cc.factorie.app.nlp.ner.StackedChainNer

StackedChainNereModel

class StackedChainNereModel[Features <: CategoricalVectorVar[String]] extends ChainModel[L, Features, Token] with Parameters

Linear Supertypes
ChainModel[L, Features, Token], model.Parameters, model.Model, AnyRef, Any
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Inherited
  1. StackedChainNereModel
  2. ChainModel
  3. Parameters
  4. Model
  5. AnyRef
  6. Any
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Instance Constructors

  1. new StackedChainNereModel(featuresDomain1: CategoricalVectorDomain[String], labelToFeatures1: (L) ⇒ Features, labelToToken1: (L) ⇒ Token, tokenToLabel1: (Token) ⇒ L)(implicit mf: Manifest[Features])

Type Members

  1. class ChainLikelihoodExample extends optimize.Example

    Definition Classes
    ChainModel

  2. class ChainStructuredSVMExample extends ChainViterbiExample

    Definition Classes
    ChainModel

  3. class ChainViterbiExample extends optimize.Example

    Definition Classes
    ChainModel

Value Members

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

    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef → Any

  4. def Weights(t4: ⇒ Tensor4): Weights4

    Definition Classes
    Parameters

  5. def Weights(t3: ⇒ Tensor3): Weights3

    Definition Classes
    Parameters

  6. def Weights(t2: ⇒ Tensor2): Weights2

    Definition Classes
    Parameters

  7. def Weights(t1: ⇒ Tensor1): Weights1

    Definition Classes
    Parameters

  8. def accumulateExtraObsGradients(gradient: WeightsMapAccumulator, obs: Tensor1, position: Int, labels: Seq[L]): Unit

    Definition Classes
    StackedChainNereModel → ChainModel

  9. def addFactors(dl: variable.DiffList, result: Set[model.Factor]): Unit

    Append to "result" all Factors in this Model that are affected by the given DiffList.

    Append to "result" all Factors in this Model that are affected by the given DiffList. This method must not append duplicates.

    Definition Classes
    Model

  10. def addFactors(d: variable.Diff, result: Set[model.Factor]): Unit

    Append to "result" all Factors in this Model that are affected by the given Diff.

    Append to "result" all Factors in this Model that are affected by the given Diff. This method must not append duplicates.

    Definition Classes
    Model

  11. def addFactors(variable: variable.Var, result: Set[model.Factor]): Unit

    Append to "result" all Factors in this Model that touch the given "variable".

    Append to "result" all Factors in this Model that touch the given "variable". This method must not append duplicates.

    Definition Classes
    Model

  12. def addFactors(variables: Iterable[variable.Var], result: Set[model.Factor]): Unit

    Append to "result" all Factors in this Model that touch any of the given "variables".

    Append to "result" all Factors in this Model that touch any of the given "variables". This method must not append duplicates.

    Definition Classes
    Model

  13. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  14. def assignmentScore(dl: variable.DiffList, assignment: variable.Assignment): Double

    Definition Classes
    Model

  15. def assignmentScore(d: variable.Diff, assignment: variable.Assignment): Double

    Definition Classes
    Model

  16. def assignmentScore(vars: Iterable[variable.Var], assignment: variable.Assignment): Double

    Definition Classes
    Model

  17. def assignmentScore(variable: variable.Var, assignment: variable.Assignment): Double

    Definition Classes
    Model

  18. val bias: DotFamilyWithStatistics1[L] { val weights: cc.factorie.model.Weights1 }

    Definition Classes
    ChainModel

  19. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  20. def currentScore(dl: variable.DiffList): Double

    Definition Classes
    Model

  21. def currentScore(d: variable.Diff): Double

    Definition Classes
    Model

  22. def currentScore(vars: Iterable[variable.Var]): Double

    Definition Classes
    Model

  23. def currentScore(variable: variable.Var): Double

    Definition Classes
    Model

  24. def deserialize(stream: InputStream): Unit

    Definition Classes
    ChainModel

  25. val embedding: DotFamilyWithStatistics2[L, EmbeddingVariable] { val weights: cc.factorie.model.Weights2 }

  26. val embeddingNext: DotFamilyWithStatistics2[L, EmbeddingVariable] { val weights: cc.factorie.model.Weights2 }

  27. val embeddingPrev: DotFamilyWithStatistics2[L, EmbeddingVariable] { val weights: cc.factorie.model.Weights2 }

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

    Definition Classes
    AnyRef

  29. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  30. def factors(variables: Iterable[variable.Var]): Iterable[Factor]

    Return all Factors in this Model that touch any of the given "variables".

    Return all Factors in this Model that touch any of the given "variables". The result will not have any duplicate Factors.

    Definition Classes
    StackedChainNereModel → ChainModel → Model

  31. def factors(v: variable.Var): Iterable[model.Factor]

    Return all Factors in this Model that touch the given "variable".

    Return all Factors in this Model that touch the given "variable". The result will not have any duplicate Factors.

    Definition Classes
    ChainModel → Model

  32. def factors(dl: variable.DiffList): Iterable[model.Factor]

    Return all Factors in this Model that are affected by the given DiffList.

    Return all Factors in this Model that are affected by the given DiffList. The result will not have any duplicate Factors. By default returns just the factors that neighbor the DiffList.variables, but this method may be overridden for special handling of the DiffList

    Definition Classes
    Model

  33. def factors(d: variable.Diff): Iterable[model.Factor]

    Return all Factors in this Model that are affected by the given Diff.

    Return all Factors in this Model that are affected by the given Diff. The result will not have any duplicate Factors. By default returns just the factors that neighbor Diff.variable, but this method may be overridden for special handling of the Diff

    Definition Classes
    Model

  34. def factorsOfClass[F <: model.Factor](d: variable.DiffList)(implicit fm: ClassTag[F]): Iterable[F]

    Definition Classes
    Model

  35. def factorsOfClass[F <: model.Factor](d: variable.DiffList, fclass: Class[F]): Iterable[F]

    Definition Classes
    Model

  36. def factorsOfClass[F <: model.Factor](variables: Iterable[variable.Var])(implicit fm: ClassTag[F]): Iterable[F]

    Definition Classes
    Model

  37. def factorsOfClass[F <: model.Factor](variable: variable.Var)(implicit fm: ClassTag[F]): Iterable[F]

    Definition Classes
    Model

  38. def factorsOfClass[F <: model.Factor](variables: Iterable[variable.Var], fclass: Class[F]): Iterable[F]

    Definition Classes
    Model

  39. def factorsOfClass[F <: model.Factor](variable: variable.Var, fclass: Class[F]): Iterable[F]

    Definition Classes
    Model

  40. def factorsOfFamilies[F <: Family](d: variable.DiffList, families: Seq[F]): Iterable[model.Model.factorsOfFamilies.F.Factor]

    Definition Classes
    Model

  41. def factorsOfFamilies[F <: Family](variables: Iterable[variable.Var], families: Seq[F]): Iterable[model.Model.factorsOfFamilies.F.Factor]

    Definition Classes
    Model

  42. def factorsOfFamilies[F <: Family](variable: variable.Var, families: Seq[F]): Iterable[model.Model.factorsOfFamilies.F.Factor]

    Definition Classes
    Model

  43. def factorsOfFamily[F <: Family](d: variable.DiffList, family: F): Iterable[model.Model.factorsOfFamily.F.Factor]

    Definition Classes
    Model

  44. def factorsOfFamily[F <: Family](variables: Iterable[variable.Var], family: F): Iterable[model.Model.factorsOfFamily.F.Factor]

    Definition Classes
    Model

  45. def factorsOfFamily[F <: Family](variable: variable.Var, family: F): Iterable[model.Model.factorsOfFamily.F.Factor]

    Definition Classes
    Model

  46. def factorsOfFamilyClass[F <: Family](d: variable.DiffList)(implicit fm: ClassTag[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  47. def factorsOfFamilyClass[F <: Family](d: variable.DiffList, fclass: Class[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  48. def factorsOfFamilyClass[F <: Family](variables: Iterable[variable.Var])(implicit fm: ClassTag[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  49. def factorsOfFamilyClass[F <: Family](variable: variable.Var)(implicit fm: ClassTag[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  50. def factorsOfFamilyClass[F <: Family](variables: Iterable[variable.Var], fclass: Class[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  51. def factorsOfFamilyClass[F <: Family](variable: variable.Var, fclass: Class[F]): Iterable[model.Model.factorsOfFamilyClass.F.Factor]

    Definition Classes
    Model

  52. val featureClass: Class[_]

    Definition Classes
    ChainModel

  53. val featuresDomain: CategoricalVectorDomain[String]

    Definition Classes
    ChainModel

  54. def filterByFactorClass[F <: model.Factor](factors: Iterable[model.Factor], fclass: Class[F]): Iterable[F]

    Definition Classes
    Model

  55. def filterByFamilies[F <: Family](factors: Iterable[model.Factor], families: Seq[F]): Iterable[model.Model.filterByFamilies.F.Factor]

    Definition Classes
    Model

  56. def filterByFamily[F <: Family](factors: Iterable[model.Factor], family: F): Iterable[model.Model.filterByFamily.F.Factor]

    Definition Classes
    Model

  57. def filterByFamilyClass[F <: Family](factors: Iterable[model.Factor], fclass: Class[F]): Iterable[model.Model.filterByFamilyClass.F.Factor]

    Definition Classes
    Model

  58. def filterByNotFamilyClass[F <: Family](factors: Iterable[model.Factor], fclass: Class[F]): Iterable[model.Factor]

    Definition Classes
    Model

  59. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  61. def getCliqueValues(varying: Seq[L], addToLocalScoresOpt: Option[Array[la.Tensor1]] = None): ChainCliqueValues

    Definition Classes
    ChainModel

  62. def getHammingLossScores(varying: Seq[L with LabeledMutableDiscreteVar]): Array[la.Tensor1]

    Definition Classes
    ChainModel

  63. def getLocalScores(varying: Seq[L]): Array[DenseTensor1]

    Definition Classes
    StackedChainNereModel → ChainModel

  64. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  65. def inferFast(varying: Seq[L], addToLocalScoresOpt: Option[Array[la.Tensor1]] = None): ChainForwardBackwardResults

    Definition Classes
    ChainModel

  66. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  67. def itemizedModel(dl: variable.DiffList): ItemizedModel

    Definition Classes
    Model

  68. def itemizedModel(d: variable.Diff): ItemizedModel

    Definition Classes
    Model

  69. def itemizedModel(variables: Iterable[variable.Var]): ItemizedModel

    Definition Classes
    Model

  70. def itemizedModel(variable: variable.Var): ItemizedModel

    Definition Classes
    Model

  71. val labelClass: Class[_]

    Definition Classes
    ChainModel

  72. val labelDomain: CategoricalDomain[String]

    Definition Classes
    ChainModel

  73. val labelToFeatures: (L) ⇒ Features

    Definition Classes
    ChainModel

  74. val labelToToken: (L) ⇒ Token

    Definition Classes
    ChainModel

  75. val markov: DotFamilyWithStatistics2[L, L] { val weights: cc.factorie.model.Weights2 }

    Definition Classes
    ChainModel

  76. def maximize(vars: Seq[L])(implicit d: variable.DiffList): Unit

    Definition Classes
    ChainModel

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

    Definition Classes
    AnyRef

  78. def newFactorsCollection: Set[model.Factor]

    The "factors" methods need a new collection to return; this method is used by them to construct this collection.

    The "factors" methods need a new collection to return; this method is used by them to construct this collection.

    Definition Classes
    Model

  79. final def notify(): Unit

    Definition Classes
    AnyRef

  80. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  81. val obs: DotFamilyWithStatistics2[Features, L] { val weights: cc.factorie.model.Weights2 }

    Definition Classes
    ChainModel

  82. val obsmarkov: DotFamilyWithStatistics3[L, L, Features] { val weights: cc.factorie.model.Weights3 }

    Definition Classes
    ChainModel

  83. val parameters: WeightsSet

    Definition Classes
    Parameters

  84. def serialize(stream: OutputStream): Unit

    Definition Classes
    ChainModel

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

    Definition Classes
    AnyRef

  86. def toString(): String

    Definition Classes
    AnyRef → Any

  87. val tokenClass: Class[_]

    Definition Classes
    ChainModel

  88. val tokenToLabel: (Token) ⇒ L

    Definition Classes
    ChainModel

  89. var useObsMarkov: Boolean

    Definition Classes
    ChainModel

  90. def viterbiFast(varying: Seq[L], addToLocalScoresOpt: Option[Array[la.Tensor1]] = None): ChainViterbiResults

    Definition Classes
    ChainModel

  91. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from ChainModel[L, Features, Token]

Inherited from model.Parameters

Inherited from model.Model

Inherited from AnyRef

Inherited from Any

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