Package org.apache.spark.mllib.feature

Class Word2Vec

Object
org.apache.spark.mllib.feature.Word2Vec
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging
public class Word2Vec extends Object implements Serializable, org.apache.spark.internal.Logging
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.

See Also:

  • Nested Class Summary

    Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

    org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

  • Constructor Summary

    Constructors
    Constructor
    Description
    Word2Vec()
     

  • Method Summary

    Modifier and Type
    Method
    Description
    <S extends Iterable<String>>
    Word2VecModel
    fit(JavaRDD<S> dataset)
    Computes the vector representation of each word in vocabulary (Java version).
    <S extends scala.collection.Iterable<String>>
    Word2VecModel
    fit(RDD<S> dataset)
    Computes the vector representation of each word in vocabulary.
    Word2Vec
    setLearningRate(double learningRate)
    Sets initial learning rate (default: 0.025).
    Word2Vec
    setMaxSentenceLength(int maxSentenceLength)
    Sets the maximum length (in words) of each sentence in the input data.
    Word2Vec
    setMinCount(int minCount)
    Sets minCount, the minimum number of times a token must appear to be included in the word2vec model's vocabulary (default: 5).
    Word2Vec
    setNumIterations(int numIterations)
    Sets number of iterations (default: 1), which should be smaller than or equal to number of partitions.
    Word2Vec
    setNumPartitions(int numPartitions)
    Sets number of partitions (default: 1).
    Word2Vec
    setSeed(long seed)
    Sets random seed (default: a random long integer).
    Word2Vec
    setVectorSize(int vectorSize)
    Sets vector size (default: 100).
    Word2Vec
    setWindowSize(int window)
    Sets the window of words (default: 5)

    Methods inherited from class java.lang.Object

    equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    Methods inherited from interface org.apache.spark.internal.Logging

    initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext

  • Constructor Details

    • Word2Vec

      public Word2Vec()

  • Method Details

    • fit

      public <S extends scala.collection.Iterable<String>> Word2VecModel fit(RDD<S> dataset)
      Computes the vector representation of each word in vocabulary.
      Parameters:
      dataset - an RDD of sentences, each sentence is expressed as an iterable collection of words
      Returns:
      a Word2VecModel

    • fit

      public <S extends Iterable<String>> Word2VecModel fit(JavaRDD<S> dataset)
      Computes the vector representation of each word in vocabulary (Java version).
      Parameters:
      dataset - a JavaRDD of words
      Returns:
      a Word2VecModel

    • setLearningRate

      public Word2Vec setLearningRate(double learningRate)
      Sets initial learning rate (default: 0.025).
      Parameters:
      learningRate - (undocumented)
      Returns:
      (undocumented)

    • setMaxSentenceLength

      public Word2Vec setMaxSentenceLength(int maxSentenceLength)
      Sets the maximum length (in words) of each sentence in the input data. Any sentence longer than this threshold will be divided into chunks of up to maxSentenceLength size (default: 1000)
      Parameters:
      maxSentenceLength - (undocumented)
      Returns:
      (undocumented)

    • setMinCount

      public Word2Vec setMinCount(int minCount)
      Sets minCount, the minimum number of times a token must appear to be included in the word2vec model's vocabulary (default: 5).
      Parameters:
      minCount - (undocumented)
      Returns:
      (undocumented)

    • setNumIterations

      public Word2Vec setNumIterations(int numIterations)
      Sets number of iterations (default: 1), which should be smaller than or equal to number of partitions.
      Parameters:
      numIterations - (undocumented)
      Returns:
      (undocumented)

    • setNumPartitions

      public Word2Vec setNumPartitions(int numPartitions)
      Sets number of partitions (default: 1). Use a small number for accuracy.
      Parameters:
      numPartitions - (undocumented)
      Returns:
      (undocumented)

    • setSeed

      public Word2Vec setSeed(long seed)
      Sets random seed (default: a random long integer).
      Parameters:
      seed - (undocumented)
      Returns:
      (undocumented)

    • setVectorSize

      public Word2Vec setVectorSize(int vectorSize)
      Sets vector size (default: 100).
      Parameters:
      vectorSize - (undocumented)
      Returns:
      (undocumented)

    • setWindowSize

      public Word2Vec setWindowSize(int window)
      Sets the window of words (default: 5)
      Parameters:
      window - (undocumented)
      Returns:
      (undocumented)