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---
layout: global
title: Feature Extraction - MLlib
displayTitle: <a href="mllib-guide.html">MLlib</a> - Feature Extraction
---
* Table of contents
{:toc}
## Word2Vec
Word2Vec computes distributed vector representation of words. The main advantage of the distributed
representations is that similar words are close in the vector space, which makes generalization to
novel patterns easier and model estimation more robust. Distributed vector representation is
showed to be useful in many natural language processing applications such as named entity
recognition, disambiguation, parsing, tagging and machine translation.
### Model
In our implementation of Word2Vec, we used skip-gram model. The training objective of skip-gram is
to learn word vector representations that are good at predicting its context in the same sentence.
Mathematically, given a sequence of training words `$w_1, w_2, \dots, w_T$`, the objective of the
skip-gram model is to maximize the average log-likelihood
`\[
\frac{1}{T} \sum_{t = 1}^{T}\sum_{j=-k}^{j=k} \log p(w_{t+j} | w_t)
\]`
where $k$ is the size of the training window.
In the skip-gram model, every word $w$ is associated with two vectors $u_w$ and $v_w$ which are
vector representations of $w$ as word and context respectively. The probability of correctly
predicting word $w_i$ given word $w_j$ is determined by the softmax model, which is
`\[
p(w_i | w_j ) = \frac{\exp(u_{w_i}^{\top}v_{w_j})}{\sum_{l=1}^{V} \exp(u_l^{\top}v_{w_j})}
\]`
where $V$ is the vocabulary size.
The skip-gram model with softmax is expensive because the cost of computing $\log p(w_i | w_j)$
is proportional to $V$, which can be easily in order of millions. To speed up training of Word2Vec,
we used hierarchical softmax, which reduced the complexity of computing of $\log p(w_i | w_j)$ to
$O(\log(V))$
### Example
The example below demonstrates how to load a text file, parse it as an RDD of `Seq[String]`,
construct a `Word2Vec` instance and then fit a `Word2VecModel` with the input data. Finally,
we display the top 40 synonyms of the specified word. To run the example, first download
the [text8](http://mattmahoney.net/dc/text8.zip) data and extract it to your preferred directory.
Here we assume the extracted file is `text8` and in same directory as you run the spark shell.
<div class="codetabs">
<div data-lang="scala">
{% highlight scala %}
import org.apache.spark._
import org.apache.spark.rdd._
import org.apache.spark.SparkContext._
import org.apache.spark.mllib.feature.Word2Vec
val input = sc.textFile("text8").map(line => line.split(" ").toSeq)
val word2vec = new Word2Vec()
val model = word2vec.fit(input)
val synonyms = model.findSynonyms("china", 40)
for((synonym, cosineSimilarity) <- synonyms) {
println(s"$synonym $cosineSimilarity")
}
{% endhighlight %}
</div>
</div>
## TFIDF
|
