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Word Embeddings
gensim also provides a doesnt_match() function, which can be used to detect the
odd one out of a list of words:
print(word_vectors.doesnt_match(["hindus", "parsis", "singapore",
"christians"]))
This gives us singapore as expected, since it is the only country among a set of
words identifying religions.
We can also calculate the similarity between two words. Here we demonstrate
that the distance between related words is less than that of unrelated words:
for word in ["woman", "dog", "whale", "tree"]:
print("similarity({:s}, {:s}) = {:.3f}".format(
"man", word,
word_vectors.similarity("man", word)
))
Gives the following interesting result:
similarity(man, woman) = 0.759
similarity(man, dog) = 0.474
similarity(man, whale) = 0.290
similarity(man, tree) = 0.260
The similar_by_word() function is functionally equivalent to similar() except
that the latter normalizes the vector before comparing by default. There is also a
related similar_by_vector() function which allows you to find similar words
by specifying a vector as input. Here we try to find words that are similar to
"singapore":
print(print_most_similar(
word_vectors.similar_by_word("singapore"), 5)
)
And we get the following output, which seems to be mostly correct, at least from
a geographical point of view:
0.882 malaysia
0.837 indonesia
0.826 philippines
0.825 uganda
0.822 thailand
...
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