sparknlp_jsl.annotator.disambiguation.ner_disambiguator#

Classes#

class NerDisambiguator#

Bases: sparknlp_jsl.common.AnnotatorApproachInternal

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 = NerDisambiguator() … .setS3KnowledgeBaseName(“i-per”) … .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

embeddingTypeParam#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

levenshteinDistanceThresholdParam#

narrowWithApproximateMatching#

nearMatchingGapParam#

numFirstChars#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

predictionsLimit#

s3KnowledgeBaseName#

skipLPInputColsValidation = True#

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 (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

fit(dataset, params=None)#

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

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

• params (dict or list or tuple, optional) – an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns:

fitted model(s)

Return type:

Transformer or a list of Transformer

fitMultiple(dataset, paramMaps)#

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters:

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

• paramMaps (collections.abc.Sequence) – A Sequence of param maps.

Returns:

A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

Return type:

_FitMultipleIterator

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.

setEmbeddingType(value)#

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

Parameters:

value (str) – Can be ‘bow’ for word embeddings or ‘sentence’ for sentences (Default: sentence) Can be ‘bow’ for word embeddings or ‘sentence’ for sentences (Default: sentence)

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

setLevenshteinDistanceThresholdParam(value)#

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

Parameters:

value (float) – 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)

Parameters:

value (bool) – 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.

Parameters:

value (int) – 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

Parameters:

value (bool) – How many characters should be considered for initial prefix search in knowledge base

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

setPredictionLimit(value)#

Sets limit on amount of predictions N for topN predictions

Parameters:

value (bool) – Limit on amount of predictions N for topN predictions

setS3KnowledgeBaseName(value)#

Sets knowledge base name in s3

Parameters:

value (str) – knowledge base name in s3 example (i-per)

setTokenSearch(value)#

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

Parameters:

value (bool) – Whether to search by token or by chunk in knowledge base (Default: true)

write()#

Returns an MLWriter instance for this ML instance.

class NerDisambiguatorModel(classname='com.johnsnowlabs.nlp.annotators.disambiguation.NerDisambiguatorModel', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

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

embeddingTypeParam#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

levenshteinDistanceThresholdParam#

name = NerDisambiguatorModel#

narrowWithApproximateMatching#

nearMatchingGapParam#

numFirstChars#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

predictionsLimit#

skipLPInputColsValidation = True#

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 (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).

static pretrained(name='disambiguator_per', lang='en', remote_loc='clinical/models')#

Downloads and loads a pretrained model.

Parameters:

• name (str, optional) – Name of the pretrained model, by default “disambiguator_per”

• lang (str, optional) – Language of the pretrained model, by default “en”

• remote_loc (str, optional) – Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:

The restored model

Return type:

NerDisambiguatorModel

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)#

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

Parameters:

value (str) – Can be ‘bow’ for word embeddings or ‘sentence’ for sentences (Default: sentence)

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

setLevenshteinDistanceThresholdParam(value)#

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

Parameters:

value (float) – 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)

Parameters:

value (bool) – 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.

Parameters:

value (int) – 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

Parameters:

value (bool) – How many characters should be considered for initial prefix search in knowledge base

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()#

setPredictionLimit(value)#

Sets limit on amount of predictions N for topN predictions

Parameters:

s (bool) – 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)

Parameters:

value (bool) – Whether to search by token or by chunk in knowledge base (Default: true)

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.