epic.parser.models

SpanModel

class SpanModel[L, L2, W] extends ParserModel[L, W] with Serializable

A rather more sophisticated discriminative parser. Uses features on the underlying span.

Annotations
@SerialVersionUID( 1L )
Linear Supertypes
Serializable, Serializable, ParserModel[L, W], ParserExtractable[L, W], Model[TreeInstance[L, W]], Model[TreeInstance[L, W]], SafeLogging, AnyRef, Any
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Inherited
  1. SpanModel
  2. Serializable
  3. Serializable
  4. ParserModel
  5. ParserExtractable
  6. Model
  7. Model
  8. SafeLogging
  9. AnyRef
  10. Any
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  1. Public
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Instance Constructors

  1. new SpanModel(featurizer: RefinedFeaturizer[L, W, Feature], featureIndex: Index[Feature], annotator: (BinarizedTree[L], IndexedSeq[W]) ⇒ BinarizedTree[IndexedSeq[L2]], constrainer: Factory[L, W], topology: RuleTopology[L], lexicon: Lexicon[L, W], refinedGrammar: RuleTopology[L2], refinements: GrammarRefinements[L, L2], initialFeatureVal: (Feature) ⇒ Option[Double] = ...)

Type Members

  1. type ExpectedCounts = StandardExpectedCounts[Feature]

    Definition Classes
    Model → Model

  2. type Inference = LatentParserInference[L, L2, W]

    Definition Classes
    SpanModel → ParserModel → Model

  3. type Marginal = ParseMarginal[L, W]

    Definition Classes
    ParserModel → Model

  4. type Scorer = GrammarAnchoring[L, W]

    Definition Classes
    ParserModel → Model

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. def accumulateCounts(inf: Inference, s: Scorer, d: TreeInstance[L, W], m: Marginal, accum: ExpectedCounts, scale: Double): Unit

    Definition Classes
    SpanModel → Model

  7. final def accumulateCounts(inf: Inference, d: TreeInstance[L, W], accum: ExpectedCounts, scale: Double): Unit

    Definition Classes
    Model

  8. val annotator: (BinarizedTree[L], IndexedSeq[W]) ⇒ BinarizedTree[IndexedSeq[L2]]

  9. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  10. def cacheFeatureWeights(weights: DenseVector[Double], suffix: String = ""): Unit

    Caches the weights using the cache broker.

    Caches the weights using the cache broker.

    Definition Classes
    Model

  11. def clone(): AnyRef

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

  12. val constrainer: Factory[L, W]

    Definition Classes
    SpanModel → ParserExtractable

  13. def emptyCounts: StandardExpectedCounts[Feature]

    Definition Classes
    Model → Model

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

    Definition Classes
    AnyRef

  15. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  16. final def expectedCounts(inf: Inference, d: TreeInstance[L, W], scale: Double = 1.0): ExpectedCounts

    Definition Classes
    Model

  17. def expectedCountsToObjective(ecounts: ExpectedCounts): (Double, DenseVector[Double])

    Definition Classes
    Model → Model

  18. def extractParser(weights: DenseVector[Double])(implicit deb: Debinarizer[L]): Parser[L, W]

    Definition Classes
    ParserModel → ParserExtractable

  19. val featureIndex: Index[Feature]

    Models have features, and this defines the mapping from indices in the weight vector to features.

    Models have features, and this defines the mapping from indices in the weight vector to features.

    returns

    Definition Classes
    SpanModel → Model

  20. val featurizer: RefinedFeaturizer[L, W, Feature]

  21. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  23. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  24. def inferenceFromWeights(weights: DenseVector[Double]): LatentParserInference[L, L2, W]

    Definition Classes
    SpanModel → Model

  25. def initialValueForFeature(f: Feature): Double

    Definition Classes
    SpanModel → Model

  26. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  27. val lexicon: Lexicon[L, W]

    Definition Classes
    SpanModel → ParserExtractable

  28. def logger: Logger

    Definition Classes
    SafeLogging

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

    Definition Classes
    AnyRef

  30. final def notify(): Unit

    Definition Classes
    AnyRef

  31. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  32. def numFeatures: Int

    Definition Classes
    Model

  33. def readCachedFeatureWeights(suffix: String = ""): Option[DenseVector[Double]]

    just saves feature weights to disk as a serialized counter.

    just saves feature weights to disk as a serialized counter. The file is prefix.ser.gz

    Definition Classes
    Model

  34. val refinedGrammar: RuleTopology[L2]

  35. val refinements: GrammarRefinements[L, L2]

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

    Definition Classes
    AnyRef

  37. def toString(): String

    Definition Classes
    AnyRef → Any

  38. val topology: RuleTopology[L]

    Definition Classes
    SpanModel → ParserExtractable

  39. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  42. def weightsCacheName: String

    Attributes
    protected
    Definition Classes
    Model

Inherited from Serializable

Inherited from Serializable

Inherited from ParserModel[L, W]

Inherited from ParserExtractable[L, W]

Inherited from Model[TreeInstance[L, W]]

Inherited from Model[TreeInstance[L, W]]

Inherited from SafeLogging

Inherited from AnyRef

Inherited from Any

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