resolution

Type Members

1. class BigChunkEntityResolverApproach extends AnnotatorApproach[BigChunkEntityResolverModel] with HasStorage with HasStorageReader with Licensed

   

2. class BigChunkEntityResolverModel extends AnnotatorModel[BigChunkEntityResolverModel] with HasStorageModel with HasEmbeddingsProperties with Licensed

   

3. case class BigFoundData(distance: Double, probability: Double, code: String, trained: Array[String], normalized: String) extends Product with Serializable

   

4. class ChunkEntityResolverApproach extends AnnotatorApproach[ChunkEntityResolverModel] with Licensed

   

5. class ChunkEntityResolverModel extends AnnotatorModel[ChunkEntityResolverModel] with HasStorageModel with HasEmbeddingsProperties with Licensed

   

   Contains all the parameters to transform a dataset with two Input Annotations of types TOKEN and WORD_EMBEDDINGS, coming from ChunkTokenizer and ChunkEmbeddings Annotators and return the Normalized Entity for a particular trained ontology / curated dataset.

6. case class DistanceResult(distance: Double, weightedDistance: Double) extends Product with Serializable

   

   Class that contains distance in both representations: weighted and non-weighted, for using later in DistancePooling

7. class EnsembleEntityResolverApproach extends AnnotatorApproach[EnsembleEntityResolverModel] with Licensed with EnsembleApproachClassifierParams with EnsembleModelResolverParams with EnsembleApproachResolverParams with StringFunctions

   

   Trains a model given two Input Annotators of types TOKEN and WORD_EMBEDDINGS, coming from ChunkTokenizer and ChunkEmbeddings Annotators

   Trains a model given two Input Annotators of types TOKEN and WORD_EMBEDDINGS, coming from ChunkTokenizer and ChunkEmbeddings Annotators

   The returned EnsembleEntityResolverModel consists of two layers: - First a TFIDF + OvrLogRegClassifier on top of the TOKEN Annotations - Second a set of ChunkEntityResolversModels, one per each different class from the first layer

   This approach allows Spark NLP's Entity Resolution Architecture to scale to a few millions of rows [codes]

8. class EnsembleEntityResolverModel extends Model[EnsembleEntityResolverModel] with RawAnnotator[EnsembleEntityResolverModel] with EnsembleModelResolverParams with EnsembleModelParams with StringFunctions with CanBeLazy with Licensed

   

   Contains all the parameters to transform a dataset with two Input Annotations of types TOKEN and WORD_EMBEDDINGS, coming from ChunkTokenizer and ChunkEmbeddings Annotators and return the Normalized Entity for a particular trained ontology / curated dataset.

   Contains all the parameters to transform a dataset with two Input Annotations of types TOKEN and WORD_EMBEDDINGS, coming from ChunkTokenizer and ChunkEmbeddings Annotators and return the Normalized Entity for a particular trained ontology / curated dataset.

   This EnsembleChunkEntityResolverModel consists of two layers: First a TFIDF + OvrLogRegClassifier on top of the TOKEN Annotations Second a set of ChunkEntityResolversModels, one per each different class from the first layer

   This architecture allows Spark NLP's Entity Resolution Architecture to scale to a few millions of rows [codes]

9. class JDataReader extends AnyRef

   

10. case class JTreeComponent(embeddings: Array[Float], data: JTreeData) extends Product with Serializable

    

11. case class JTreeData(code: String, trained: Array[String], normalized: String) extends Product with Serializable

    

12. class JTreeReader extends StorageReader[JTreeComponent]

    

13. class JTreeWriter extends StorageBatchWriter[JTreeComponent]

    

14. trait ReadablePretrainedBigChunkEntityResolver extends StorageReadable[BigChunkEntityResolverModel] with HasPretrained[BigChunkEntityResolverModel] with EvalEntityResolver

    

15. trait ReadablePretrainedChunkEntityResolver extends ParamsAndFeaturesReadable[ChunkEntityResolverModel] with HasPretrained[ChunkEntityResolverModel] with EvalEntityResolver

    

16. trait ReadablePretrainedEnsembleEntityResolver extends ParamsAndFeaturesReadable[EnsembleEntityResolverModel] with EvalEntityResolver with HasPretrained[EnsembleEntityResolverModel]

    

17. case class TreeData(code: String, trained: Array[String], normalized: String) extends Product with Serializable