EDUClassifier

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. final def asInstanceOf[T0]: T0

   

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5. def boundarySort(e1: EDUToken, e2: EDUToken): Boolean

   

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

   

7. var classifier: Classifier[String, String]

   

8. def clone(): AnyRef

   

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9. def constructDataset(trees: List[(DiscourseTree, Document)]): Dataset[String, String]

   

10. def eduBoundaryStats(tokens: Iterable[EDUToken]): Unit

    

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

    

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12. def equals(arg0: Any): Boolean

    

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13. def f1(output: Iterable[(String, String)]): (Double, Double, Double, Int, Int, Int)

    

14. def featureSelectionByFrequency(trees: List[(DiscourseTree, Document)], corpusStats: CorpusStats): Unit

    

15. def featureSelectionByInformativeness(trees: List[(DiscourseTree, Document)], corpusStats: CorpusStats): Unit

    

16. def featureSelectionIncremental(trees: List[(DiscourseTree, Document)], corpusStats: CorpusStats): Unit

    

17. def finalize(): Unit

    

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18. def findEDUStarts(tree: DiscourseTree, starts: HashSet[TokenOffset]): Unit

    

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

    

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20. def hashCode(): Int

    

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21. final def isInstanceOf[T0]: Boolean

    

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22. def isTrained: Boolean

    

23. def mkDatum(token: EDUToken): Datum[String, String]

    

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

    

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25. final def notify(): Unit

    

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26. final def notifyAll(): Unit

    

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27. def printEDUBoundaries(tokens: Iterable[EDUToken]): Unit

    

28. def report(token: EDUToken, errType: String): Unit

    

29. def saveTo(w: Writer): Unit

    

30. var scaleRanges: ScaleRange[String]

    

31. def simpleF1(output: Iterable[(String, String)]): Double

    

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

    

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

    

    Tests the standalone segmenter for intra-sentence classification

34. def toString(): String

    

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35. def train(trees: List[(DiscourseTree, Document)], corpusStats: CorpusStats): Unit

    

    Trains using all .dis in the given directory

36. final def wait(): Unit

    

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

    

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

    

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