RelationClassifier

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. def acc(output: Iterable[(String, String)]): Double

   

5. def addExamples(labelDataset: Dataset[String, String], tree: DiscourseTree, doc: Document, edus: Array[Array[(Int, Int)]]): Unit

   

6. final def asInstanceOf[T0]: T0

   

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7. def classOf(datum: Datum[String, String]): String

   

8. var classifier: Classifier[String, String]

   

9. def clone(): AnyRef

   

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10. var corpusStats: CorpusStats

    

11. final def eq(arg0: AnyRef): Boolean

    

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12. def equals(arg0: Any): Boolean

    

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13. val featureExtractor: RelationFeatureExtractor

    

14. def featureSelectionIncremental(trees: List[(DiscourseTree, Document)], cs: CorpusStats): Unit

    

15. def finalize(): Unit

    

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16. final def getClass(): Class[_]

    

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17. def hashCode(): Int

    

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18. final def isInstanceOf[T0]: Boolean

    

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19. def mkDataset(trees: List[(DiscourseTree, Document)]): Dataset[String, String]

    

20. def mkDatum(left: DiscourseTree, right: DiscourseTree, doc: Document, edus: Array[Array[(Int, Int)]], label: String): Datum[String, String]

    

21. def mkLabel(relLabel: String, relDir: RelationDirection.Value): String

    

22. final def ne(arg0: AnyRef): Boolean

    

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23. final def notify(): Unit

    

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24. final def notifyAll(): Unit

    

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25. def parseLabel(label: String): (String, RelationDirection.Value)

    

26. val prefixes: String

    

27. def saveTo(w: Writer, saveCorpusStats: Boolean = false): Unit

    

28. var scaleRanges: ScaleRange[String]

    

29. final def synchronized[T0](arg0: ⇒ T0): T0

    

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30. def test(tree: DiscourseTree, doc: Document, edus: Array[Array[(Int, Int)]], scorer: DiscourseScorer, score: DiscourseScore): Unit

    

    Tests the relation classifier, assuming gold segmentation and structure

31. def test(trees: List[(DiscourseTree, Document)]): Unit

    

    Tests the relation classifier, assuming gold segmentation and structure

32. def toString(): String

    

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33. def train(trees: List[(DiscourseTree, Document)], cs: CorpusStats): Unit

    

    Trains the relation classifier, assuming gold segmentation and structure

34. final def wait(): Unit

    

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35. final def wait(arg0: Long, arg1: Int): Unit

    

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36. final def wait(arg0: Long): Unit

    

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37. var withNuclearity: Boolean