com.johnsnowlabs.nlp.annotators
GraphExtraction
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package annotators
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
takes a document and annotations and produces new annotations of this annotator's annotation type
takes a document and annotations and produces new annotations of this annotator's annotation type
Annotations that correspond to inputAnnotationCols generated by previous annotators if any
any number of annotations processed for every input annotation. Not necessary one to one relationship
requirement for annotators copies
requirement for annotators copies
Delimiter symbol used for path output (Default: ",")
Coordinates (name, lang, remoteLoc) to a pretrained Dependency Parser model (Default: Array())
Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column
Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column
udf function to be applied to inputCols using this annotator's annotate function as part of ML transformation
Find paths between a pair of entities (Default: Array())
When set to true find paths between entities (Default: false)
Override for additional custom schema checks
Override for additional custom schema checks
input annotations columns currently used
Gets annotation column name going to generate
Gets annotation column name going to generate
Whether to include edges when building paths (Default: true)
Input annotator types: DOCUMENT, TOKEN, NAMED_ENTITY
Input annotator types: DOCUMENT, TOKEN, NAMED_ENTITY
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
Maximum sentence size that the annotator will process (Default: 1000).
Maximum sentence size that the annotator will process (Default: 1000). Above this, the sentence is skipped
Merge same neighboring entities as a single token (Default: false)
IOB format to apply when merging entities
Minimum sentence size that the annotator will process (Default: 2).
Minimum sentence size that the annotator will process (Default: 2). Below this, the sentence is skipped
Output annotator types: NODE
Output annotator types: NODE
Coordinates (name, lang, remoteLoc) to a pretrained POS model (Default: Array())
Find paths between a pair of token and entity (Default: Array())
Tokens to be consider as root to start traversing the paths (Default: Array()).
Tokens to be consider as root to start traversing the paths (Default: Array()). Use it along with explodeEntities
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Overrides annotation column name when transforming
Overrides annotation column name when transforming
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Dataset[Row]
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
Coordinates (name, lang, remoteLoc) to a pretrained Typed Dependency Parser model (Default: Array())
required uid for storing annotator to disk
required uid for storing annotator to disk
takes a Dataset and checks to see if all the required annotation types are present.
takes a Dataset and checks to see if all the required annotation types are present.
to be validated
True if all the required types are present, else false
A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.
Required input and expected output annotator types
Extracts a dependency graph between entities.
The GraphExtraction class takes e.g. extracted entities from a NerDLModel and creates a dependency tree which describes how the entities relate to each other. For that a triple store format is used. Nodes represent the entities and the edges represent the relations between those entities. The graph can then be used to find relevant relationships between words.
Both the DependencyParserModel and TypedDependencyParserModel need to be present in the pipeline. There are two ways to set them:
setMergeEntitiestotruewill download the default pretrained models for those two Annotators automatically. The specific models can also be set withsetDependencyParserModelandsetTypedDependencyParserModel:To transform the resulting graph into a more generic form such as RDF, see the GraphFinisher.
Example
GraphFinisher to output the paths in a more generic format, like RDF