In many placement exam rounds, we often encounter a basic question to find word analogies. In the word analogy task, we complete the sentence “a is to b as c is to ___ “, which is often represented as a : b :: c : d and we have to find the word ‘d’. A sample question can be like: ‘man is to woman as king is to ___‘.
The human brain can recognize that the blank must be filled with the word ‘queen‘. But for a machine to understand this pattern and fill the blank with the most appropriate word requires a lot of training to be done. What if we can use a Machine Learning algorithm to automate this task of finding the word analogy. In this tutorial, we will be using Word2Vec model and a pre-trained model named ‘GoogleNews-vectors-negative300.bin‘ which is trained on over 50 Billion words by Google. Each word inside the pre-trained dataset is embedded in a 300-dimensional space and the words which are similar in context/meaning are placed closer to each other in the space.
Methodology to find out the analogous word:
In this problem, our goal is to find a word d, such that the associated word vectors va, vb, vc, vd are related to each other in the following relationship: ‘vb – va = vd – vc‘. We will measure the similarity between vb-va and vd-vc using cosine similarity.
Importing important libraries:
We need to install an additional gensim library, to use word2vec model, to install gensim use the command ‘pip install gensim‘ on your terminal/command prompt.
Python3
import numpy as npimport gensimfrom gensim.models import word2vec,KeyedVectorsfrom sklearn.metrics.pairwise import cosine_similarity |
Loading the word vectors using the pre-trained model:
Python3
vector_word_notations = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin',binary=True) |
Defining a function to predict analogous word:
Python3
def analogous_word(word_1,word_2,word_3,vector_word_notations): ''' The function accepts a triad of words, word_1, word_2, word_3 and returns word_4 such that word_1:word_2::word_3:word_4 ''' # converting each word to its lowercase word_1,word_2,word_3 = word_1.lower(),word_2.lower(),word_3.lower() # Similarity between |word_2-word_1| = |word_4-word_3| should be maximum maximum_similarity = -99999 word_4 = None words = vector_word_notations.vocab.keys() va,vb,vc = vector_word_notations[word_1],\ vector_word_notations[word_2],vector_word_notations[word_3] # to find word_4 such that similarity # (|word_2 - word_1|, |word_4 - word_3|) should be maximum for i in words: if i in [word_1,word_2,word_3]: continue wvec = vector_word_notations[i] similarity = cosine_similarity(,[wvec-vc]) if similarity > maximum_similarity: maximum_similarity = similarity word_4 = i return word_4 |
Testing our model:
Python3
triad_1 = ("Man","Woman","King")# *triad_1 is written to unpack the elements in the tupleoutput = analogous_word(*triad_1,word_vectors) print(output) # The output will be shown as queen |
