server/src/main/resources/eclairjs/mllib/feature/Word2Vec.js
function (jvmObject) {

        this.logger = Logger.getLogger("mllib_feature_Word2Vec_js");
        if (!jvmObject) {
            jvmObject = new org.apache.spark.mllib.feature.Word2Vec();
        }
        JavaWrapper.call(this, jvmObject);

    }
function (jvmObject) {

        this.logger = Logger.getLogger("mllib_feature_Word2Vec_js");
        if (!jvmObject) {
            jvmObject = new org.apache.spark.mllib.feature.Word2Vec();
        }
        JavaWrapper.call(this, jvmObject);

    }
function ( jvmObject ) { this . logger = Logger . getLogger ( "mllib_feature_Word2Vec_js" ) ; if ( ! jvmObject ) { jvmObject = new org . apache . spark . mllib . feature . Word2Vec ( ) ; } JavaWrapper . call ( this , jvmObject ) ; }
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.
@memberof module:eclairjs/mllib/feature
@classdesc
@class
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 .