For classifying or clustering data in the context of a machine learning problem, the first step is to create a representation of data, usually called the Feature Vector. Datasets consisting of images or audio files have feature vectors that are already in numeric form. If we have text data, we have to convert words /characters into numbers.

For a number of years, the Bag of words approach was used to create a Feature Vector. This approach required the use of a dictionary which contains all the words used in the dataset.

Assume that we have a dictionary consisting of the words {“the”, “sleepy”,”happy”,”cat”,”dog”}. If we encounter 2 sentences :”the sleepy cat” and “the happy dog”, we replace the words with the index in the dictionary. Thus “the sleepy cat” becomes “0,1,3”, and “the happy dog” translates to “0,2,5” .

The problem with this approach is that it ignores similarities between words. For example, ‘Cricket’, ‘Batsman’ & ‘Bowler’ are related terms from the game of Cricket, but the use of Bag of words gives each of the words a different number which doesn’t convey that they are related.

Word2Vec is a tool developed by Mikolov et al which is capable of generating feature vectors from text data, and these feature vectors encode relationships that words share with each other. When given a (word’s) feature vector, we can find words that are similar to the given word by using a distance function.

Multiple implementations of Word2Vec are available, and in this post we introduce a Clojure wrapper to a Java implementation of Word2vec. The source and the documentation are available at Github.

Introduction to clojure-word2vec

We’ll use a dataset (consists of forum postings on Apple products) to try out the capabilities of Word2vec. We read the Apple dataset and train a Word2vec model on the data. This is a modified version of the Apple dataset, which can be downloaded here.

We read in the dataset using the read-filtered-dataset function and then train it on word2vec.

(def appvec
  (-> (read-filtered-dataset "resources/apple-data.txt.gz") word2vec))

Let’s view the top 20 words in the vocabulary.

(take 20 (.getVocab appvec))
("ipod" "drive" "problem" "computer" "itune" "apple" "disc" 
"nano" "song" "thank" "mac" "os" "screen" "card" "time" 
"system" "music" "g5" "display" "file")

We can see from the words that the common terms were either Apple products or related to them.

The total number of words in the vocabulary

(.getVocabSize (.toThrift appvec))
    6813

Given that Word2vec transforms a word into a high dimension vector, we can compute the similarity of 2 words by comparing the corresponding word vectors using a distance metric like cosine similarity

To calculate the distance between 2 words, g5 and ipod, we fetch the raw word vectors and compute distance using the Incanter cosine-similarity API.

(defn cosine-sim
  [model word1 word2]
  (let [rawvecfn  #(.getRawVector (.forSearch model) %)

(cosine-sim appvec "g5" "ipod")

The resulting score is a value between 0 and 1, where a high score indicates that the words lie close to each other in the vector space model.

0.9988300697889931

Some examples of the relations API:

In the original paper, the example offered was: “if Paris is related to France, Berlin is related to ?” and the query would find Germany as the answer.

In the Apple dataset (as with any other dataset), the relationship found are usually noisy. Lets look at some good answers

If Nano is a ‘kind of’ ipod, (The Ipod Nano was a bestselling model of the Ipod line) then g3 is a

(get-relations appvec "nano" "ipod"  "g3")
    ("mac" "imac" "ibook" "installation" "system")

If ghz is a measure of speed, then gb is a measure of

(get-relations appvec "ghz" "speed" "gb")
    ("data" "hd" "backup" "size" "cache")

We’d expect memory to be the right answer, but data, the 1st item on the list, is a reasonable approximation.

If 300gb is the measure of a drive, then 2ghz is a

(get-relations appvec "300gb" "drive" "2ghz")
    ("imac" "system" "processor" "upgrade" "model")

measure of a processor (3nd item)

Airport is a product line for wifi basestations, and Extreme is one of the products in that line, (a wifi base station made by apple) what’s a model in the ipod line?

(get-relations appvec "airport" "extreme" "ipod")
    ("nano" "content" "library" "music" "shuffle")

The nano (1nd item)


There’s a lot of noise in the answers however. When we query for a G5 instead (a desktop computer)

(get-relations appvec "nano" "ipod"  "g5")
    ("speed" "raid" "quad" "model" "performance")

We don’t find a desktop in the top 5 answers.


We can use the get-matches API to return the words that are closest (by euclidean distance) to the queried word. Here are a few examples

Radeon is a video card

(get-matches appvec "radeon")
    ("card" "ati" "dual" "g5" "agp" "nvidia" "ghz" "graphic" "pcie" "model")

The top few answers suggest that it is a card, and the manufacturer is ATI.

(get-matches appvec "seagate")
    ("maxtor" "gb" "raid" "drive" "quad" "speed" "raptor" "performance" "digital" "enclosure")
(get-matches appvec "nano")
    ("ipod" "gen" "track" "music" "shuffle" "content" "itune" "library" "play" "ipods")

Conclusion

  • Word2vec is an excellent tool to find co-occurances of words in a corpus. Depending on the kind of data, it may be possible to determine relationships as well.
  • We used the Apple dataset as it has content that was annotated with Part of Speech tags (such as nouns, verbs). For this exercise, we only used words that were nouns or were part of noun phrases.
  • Word2vec can tell us what is being discussed about something. We can see from the relations API that customers talking about Seagate (A hard disk manufacturer) are concerned about sizes, speed, performance and enclosures.

This blog is written by Kiran Karkera, Lead Data Scientist at BRIDGEi2i

About BRIDGEi2i: BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. Our analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems and make data driven decisions across pan-enterprise processes to create sustainable business impact. To know more visit www.bridgei2i.com