Packages

package logreg

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Type Members

  1. class AssertionLogRegApproach extends AnnotatorApproach[AssertionLogRegModel] with Windowing with Licensed

    This is a classification method, which uses LogisticRegression algorithm Contains all the methods for training a AssertionLogRegModel, together with trainWithChunk, trainWithStartEnd.

    This is a classification method, which uses LogisticRegression algorithm Contains all the methods for training a AssertionLogRegModel, together with trainWithChunk, trainWithStartEnd.

    Example

    Training with Glove Embeddings

    First define pipeline stages to extract embeddings and text chunks

     val documentAssembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")
    
    val tokenizer = new Tokenizer()
      .setInputCols("document")
      .setOutputCol("token")
    
    val glove = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
      .setInputCols("document", "token")
      .setOutputCol("word_embeddings")
      .setCaseSensitive(false)
    
    val chunkAssembler = new Doc2Chunk()
      .setInputCols("document")
      .setChunkCol("target")
      .setOutputCol("chunk")

    Then the AssertionLogRegApproach model is defined. Label column is needed in the dataset for training.

    val assertion = new AssertionLogRegApproach()
      .setLabelCol("label")
      .setInputCols("document", "chunk", "word_embeddings")
      .setOutputCol("assertion")
      .setReg(0.01)
      .setBefore(11)
      .setAfter(13)
      .setStartCol("start")
      .setEndCol("end")
    
    val assertionPipeline = new Pipeline().setStages(Array(
      documentAssembler,
      sentenceDetector,
      tokenizer,
      embeddings,
      nerModel,
      nerConverter,
      assertion
    ))
    
    val assertionModel = assertionPipeline.fit(dataset)
  2. class AssertionLogRegModel extends Model[AssertionLogRegModel] with RawAnnotator[AssertionLogRegModel] with Windowing with HasStorageRef with CanBeLazy with Licensed

    This is a main class in AssertionLogReg family.

    This is a main class in AssertionLogReg family. Logarithmic Regression is used to extract Assertion Status from extracted entities and text. AssertionLogRegModel requires DOCUMENT, CHUNK and WORD_EMBEDDINGS type annotator inputs, which can be obtained by e.g a DocumentAssembler, NerConverter and WordEmbeddingsModel. The result is an assertion status annotation for each recognized entity. Possible values are "Negated", "Affirmed" and "Historical".

    Unlike the DL Model, this class does not extend AnnotatorModel. Instead it extends the RawAnnotator, that's why the main point of interest is method transform().

    At the moment there are no pretrained models available for this class. Please refer to AssertionLogRegApproach to train your own model.

    See also

    AssertionLogRegApproach to train a custom model

    AssertionDLModel for deep learning extraction

  3. case class Datapoint(sentence: String, target: String, label: String, start: Int, end: Int) extends Product with Serializable
  4. class NegexDatasetReader extends Serializable

    Reader for this dataset, https://github.com/mongoose54/negex/blob/master/genConText/rsAnnotations-1-120-random.txt

  5. trait ReadablePretrainedAssertionLogReg extends ParamsAndFeaturesReadable[AssertionLogRegModel] with HasPretrained[AssertionLogRegModel]
  6. trait Windowing extends Serializable

    Created by jose on 24/11/17.

Value Members

  1. object AssertionLogRegApproach extends DefaultParamsReadable[AssertionLogRegApproach] with Serializable
  2. object AssertionLogRegModel extends ReadablePretrainedAssertionLogReg with Serializable

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