sparknlp_jsl.annotator.NerDisambiguatorModel#

class sparknlp_jsl.annotator.NerDisambiguatorModel(classname='com.johnsnowlabs.nlp.annotators.disambiguation.NerDisambiguatorModel', java_model=None)[source]#

Bases: AnnotatorModel

Links words of interest, such as names of persons, locations and companies, from an input text document to a corresponding unique entity in a target Knowledge Base (KB). Words of interest are called Named Entities (NEs), mentions, or surface forms. Instantiated / pretrained model of the NerDisambiguator. Links words of interest, such as names of persons, locations and companies, from an input text document to a corresponding unique entity in a target Knowledge Base (KB). Words of interest are called Named Entities (NEs),

Input Annotation types	Output Annotation type
`CHUNK, SENTENCE_EMBEDDINGS`	`DISAMBIGUATION`

Parameters:

embeddingTypeParam: Could be bow for word embeddings or sentence for sentences
numFirstChars: How many characters should be considered for initial prefix search in knowledge base
tokenSearch: Should we search by token or by chunk in knowledge base (token is recommended)
narrowWithApproximateMatching: Should we narrow prefix search results with levenstein distance based matching (true is recommended)
levenshteinDistanceThresholdParam: Levenshtein distance threshold to narrow results from prefix search (0.1 is default)
nearMatchingGapParam: Puts a limit on a string length (by trimming the candidate chunks) during levenshtein-distance based narrowing,len(candidate) - len(entity chunk) > nearMatchingGap (Default: 4).
predictionsLimit: Limit on amount of predictions N for topN predictions
s3KnowledgeBaseName: knowledge base name in s3

Examples

>>> data = spark.createDataFrame([["The show also had a contestant named Donald Trump who later defeated Christina Aguilera ..."]])     ...   .toDF("text")
>>> documentAssembler = DocumentAssembler() \
...   .setInputCol("text") \
...   .setOutputCol("document")
>>> sentenceDetector = SentenceDetector() \
...   .setInputCols(["document"]) \
...   .setOutputCol("sentence")
>>> tokenizer = Tokenizer() \
...   .setInputCols(["sentence"]) \
...   .setOutputCol("token")
>>> word_embeddings = WordEmbeddingsModel.pretrained() \
...   .setInputCols(["sentence", "token"]) \
...   .setOutputCol("embeddings")
>>> sentence_embeddings = SentenceEmbeddings() \
...   .setInputCols(["sentence","embeddings"]) \
...   .setOutputCol("sentence_embeddings")    >>> ner_model = NerDLModel.pretrained() \
...   .setInputCols(["sentence", "token", "embeddings"]) \
...   .setOutputCol("ner")
>>> ner_converter = NerConverter() \
...   .setInputCols(["sentence", "token", "ner"]) \
...   .setOutputCol("ner_chunk") \
...   .setWhiteList(["PER"])

Then the extracted entities can be disambiguated. >>> disambiguator = NerDisambiguatorModel.pretrained() … .setInputCols([“ner_chunk”, “sentence_embeddings”]) … .setOutputCol(“disambiguation”) … .setNumFirstChars(5) … >>> nlpPipeline = Pipeline(stages=[ … documentAssembler, … sentenceDetector, … tokenizer, … word_embeddings, … sentence_embeddings, … ner_model, … ner_converter, … disambiguator]) … >>> model = nlpPipeline.fit(data) >>> result = model.transform(data) >>> result.selectExpr(“explode(disambiguation)”) … .selectExpr(“col.metadata.chunk as chunk”, “col.result as result”).show(5, False)

chunk	result
Donald Trump Christina Aguilera	http:#en.wikipedia.org/?curid=4848272, http:#en.wikipedia.org/?curid=31698421, http:#en.wikipedia.org/?curid=55907961 http:#en.wikipedia.org/?curid=144171, http:#en.wikipedia.org/?curid=6636454

Methods

`__init__`([classname, java_model])	Initialize this instance with a Java model object.
`clear`(param)	Clears a param from the param map if it has been explicitly set.
`copy`([extra])	Creates a copy of this instance with the same uid and some extra params.
`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])	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.
`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.
`getOutputCol`()	Gets output column name of annotations.
`getParam`(paramName)	Gets a param by its name.
`getParamValue`(paramName)	Gets the value of a 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.
`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.
`load`(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
`pretrained`([name, lang, remote_loc])
`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.
`setEmbeddingType`(value)	Sets if we want to use 'bow' for word embeddings or 'sentence' for sentences
`setInputCols`(*value)	Sets column names of input annotations.
`setLazyAnnotator`(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
`setLevenshteinDistanceThresholdParam`(value)	Sets Levenshtein distance threshold to narrow results from prefix search (0.1 is default)
`setNarrowWithApproximateMatching`(value)	Sets whether to narrow prefix search results with levenstein distance based matching (Default: true)
`setNearMatchingGapParam`(value)	Sets a limit on a string length (by trimming the candidate chunks) during levenshtein-distance based narrowing.
`setNumFirstChars`(value)	How many characters should be considered for initial prefix search in knowledge base
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setParams`()
`setPredictionLimit`(value)	Sets limit on amount of predictions N for topN predictions
`setTokenSearch`(value)	Sets whether to search by token or by chunk in knowledge base (Default: true)
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`embeddingTypeParam`
`getter_attrs`
`inputCols`
`lazyAnnotator`
`levenshteinDistanceThresholdParam`
`name`
`narrowWithApproximateMatching`
`nearMatchingGapParam`
`numFirstChars`
`outputCol`
`params`	Returns all params ordered by name.
`predictionsLimit`
`tokenSearch`

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 – Extra parameters to copy to the new instance
Returns:: Copy of this instance

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 – extra param values
Returns:: merged param map

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:

paramNamestr: 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.

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).

property params#: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

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.

setEmbeddingType(value)[source]#

Sets if we want to use ‘bow’ for word embeddings or ‘sentence’ for sentences

Parameters:

valuestr: Can be ‘bow’ for word embeddings or ‘sentence’ for sentences (Default: sentence)

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

*valuestr: Input columns for the annotator

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:

valuebool: Whether Annotator should be evaluated lazily in a RecursivePipeline

setLevenshteinDistanceThresholdParam(value)[source]#

Sets Levenshtein distance threshold to narrow results from prefix search (0.1 is default)

Parameters:

valuefloat: Levenshtein distance threshold to narrow results from prefix search (0.1 is default)

setNarrowWithApproximateMatching(value)[source]#

Sets whether to narrow prefix search results with levenstein distance based matching (Default: true)

Parameters:

valuebool: Whether to narrow prefix search results with levenstein distance based matching (Default: true)

setNearMatchingGapParam(value)[source]#

Sets a limit on a string length (by trimming the candidate chunks) during levenshtein-distance based narrowing.

Parameters:

valueint: Limit on a string length (by trimming the candidate chunks) during levenshtein-distance based narrowing

setNumFirstChars(value)[source]#

How many characters should be considered for initial prefix search in knowledge base

Parameters:

valuebool: How many characters should be considered for initial prefix search in knowledge base

setOutputCol(value)#

Sets output column name of annotations.

Parameters:

valuestr: Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:

paramNamestr: Name of the parameter

setPredictionLimit(value)[source]#

Sets limit on amount of predictions N for topN predictions

Parameters:

sbool: Limit on amount of predictions N for topN predictions

setTokenSearch(value)[source]#

Sets whether to search by token or by chunk in knowledge base (Default: true)

Parameters:

valuebool: Whether to search by token or by chunk in knowledge base (Default: true)

transform(dataset, params=None)#

Transforms the input dataset with optional parameters.

Parameters:

dataset – input dataset, which is an instance of pyspark.sql.DataFrame
params – an optional param map that overrides embedded params.

Returns:

transformed dataset

New in version 1.3.0.

uid#: A unique id for the object.

write()#: Returns an MLWriter instance for this ML instance.