Word2Vec is a semantic learning framework that uses a shallow neural network to learn the representations of words/phrases in a particular text. Simply put, its an algorithm that takes in all the terms (with repetitions) in a particular document, divided into sentences, and outputs a vectorial form of each. The ‘advantage’ word2vec offers is in its utilization of a neural model in understanding the semantic meaning behind those terms. For example, a document may employ the words ‘dog’ and ‘canine’ to mean the same thing, but never use them together in a sentence. Ideally, Word2Vec would be able to learn the context and place them together in its semantic space. Most applications of Word2Vec using cosine similarity to quantify closeness. This Quora question (or rather its answers) does a good job of explaining the intuition behind it.

You would need to take the following steps to develop a Word2Vec model from a block of text (Usually, documents that are extensive and yet stick to the topic of interest with minimum ambiguity do well):

[I use Gensim’s Word2Vec API in Python to form Word2Vec models of Wikipedia articles.]

1. Obtain the text (obviously)

To obtain the Wikipedia articles, I use the Python wikipedia library. Once installed from the link, here’s how you could use it obtain all the text from an aritcle-

#'title' denotes the exact title of the article to be fetched title = "Machine learning" from wikipedia import page wikipage = page(title)

You could then use wikipage.context to access the entire textual context in the form of a String. Now, incase you don’t have the exact title and want to do a search, you would do:

from wikipedia import search, page titles = search('machine learning') wikipage = page(titles[0])

[Tip: Store the content into a file and access it from there. This would provide you a reference later, if needed.]

2. Preprocess the text

In the context of Python, you would require an iterable that yields one iterable for each sentence in the text. The inner iterable would contain the terms in the particular sentence. A ‘term’ could be individual words like ‘machine’, or phrases(n-grams) like ‘machine learning’, or a combination of both. Coming up with appropriate bigrams/trigrams is a tricky task on its own, so I just stick to unigrams.

First of all, I remove all special characters and short lines from the article, to eliminate noise. Then, I use Porter Stemming on my unigrams, using a ‘wrapper’ around Gensim’s stemming API.

from gensim.parsing import PorterStemmer global_stemmer = PorterStemmer() class StemmingHelper(object): """ Class to aid the stemming process - from word to stemmed form, and vice versa. The 'original' form of a stemmed word will be returned as the form in which its been used the most number of times in the text. """ #This reverse lookup will remember the original forms of the stemmed #words word_lookup = {} @classmethod def stem(cls, word): """ Stems a word and updates the reverse lookup. """ #Stem the word stemmed = global_stemmer.stem(word) #Update the word lookup if stemmed not in cls.word_lookup: cls.word_lookup[stemmed] = {} cls.word_lookup[stemmed][word] = ( cls.word_lookup[stemmed].get(word, 0) + 1) return stemmed @classmethod def original_form(cls, word): """ Returns original form of a word given the stemmed version, as stored in the word lookup. """ if word in cls.word_lookup: return max(cls.word_lookup[word].keys(), key=lambda x: cls.word_lookup[word][x]) else: return word

Refer to the code and docstrings to understand how it works. (Its pretty simple anyways). It can be used as follows-

>>> StemmingHelper.stem('learning') 'learn' >>> StemmingHelper.original_form('learn') 'learning'

Pre-stemming, you could also use a list of stopwords to eliminate terms that occur frequently in the English language, but don’t carry much semantic meaning.

After your pre-processing, lets assume you come up with an iterable called sentences from your source of text.

3. Figure out the values for your numerical parameters

Gensim’s Word2Vec API requires some parameters for initialization. Ofcourse they do have default values, but you want to define some on your own:

i. size – Denotes the number of dimensions present in the vectorial forms. If you have read the document and have an idea of how many ‘topics’ it has, you can use that number. For sizeable blocks, people use 100-200. I use around 50 for the Wikipedia articles. Usually, you would want to repeat the initialization for different numbers of topics in a certain range, and pick the one that yields the best results (depending on your application – I will be using them to build Mind-Maps, and I usually have to try values from 20-100.). A good heuristic thats frequently used is the square-root of the length of the vocabulary, after pre-processing.

ii. min_count – Terms that occur less than min_count number of times are ignored in the calculations. This reduces noise in the semantic space. I use 2 for Wikipedia. Usually, the bigger and more extensive your text, the higher this number can be.

iii. window – Only terms hat occur within a window-neighbourhood of a term, in a sentence, are associated with it during training. The usual value is 4. Unless your text contains big sentences, leave it at that.

iv. sg – This defines the algorithm. If equal to 1 , the skip-gram technique is used. Else, the CBoW method is employed. (Look at the aforementioned Quora answers). I usually use the default(1).

4. Initialize the model and use it

The model can be generated using Gensim’s API, as follows:

from gensim.models import Word2Vec min_count = 2 size = 50 window = 4 model = Word2Vec(sentences, min_count=min_count, size=size, window=window)

Now that you have the model initialized, you can access all the terms in its vocabulary, using something like list(model.vocab.keys()) . To get the vectorial representation of a particular term, use model[term] . If you have used my stemming wrapper, you could find the appropriate original form of the stemmed terms using StemmingHelper.original_form(term) . Heres an example, from the Wiki article on Machine learning:

>>> vocab = list(model.vocab.keys()) >>> vocab[:10] [u'represent', u'concept', u'founder', u'focus', u'invent', u'signific', u'abil', u'implement', u'benevol', u'hierarch'] >>> 'learn' in model.vocab True >>> model['learn'] array([ 1.23792759e-03, 5.49776992e-03, 2.18261080e-03, 8.37465748e-03, -6.10323064e-03, -6.94877980e-03, 6.29429379e-03, -7.06598908e-03, -7.16267806e-03, -2.78065586e-03, 7.40372669e-03, 9.68673080e-03, -4.75220988e-03, -8.34807567e-03, 5.25208283e-03, 8.43616109e-03, -1.07231298e-02, -3.88528360e-03, -9.20894090e-03, 4.17305576e-03, 1.90116244e-03, -1.92442467e-03, 2.74807960e-03, -1.01113841e-02, -3.71694425e-03, -6.60350174e-03, -5.90716442e-03, 3.90679482e-03, -5.32188127e-03, 5.63300075e-03, -5.52612450e-03, -5.57334488e-03, -8.51202477e-03, -8.78736563e-03, 6.41061319e-03, 6.64879987e-03, -3.55080629e-05, 4.81080823e-03, -7.11903954e-03, 9.83678619e-04, 1.60697231e-03, 7.42980337e-04, -2.12235347e-04, -8.05167668e-03, 4.08948492e-03, -5.48054813e-04, 8.55423324e-03, -7.08682090e-03, 1.57684216e-03, 6.79725129e-03], dtype=float32) >>> StemmingHelper.original_form('learn') u'learning' >>> StemmingHelper.original_form('hierarch') u'hierarchical'

As you might have guessed, the vectors are NumPy arrays, and support all their functionality. Now, to compute the cosine similarity between two terms, use the similarity method. Cosine similarity is generally bounded by [-1, 1]. The corresponding ‘distance’ can be measured as 1-similarity. To figure out the terms most similar to a particular one, you can use the most_similar method.

>>> model.most_similar(StemmingHelper.stem('classification')) [(u'spam', 0.25190210342407227), (u'metric', 0.22569453716278076), (u'supervis', 0.19861873984336853), (u'decis', 0.18607790768146515), (u'inform', 0.17607420682907104), (u'artifici', 0.16593246161937714), (u'previous', 0.16366994380950928), (u'train', 0.15940310060977936), (u'network', 0.14765430986881256), (u'term', 0.14321796596050262)] >>> model.similarity(StemmingHelper.stem('classification'), 'supervis') 0.19861870268896875 >>> model.similarity('unsupervis', 'supervis') -0.11546791800661522

There’s a ton of other functionality that’s supported by the class, so you should have a look at the API I gave a link to. Happy topic modelling 🙂