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org.clulab.embeddings

LemmatizeEmbeddings

Related Docs: object LemmatizeEmbeddings | package embeddings

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class LemmatizeEmbeddings extends AnyRef

Generates embeddings for lemmas, by averaging GloVe embeddings for words that have the same lemma The averaging of embedding vectors is weighted by the frequency of the corresponding words in Gigaword

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Instance Constructors

  1. new LemmatizeEmbeddings(frequencyFile: String, embeddingFile: String)

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Value Members

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

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  2. final def ##(): Int

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

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  4. def add(e: HashMap[String, Array[Double]], lemma: String, v: Array[Double]): Unit

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  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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  7. def divide(v: Array[Double], s: Double): Unit

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  8. val embeddingFile: String

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

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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  12. val frequencies: Map[String, Double]

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  13. val frequencyFile: String

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

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. def lemmatize(): Map[String, Array[Double]]

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  18. def loadEmbeddings(): Map[String, Array[Double]]

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  19. def loadFreqFile(): Map[String, Double]

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  20. def multiply(v: Array[Double], s: Double): Unit

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

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  22. final def notify(): Unit

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  23. final def notifyAll(): Unit

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

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  25. def toString(): String

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  26. final def wait(): Unit

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

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

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  29. val wordEmbeddings: Map[String, Array[Double]]

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