WordClusters

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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
val commonWords: Set[Symbol]
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def harvestFrequentWords(words: List[String], qualifyingCount: Int): Set[String]

Given a list of strings and a minimum threshold qualifyingCount, returns the set of strings that appear at least qualifyingCount times in the argument list.
Given a list of strings and a minimum threshold qualifyingCount, returns the set of strings that appear at least qualifyingCount times in the argument list.
words
the strings that we want to count
qualifyingCount
the minimum frequency of words to include in the return value
returns
the set of strings that appear at least qualifyingCount times in words
def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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val prefixes: Set[Symbol]
val ptbToUniversalPosTag: Map[String, String]

Maps standard Penn Treebank-style part-of-speech tags into the Google's "universal" POS set:
Maps standard Penn Treebank-style part-of-speech tags into the Google's "universal" POS set:
https://code.google.com/p/universal-pos-tags/
TODO: move this to the nlpstack.postag library
val puncWords: Set[Symbol]
val stopWords: Set[Symbol]
val suffixes: Set[Symbol]
final def synchronized[T0](arg0: ⇒ T0): T0

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def toString(): String

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final def wait(): Unit

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

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final def wait(arg0: Long): Unit

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def wordFrequencies(words: List[String]): Map[String, Int]

Given a list of strings, creates a histogram that maps the strings to their frequency in the list.
Given a list of strings, creates a histogram that maps the strings to their frequency in the list.
words
the strings that we want to count
returns
a mapping from strings to their frequency in the argument list

Related Doc: package core

object WordClusters

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val commonWords: Set[Symbol]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def harvestFrequentWords(words: List[String], qualifyingCount: Int): Set[String]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val prefixes: Set[Symbol]

val ptbToUniversalPosTag: Map[String, String]

val puncWords: Set[Symbol]

val stopWords: Set[Symbol]

val suffixes: Set[Symbol]

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def wordFrequencies(words: List[String]): Map[String, Int]

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