Instance Constructors
-
new
MSRChainChineseWordSegmenter(url: URL)
Value Members
-
final
def
!=(arg0: AnyRef): Boolean
-
final
def
!=(arg0: Any): Boolean
-
final
def
##(): Int
-
final
def
==(arg0: AnyRef): Boolean
-
final
def
==(arg0: Any): Boolean
-
-
final
def
asInstanceOf[T0]: T0
-
def
characterToFeatures(i: Int, labeledCharacters: IndexedSeq[(String, String)]): Seq[String]
-
def
clone(): AnyRef
-
def
deserialize(stream: InputStream): Unit
-
def
deserialize(filePath: String): Unit
-
def
documentAnnotationString(document: Document): String
-
final
def
eq(arg0: AnyRef): Boolean
-
def
equals(arg0: Any): Boolean
-
def
finalize(): Unit
-
final
def
getClass(): Class[_]
-
def
getF1Score(filePath: String): Double
-
-
-
-
def
hashCode(): Int
-
final
def
isInstanceOf[T0]: Boolean
-
def
mentionAnnotationString(mention: Mention): String
-
-
final
def
ne(arg0: AnyRef): Boolean
-
final
def
notify(): Unit
-
final
def
notifyAll(): Unit
-
def
phraseAnnotationString(phrase: Phrase): String
-
def
postAttrs: Seq[Class[_ >: Sentence with Token <: Attr]]
-
def
prereqAttrs: Seq[Nothing]
-
-
-
-
-
-
-
def
serialize(stream: OutputStream): Unit
-
def
serialize(filePath: String): Unit
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
-
def
toString(): String
-
def
tokenAnnotationString(token: Token): String
-
def
train(filePath: String): Unit
-
final
def
wait(): Unit
-
final
def
wait(arg0: Long, arg1: Int): Unit
-
final
def
wait(arg0: Long): Unit
A linear-chain CRF model for Chinese word segmentation with four companion objects, each pre-trained on a different corpus that corresponds to a different variety of written Mandarin.