List of Tables
15.3
15.4
15.5
15.6
15.7
15.8
15.9
16.1
16.2
16.3
16.4
16.5
16.6
16.7
16.8
16.9
Three quantities that are commonly used in term weighting
in information retrieval.
Term and document frequencies of two words in an
example corpus.
Components of tf.idf weighting schemes.
Document frequency
(df)
and collection frequency (cf) for
6 words in the New York Times corpus.
Actual and estimated number of documents with
k
occurrences for six terms.
Example for exploiting co-occurrence in computing content
similarity.
The matrix of document correlations
BTB.
Some examples of classification tasks in NLP.
Contingency table for evaluating a binary classifier.
The representation of document 11, shown in figure 16.3.
An example of information gain as a splitting criterion.
Contingency table for a decision tree for the Reuters
category “earnings.”
An example of a maximum entropy distribution in the form
of equation (16.4).
An empirical distribution whose corresponding maximum
entropy distribution is the one in table 16.6.
Feature weights in maximum entropy modeling for the
category “earnings” in Reuters.
Classification results for the distribution corresponding to
table 16.8 on the test set.
16.10 Perceptron for the “earnings” category.
16.11 Classification results for the perceptron in table 16.10 on
the test set.
16.12 Classification results for an
1NN
categorizer
for the
“earnings” category.
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