RuleBasedContextEngine

abstract def extractEntryFeatures(entry: FriesEntry): Array[(String, Double)]

abstract def inferContext: List[Seq[Int]]

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def assign(mentions: Seq[BioMention]): Seq[BioMention]

assigns context to mentions given current state of the engine

def clone(): AnyRef

def densifyFeatures: Seq[Seq[Double]]

var docOffsets: Map[String, Int]

var entries: Seq[FriesEntry]

var entryFeatures: Seq[Array[(String, Double)]]

def entryFeaturesNames: Seq[String]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

var fallbackSpecies: Option[String]

def featureMatrix: Seq[Seq[Double]]

def finalize(): Unit

final def getClass(): Class[_]

def getObservationsMatrixStrings: Seq[String]

def getStatesMatrixStrings: Seq[String]

def hashCode(): Int

def infer(entries: Seq[FriesEntry], documents: Seq[Document], mentionsPerEntry: Seq[Seq[BioMention]]): Unit

initializes any data structure that needs to be initialized

var inferedLatentSparseMatrix: List[Seq[Int]]

final def isInstanceOf[T0]: Boolean

var latentSparseMatrix: List[Seq[Int]]

def latentStateMatrix: Seq[Seq[Boolean]]

def latentVocabulary: Iterable[String]

var mentions: Seq[Seq[BioMention]]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def observationVocavulary: Iterable[String]

var observedSparseMatrix: Seq[Seq[Int]]

def preprocessedLatentStateMatrix: Seq[Seq[Boolean]]

def query(line: Int): Map[String, Seq[String]]

* Queries the context of the specified line line.

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

def toString(): String

def update(mentions: Seq[BioMention]): Unit

updates those data structures with any new info

final def wait(): Unit

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

final def wait(arg0: Long): Unit