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Chapter 7
if i >= k-1:
break
if k < len(word_conf_pairs):
print("...")
print_most_similar(word_vectors.most_similar("king"), 5)
The most_similar() method with a single parameter produces the following
output. Here the floating point score is a measure of the similarity, higher values
being better than lower values. As you can see, the similar words seem to be
mostly accurate:
0.760 prince
0.701 queen
0.700 kings
0.698 emperor
0.688 throne
...
You can also do vector arithmetic similar to the country-capital example we
described earlier. Our objective is to see if the relation Paris : France :: Berlin :
Germany holds true. This is equivalent to saying that the distance in embedding
space between Paris and France should be the same as that between Berlin and
Germany. In other words, France - Paris + Berlin should give us Germany. In
code, then, this would translate to:
print_most_similar(word_vectors.most_similar(
positive=["france", "berlin"], negative=["paris"]), 1
)
This returns the following result, as expected:
0.803 germany
The preceding similarity value reported is Cosine similarity, but a better measure
of similarity was proposed by Levy and Goldberg [9] which is also implemented
in the gensim API:
print_most_similar(word_vectors.most_similar_cosmul(
positive=["france", "berlin"], negative=["paris"]), 1
)
And this also yields the expected result, but with higher similarity:
0.984 germany
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