Introduction to word2phrase
When we communicate, we often know that individual words in the correct placements can change the meaning of what we’re trying to say. Add “very” in front of an adjective and you place more emphasis on the adjective. Add “york” in after the word “new” and you get a location. Throw in “times” after that and now it’s a newspaper.
It follows that when working with data, these meanings should be known. The three separate words “new”, “york”, and “times” are very different than “New York Times” as one phrase. This is where the word2phrase algorithm comes into play.
At its core, word2phrase takes in a sentence of individual words and potentially turns bigrams (two consecutive words) into a phrase by joining the two words together with a symbol (underscore in our case). Whether or not a bigram is turned into a phrase is determined by the training set and parameters set by the user. Note that every two consecutive words are considered, so in a sentence with w1 w2 w3, bigrams would be w1w2, w2w3.
In our word2phrase implementation in Spark (and done similarly in Gensim), there are two distinct steps; a training (estimator) step and application (transform) step.
*For clarity, note that “new york” is a bigram, while “new_york” is a phrase.
The training step is where we pass in a training set to the word2phrase estimator. The estimator takes this dataset and produces a model using the algorithm. The model is called the transformer, which we pass in datasets that we want to transform, i.e. sentences that with bigrams that we may want to transform to phrases.
In the training set, the dataset is an array of sentences. The algorithm will take these sentences and apply the following formula to give a score to each bigram:
score(wi, wj) = (count(wiwj) – delta) / (count(wi) * count(wj))
where wi and wj are word i and word j, and delta is discounting coefficient that can be set to prevent phrases consisting of infrequent words to be formed. So wiwj is when word j follows word i.
After the score for each bigram is calculated, those above a set threshold (this value can be changed by the user) will be transformed into phrases. The model produces by the estimator step is thus an array of bigrams; the ones that should be turned to phrases.
The transform step is incredibly simple; pass in any array of sentences to your model and it will search for matching bigrams. All matching bigrams in the array you passed in will then be turned to phrases.
You can repeat these steps to produce trigrams (i.e. three words into a phrase). For example, with “I read the New York Times” may produce “I read the new_york Times” after the first run, but run it again to get “I read the new_york_times”, because in the second run “new_york” is also an individual word now.
First we create our training dataset; it’s a dataframe where the occurrences “new york” and “test drive” appears frequently. (The sentences make no sense as they are randomly generated words. See below for link to full dataframe.)
You can copy/paste this into your spark shell to test it, so long as you have the word2phrase algorithm included (available as a maven package with coordinates com.reputation.spark:word2phrase:1.0.1).
Download the package, create our test dataframe:
spark-shell –packages com.reputation.spark.word2phrase.1.0.1
val wordDataFrame = sqlContext.createDataFrame(Seq(
(0, “new york test drive cool york how always learn media new york .”),
(1, “online york new york learn to media cool time .”),
(2, “media play how cool times play .”),
(3, “code to to code york to loaded times media .”),
(4, “play awesome to york .”),
(1099, “work please ideone how awesome times .”),
(1100, “play how play awesome to new york york awesome use new york work please loaded always like .”),
(1101, “learn like I media online new york .”),
(1102, “media follow learn code code there to york times .”),
(1103, “cool use play work please york cool new york how follow .”),
(1104, “awesome how loaded media use us cool new york online code judge ideone like .”),
(1105, “judge media times time ideone new york new york time us fun .”),
(1106, “new york to time there media time fun there new like media time time .”),
(1107, “awesome to new times learn cool code play how to work please to learn to .”),
(1108, “there work please online new york how to play play judge how always work please .”),
(1109, “fun ideone to play loaded like how .”),
(1110, “fun york test drive awesome play times ideone new us media like follow .”)
We set the input and output column names and create the model (the estimator step, represented by the fit(wordDataFrame) function).
scala> val t = new Word2Phrase().setInputCol(“inputWords”).setOutputCol(“out”)
t: org.apache.spark.ml.feature.Word2Phrase = deltathresholdScal_f07fb0d91c1f
scala> val model = t.fit(wordDataFrame)
Here are some of the scores (Table 1) calculated by the algorithm before removing those below the threshold (note all the scores above the threshold are shown here). The default values have delta -> 100, threshold -> 0.00001, and minWords -> 0.
only showing top 10 rows
So our model produces three bigrams that will be searched for in the transform step:
We then use this model to transform our original dataframe sentences and view the results. Unfortunately you can’t see the entire row in the spark-shell, but in the out column it’s clear that all instances of “new york” and “test drive” have been transformed into “new_york” and “test_drive”.
scala> val bi_gram_data = model.transform(wordDataFrame)
bi_gram_data: org.apache.spark.sql.DataFrame = [label: int, inputWords: string … 1 more field]
|0||new york test dri…||new_york test_dri…|
|1||online york new y…||online york new_y…|
|2||media play how co…||media play how co…|
|3||code to to code y…||code to to code y…|
|4||play awesome to y…||play awesome to y…|
|5||like I I always .||like I I always .|
|6||how to there lear…||how to there lear…|
|7||judge time us pla…||judge time us pla…|
|8||judge test drive …||judge test_drive …|
|9||judge follow fun …||judge follow fun …|
|10||how I follow ideo…||how I follow ideo…|
|11||use use learn I t…||use use learn I t…|
|12||us new york alway…||us new_york alway…|
|13||there always how …||there always how …|
|14||always time media…||always time media…|
|15||how test drive to…||how test_drive to…|
|16||cool us online ti…||cool us online ti…|
|17||follow time aweso…||follow time aweso…|
|18||us york test driv…||us york test_driv…|
|19||use fun new york …||use fun new_york …|
only showing top 20 rows
The algorithm and test dataset (testSentences.scala) are available at this repository.