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0 1 there is so much good food in vegas that i feel cheated for
wasting an eating opportunity by going to rice and company
Chapter 8
We also report the results across all sentences in the test dataset. As you can see,
the test accuracy is the same as that reported by the evaluate call. We have also
generated the confusion matrix, which shows that out of 1000 test examples, our
sentiment analysis network predicted correctly 782 times and incorrectly 218 times:
accuracy score: 0.782
confusion matrix
[[391 97]
[121 391]]
The full code for this example is available in lstm_sentiment_analysis.py in the
source code folder for this chapter. It can be run from the command line using the
following command:
$ python lstm_sentiment_analysis.py
Our next example will describe a many-to-many network trained for POS tagging
English text.
Example ‒ Many-to-Many – POS tagging
In this example, we will use a GRU layer to build a network that does POS tagging.
A POS is a grammatical category of words that are used in the same way across
multiple sentences. Examples of POS are nouns, verbs, adjectives, and so on. For
example, nouns are typically used to identify things, verbs are typically used
to identify what they do, and adjectives are used to describe attributes of these
things. POS tagging used to be done manually in the past, but this is now mostly
a solved problem, initially through statistical models, and more recently by using
deep learning models in an end-to-end manner, as described in Collobert, et al. [21].
For our training data, we will need sentences tagged with part of speech tags. The
Penn Treebank [22] is one such dataset; it is a human-annotated corpus of about
4.5 million words of American English. However, it is a non-free resource. A 10%
sample of the Penn Treebank is freely available as part of NLTK [23], which we will
use to train our network.
Our model will take a sequence of words in a sentence as input, then will output the
corresponding POS tag for each word. Thus, for an input sequence consisting of the
words [The, cat, sat. on, the, mat, .], the output sequence should be the POS symbols
[DT, NN, VB, IN, DT, NN, .].
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