Type Members

1.  class ConllData extends AnyRef
2.  class DependencyArcList extends AnyRef
3.  class DependencyInstance extends Serializable
4.  class DependencyPipe extends Serializable
5.  class LocalFeatureData extends AnyRef
6.  class LowRankTensor extends AnyRef
7.  class Options extends Serializable
8.  class Parameters extends Serializable
9.  class PredictionParameters extends AnyRef
10.  trait ReadablePretrainedTypedDependency extends ParamsAndFeaturesReadable[TypedDependencyParserModel] with HasPretrained[TypedDependencyParserModel]
11.  class TrainDependencies extends Serializable
12.  case class TrainFile(path: String, conllFormat: String) extends Product with Serializable
13.  class TypedDependencyParser extends Serializable

    Labeled parser that finds a grammatical relation between two words in a sentence.

    Labeled parser that finds a grammatical relation between two words in a sentence. Its input is a CoNLL2009 or ConllU dataset.

    See also

    TypedDependencyParserApproach

    TypedDependencyParserModel

14.  class TypedDependencyParserApproach extends AnnotatorApproach[TypedDependencyParserModel]

    Labeled parser that finds a grammatical relation between two words in a sentence.

    Labeled parser that finds a grammatical relation between two words in a sentence. Its input is either a CoNLL2009 or ConllU dataset.

    For instantiated/pretrained models, see TypedDependencyParserModel.

    Dependency parsers provide information about word relationship. For example, dependency parsing can tell you what the subjects and objects of a verb are, as well as which words are modifying (describing) the subject. This can help you find precise answers to specific questions.

    The parser requires the dependant tokens beforehand with e.g. DependencyParser. The required training data can be set in two different ways (only one can be chosen for a particular model):

    • Dataset in the CoNLL 2009 format set with setConll2009
    • Dataset in the CoNLL-U format set with setConllU

    Apart from that, no additional training data is needed.

    See TypedDependencyParserApproachTestSpec for further reference on this API.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base.DocumentAssembler
    import com.johnsnowlabs.nlp.annotators.sbd.pragmatic.SentenceDetector
    import com.johnsnowlabs.nlp.annotators.Tokenizer
    import com.johnsnowlabs.nlp.annotators.pos.perceptron.PerceptronModel
    import com.johnsnowlabs.nlp.annotators.parser.dep.DependencyParserModel
    import com.johnsnowlabs.nlp.annotators.parser.typdep.TypedDependencyParserApproach
    import org.apache.spark.ml.Pipeline
    
    val documentAssembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")
    
    val sentence = new SentenceDetector()
      .setInputCols("document")
      .setOutputCol("sentence")
    
    val tokenizer = new Tokenizer()
      .setInputCols("sentence")
      .setOutputCol("token")
    
    val posTagger = PerceptronModel.pretrained()
      .setInputCols("sentence", "token")
      .setOutputCol("pos")
    
    val dependencyParser = DependencyParserModel.pretrained()
      .setInputCols("sentence", "pos", "token")
      .setOutputCol("dependency")
    
    val typedDependencyParser = new TypedDependencyParserApproach()
      .setInputCols("dependency", "pos", "token")
      .setOutputCol("dependency_type")
      .setConllU("src/test/resources/parser/labeled/train_small.conllu.txt")
      .setNumberOfIterations(1)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      sentence,
      tokenizer,
      posTagger,
      dependencyParser,
      typedDependencyParser
    ))
    
    // Additional training data is not needed, the dependency parser relies on CoNLL-U only.
    val emptyDataSet = Seq.empty[String].toDF("text")
    val pipelineModel = pipeline.fit(emptyDataSet)

15.  class TypedDependencyParserModel extends AnnotatorModel[TypedDependencyParserModel] with HasSimpleAnnotate[TypedDependencyParserModel]

    Labeled parser that finds a grammatical relation between two words in a sentence.

    Labeled parser that finds a grammatical relation between two words in a sentence. Its input is either a CoNLL2009 or ConllU dataset.

    Dependency parsers provide information about word relationship. For example, dependency parsing can tell you what the subjects and objects of a verb are, as well as which words are modifying (describing) the subject. This can help you find precise answers to specific questions.

    The parser requires the dependant tokens beforehand with e.g. DependencyParser.

    Pretrained models can be loaded with pretrained of the companion object:

    val typedDependencyParser = TypedDependencyParserModel.pretrained()
      .setInputCols("dependency", "pos", "token")
      .setOutputCol("dependency_type")

    The default model is "dependency_typed_conllu", if no name is provided. For available pretrained models please see the Models Hub.

    For extended examples of usage, see the Examples and the TypedDependencyModelTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base.DocumentAssembler
    import com.johnsnowlabs.nlp.annotators.Tokenizer
    import com.johnsnowlabs.nlp.annotators.sbd.pragmatic.SentenceDetector
    import com.johnsnowlabs.nlp.annotators.pos.perceptron.PerceptronModel
    import com.johnsnowlabs.nlp.annotators.parser.dep.DependencyParserModel
    import com.johnsnowlabs.nlp.annotators.parser.typdep.TypedDependencyParserModel
    import org.apache.spark.ml.Pipeline
    
    val documentAssembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")
    
    val sentence = new SentenceDetector()
      .setInputCols("document")
      .setOutputCol("sentence")
    
    val tokenizer = new Tokenizer()
      .setInputCols("sentence")
      .setOutputCol("token")
    
    val posTagger = PerceptronModel.pretrained()
      .setInputCols("sentence", "token")
      .setOutputCol("pos")
    
    val dependencyParser = DependencyParserModel.pretrained()
      .setInputCols("sentence", "pos", "token")
      .setOutputCol("dependency")
    
    val typedDependencyParser = TypedDependencyParserModel.pretrained()
      .setInputCols("dependency", "pos", "token")
      .setOutputCol("dependency_type")
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      sentence,
      tokenizer,
      posTagger,
      dependencyParser,
      typedDependencyParser
    ))
    
    val data = Seq(
      "Unions representing workers at Turner Newall say they are 'disappointed' after talks with stricken parent " +
        "firm Federal Mogul."
    ).toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.selectExpr("explode(arrays_zip(token.result, dependency.result, dependency_type.result)) as cols")
      .selectExpr("cols['0'] as token", "cols['1'] as dependency", "cols['2'] as dependency_type")
      .show(8, truncate = false)
    +------------+------------+---------------+
    |token       |dependency  |dependency_type|
    +------------+------------+---------------+
    |Unions      |ROOT        |root           |
    |representing|workers     |amod           |
    |workers     |Unions      |flat           |
    |at          |Turner      |case           |
    |Turner      |workers     |flat           |
    |Newall      |say         |nsubj          |
    |say         |Unions      |parataxis      |
    |they        |disappointed|nsubj          |
    +------------+------------+---------------+