e1571874f2
Moved TF-IDF before Word2Vec because the former is more basic. I also added a link for Word2Vec. atalwalkar Author: Xiangrui Meng <meng@databricks.com> Closes #2061 from mengxr/tfidf-doc and squashes the following commits: ca04c70 [Xiangrui Meng] address comments a5ea4b4 [Xiangrui Meng] add tf-idf user guide
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| layout | title | displayTitle |
|---|---|---|
| global | Feature Extraction - MLlib | <a href="mllib-guide.html">MLlib</a> - Feature Extraction |
- Table of contents {:toc}
TF-IDF
Term frequency-inverse document frequency (TF-IDF) is a feature
vectorization method widely used in text mining to reflect the importance of a term to a document in the corpus.
Denote a term by $t$, a document by $d$, and the corpus by $D$.
Term frequency $TF(t, d)$ is the number of times that term $t$ appears in document $d$,
while document frequency $DF(t, D)$ is the number of documents that contains term $t$.
If we only use term frequency to measure the importance, it is very easy to over-emphasize terms that
appear very often but carry little information about the document, e.g., "a", "the", and "of".
If a term appears very often across the corpus, it means it doesn't carry special information about
a particular document.
Inverse document frequency is a numerical measure of how much information a term provides:
\[ IDF(t, D) = \log \frac{|D| + 1}{DF(t, D) + 1}, \]
where $|D|$ is the total number of documents in the corpus.
Since logarithm is used, if a term appears in all documents, its IDF value becomes 0.
Note that a smoothing term is applied to avoid dividing by zero for terms outside the corpus.
The TF-IDF measure is simply the product of TF and IDF:
\[ TFIDF(t, d, D) = TF(t, d) \cdot IDF(t, D). \]
There are several variants on the definition of term frequency and document frequency.
In MLlib, we separate TF and IDF to make them flexible.
Our implementation of term frequency utilizes the
hashing trick.
A raw feature is mapped into an index (term) by applying a hash function.
Then term frequencies are calculated based on the mapped indices.
This approach avoids the need to compute a global term-to-index map,
which can be expensive for a large corpus, but it suffers from potential hash collisions,
where different raw features may become the same term after hashing.
To reduce the chance of collision, we can increase the target feature dimension, i.e.,
the number of buckets of the hash table.
The default feature dimension is $2^{20} = 1,048,576$.
Note: MLlib doesn't provide tools for text segmentation. We refer users to the Stanford NLP Group and scalanlp/chalk.
TF and IDF are implemented in HashingTF
and IDF.
HashingTF takes an RDD[Iterable[_]] as the input.
Each record could be an iterable of strings or other types.
{% highlight scala %} import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext import org.apache.spark.mllib.feature.HashingTF import org.apache.spark.mllib.linalg.Vector
val sc: SparkContext = ...
// Load documents (one per line). val documents: RDD[Seq[String]] = sc.textFile("...").map(_.split(" ").toSeq)
val hashingTF = new HashingTF() val tf: RDD[Vector] = hasingTF.transform(documents) {% endhighlight %}
While applying HashingTF only needs a single pass to the data, applying IDF needs two passes:
first to compute the IDF vector and second to scale the term frequencies by IDF.
{% highlight scala %} import org.apache.spark.mllib.feature.IDF
// ... continue from the previous example tf.cache() val idf = new IDF().fit(tf) val tfidf: RDD[Vector] = idf.transform(tf) {% endhighlight %}
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 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.
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 %}