param)#

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()#

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra=None)#

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters:

extra (dict, optional) – extra param values

Returns:

merged param map

Return type:

dict

getInputCols()#

Gets current column names of input annotations.

getLazyAnnotator()#

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param)#

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol()#

Gets output column name of annotations.

getParam(paramName)#

Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters:

paramName (str) – Name of the parameter

hasDefault(param)#

Checks whether a param has a default value.

hasParam(paramName)#

Tests whether this instance contains a param with a given (string) name.

inputColsValidation(value)#

isDefined(param)#

Checks whether a param is explicitly set by user or has a default value.

isSet(param)#

Checks whether a param is explicitly set by user.

classmethod load(path)#

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read()#

Returns an MLReader instance for this class.

save(path)#

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)#

Sets a parameter in the embedded param map.

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

*value (List[str]) – Input columns for the annotator

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:

value (bool) – Whether Annotator should be evaluated lazily in a RecursivePipeline

setOutputCol(value)#

Sets output column name of annotations.

Parameters:

value (str) – Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:

paramName (str) – Name of the parameter

setParams()#

setRegexParsers(b)#

Sets list of regex patterns to match chunks, for example: Array(“‹DT›?‹JJ›*‹NN›”

Parameters:

b (List[String]) – list of regex patterns to match chunks, for example: Array(“‹DT›?‹JJ›*‹NN›”

transform(dataset, params=None)#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

• dataset (pyspark.sql.DataFrame) – input dataset

• params (dict, optional) – an optional param map that overrides embedded params.

Returns:

transformed dataset

Return type:

pyspark.sql.DataFrame

write()#

Returns an MLWriter instance for this ML instance.