Utils

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 clone(): AnyRef

   

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6. def countNgrams(trees: List[(DiscourseTree, Document)]): Counter[String]

   

7. def countWords(trees: List[(DiscourseTree, Document)]): Counter[String]

   

8. def deps(s: Sentence): DirectedGraph[String]

   

   Fetches dependencies from a given sentence Note: RST parsing works better with basic dependencies, so we ALWAYS use them here!

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

   

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10. def equals(arg0: Any): Boolean

    

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11. def extractConnectives(tree: DiscourseTree, doc: Document): Counter[String]

    

12. def extractLeftMost(t: DiscourseTree, nodes: ArrayBuffer[DiscourseTree]): Unit

    

13. def extractRightMost(t: DiscourseTree, nodes: ArrayBuffer[DiscourseTree]): Unit

    

14. def extractTags(tree: DiscourseTree, doc: Document): Array[String]

    

15. def extractWords(tree: DiscourseTree, doc: Document, words: Counter[String] = null, threshold: Int = 1000): Array[String]

    

16. def finalize(): Unit

    

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17. def findCommonAncestorsFromDependencies(deps: DirectedGraph[String], first: Int, last: Int): Iterable[Int]

    

18. def findFeatureGroups(sep: String, lexicon: Lexicon[String]): Map[String, Set[Int]]

    

19. def findSmallestCommonAncestor(root: Tree, first: Int, last: Int): Tree

    

20. def findSubtree(tree: Tree, start: Int, end: Int): Tree

    

    Finds the smallest subtree with label S* that includes this span

21. def findSubtree(tree: DiscourseTree, doc: Document): Tree

    

    Finds the smallest subtree with label S* that includes this discourse fragment

22. def findSyntacticHead(root: Tree, parent: Tree, first: Int, last: Int): (Tree, Tree)

    

23. def findSyntacticHeadFromDependencies(deps: DirectedGraph[String], first: Int, last: Int): (Int, Int, String)

    

24. def findSyntacticParent(root: Tree, position: Int, parent: Tree = null): (Tree, Tree)

    

25. def findSyntacticParentWithRightSibling(root: Tree, position: Int, parent: Tree = null, right: Tree = null): (Tree, Tree, Tree)

    

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

    

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27. def hasDeps(s: Sentence): Boolean

    

28. def hashCode(): Int

    

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29. final def isInstanceOf[T0]: Boolean

    

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30. def lrFactory(): Classifier[String, String]

    

31. def mkDiscretePosition(offset: Int, total: Int): String

    

32. def mkGoldEDUs(tree: DiscourseTree, doc: Document): Array[Array[(Int, Int)]]

    

    Generates a compact description of the EDUs in this document, using a formalism similar to Feng and Hirst (2012)

    Generates a compact description of the EDUs in this document, using a formalism similar to Feng and Hirst (2012)

    tree

    Discourse tree for this document

    returns

    Format: one sentence per element; each sentence stores an array of EDUs; end token offsets are inclusive!

33. def mkNgram(words: Array[String], start: Int, end: Int): String

    

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

    

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35. final def notify(): Unit

    

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36. final def notifyAll(): Unit

    

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37. def perceptronFactory(): Classifier[String, String]

    

38. def prefix(f: String, sep: String): String

    

39. def printTopWeights(c: LiblinearClassifier[String, String]): Unit

    

40. def sameSubtree(t1: Tree, t2: Tree): Boolean

    

41. def svmFactory(): Classifier[String, String]

    

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

    

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43. def toDecile(v: Int, max: Int): Int

    

44. def toString(): String

    

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45. def tokenCount(doc: Document): Int

    

46. final def wait(): Unit

    

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

    

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

    

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