MEMMSequenceTagger

abstract def featureExtractor(features: Counter[F], sentence: Sentence, offset: Int): Unit

Abstract method that generates the features for the word at the position offset in the given sentence

abstract def labelExtractor(sentence: Sentence): Array[L]

Abstract method that extracts the training labels for a given sentence

abstract def mkFeatAtBeginSent(position: Int, prefix: String): F

abstract def mkFeatAtEndSent(position: Int, prefix: String): F

abstract def mkFeatAtHistory(position: Int, prefix: String, label: L): F

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def addFirstPassFeatures(features: Counter[F], order: Int, labels: Seq[L], offset: Int): Unit

def addHistoryFeatures(features: Counter[F], order: Int, labels: Seq[L], offset: Int): Unit

def addLeftFeatures(features: Counter[F], order: Int, prefix: String, labels: Seq[L], offset: Int): Unit

def addRightFeatures(features: Counter[F], order: Int, prefix: String, labels: Seq[L], offset: Int): Unit

final def asInstanceOf[T0]: T0

def classesOf(origSentence: Sentence): Array[L]

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def find(sentence: Sentence): Array[L]

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

var leftToRight: Boolean

def load(reader: BufferedReader): Unit

def loadFromFile(fn: File): Unit

def loadFromResource(rn: String): Unit

var model: Option[Classifier[L, F]]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

var order: Int

def save(fn: File): Unit

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

def toString(): String

def train(docs: Iterator[Document]): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit