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Chapter 7
We can also compute the distance between two words in the embedding space using
the distance() function. This is really just 1 - similarity():
print("distance(singapore, malaysia) = {:.3f}".format(
word_vectors.distance("singapore", "malaysia")
))
We can also look up vectors for a vocabulary word either directly from the word_
vectors object, or by using the word_vec() wrapper, shown as follows:
vec_song = word_vectors["song"]
vec_song_2 = word_vectors.word_vec("song", use_norm=True)
There are a few other functions that you may find useful depending on your use
case. The documentation page for KeyedVectors contains a list of all the available
functions [10].
The code shown here can be found in the explore_text8_embedding.py file in the
code accompanying this book.
Using word embeddings for spam
detection
Because of the widespread availability of various robust embeddings generated
from large corpora, it has become quite common to use one of these embeddings
to convert text input for use with machine learning models. Text is treated as a
sequence of tokens. The embedding provides a dense fixed dimension vector for
each token. Each token is replaced with its vector, and this converts the sequence
of text into a matrix of examples, each of which has a fixed number of features
corresponding to the dimensionality of the embedding.
This matrix of examples can be used directly as input to standard (non-neural
network based) machine learning programs, but since this book is about deep
learning and TensorFlow, we will demonstrate its use with a one-dimensional
version of the Convolutional Neural Network (CNN) that you learned about
in Chapter 4, Convolutional Neural Networks. Our example is a spam detector that
will classify Short Message Service (SMS) or text messages as either "ham" or
"spam." The example is very similar to the sentiment analysis example in Chapter 5,
Advanced Convolutional Neural Networks that used a one-dimensional CNN, but our
focus here will be on the embedding layer.
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