org.apache.spark.mllib.feature

Word2Vec

class Word2Vec extends Serializable with Logging

:: Experimental :: Word2Vec creates vector representation of words in a text corpus. The algorithm first constructs a vocabulary from the corpus and then learns vector representation of words in the vocabulary. The vector representation can be used as features in natural language processing and machine learning algorithms.

We used skip-gram model in our implementation and hierarchical softmax method to train the model. The variable names in the implementation matches the original C implementation.

For original C implementation, see https://code.google.com/p/word2vec/ For research papers, see Efficient Estimation of Word Representations in Vector Space and Distributed Representations of Words and Phrases and their Compositionality.

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@Experimental()
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Instance Constructors

  1. new Word2Vec()

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  11. def fit[S <: Iterable[String]](dataset: JavaRDD[S]): Word2VecModel

    Computes the vector representation of each word in vocabulary (Java version).

    Computes the vector representation of each word in vocabulary (Java version).

    dataset

    a JavaRDD of words

    returns

    a Word2VecModel

  12. def fit[S <: Iterable[String]](dataset: RDD[S]): Word2VecModel

    Computes the vector representation of each word in vocabulary.

    Computes the vector representation of each word in vocabulary.

    dataset

    an RDD of words

    returns

    a Word2VecModel

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

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  16. def isTraceEnabled(): Boolean

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  17. def log: Logger

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  30. final def notify(): Unit

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  31. final def notifyAll(): Unit

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  32. def setLearningRate(learningRate: Double): Word2Vec.this.type

    Sets initial learning rate (default: 0.

    Sets initial learning rate (default: 0.025).

  33. def setMinCount(minCount: Int): Word2Vec.this.type

    Sets minCount, the minimum number of times a token must appear to be included in the word2vec model's vocabulary (default: 5).

  34. def setNumIterations(numIterations: Int): Word2Vec.this.type

    Sets number of iterations (default: 1), which should be smaller than or equal to number of partitions.

  35. def setNumPartitions(numPartitions: Int): Word2Vec.this.type

    Sets number of partitions (default: 1).

    Sets number of partitions (default: 1). Use a small number for accuracy.

  36. def setSeed(seed: Long): Word2Vec.this.type

    Sets random seed (default: a random long integer).

  37. def setVectorSize(vectorSize: Int): Word2Vec.this.type

    Sets vector size (default: 100).

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

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  39. def toString(): String

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