sparknlp_jsl.annotator.NerDisambiguator#
- class sparknlp_jsl.annotator.NerDisambiguator[source]#
Bases:
AnnotatorApproach
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
Methods
__init__
()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.
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.
fit
(dataset[, params])Fits a model to the input dataset with optional parameters.
fitMultiple
(dataset, paramMaps)Fits a model to the input dataset for each param map in paramMaps.
Gets current column names of input annotations.
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.
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).
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.
Sets Levenshtein distance threshold to narrow results from prefix search (0.1 is default)
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.
setPredictionLimit
(value)Sets limit on amount of predictions N for topN predictions
setS3KnowledgeBaseName
(value)Sets knowledge base name in s3
setTokenSearch
(value)Sets whether to search by token or by chunk in knowledge base (Default: true)
write
()Returns an MLWriter instance for this ML instance.
Attributes
embeddingTypeParam
getter_attrs
inputCols
lazyAnnotator
levenshteinDistanceThresholdParam
narrowWithApproximateMatching
nearMatchingGapParam
numFirstChars
outputCol
Returns all params ordered by name.
predictionsLimit
s3KnowledgeBaseName
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
- fit(dataset, params=None)#
Fits a model to 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. 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)
New in version 1.3.0.
- fitMultiple(dataset, paramMaps)#
Fits a model to the input dataset for each param map in paramMaps.
- Parameters:
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – 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.
New in version 2.3.0.
- 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 typeParam
.
- 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) 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:
- valuebool
Limit on amount of predictions N for topN predictions
- setS3KnowledgeBaseName(value)[source]#
Sets knowledge base name in s3
- Parameters:
- valuestr
knowledge base name in s3 example (i-per)
- 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)
- uid#
A unique id for the object.
- write()#
Returns an MLWriter instance for this ML instance.