The co-occurrence matrices above represent each cell by frequencies, either of words with documents ( Fig. 6.5 ), or words with other words (Fig. 6.6 ). But raw frequency is not the best measure of association between words. Raw frequency is very skewed and not very discriminative. If we want to know what kinds of contexts are shared by cherry and strawberry but not by digital and information, we're not going to get good discrimination from words like the, it, or they, which occur frequently with all sorts of words and aren't informative about any particular word. We saw this also in Fig. 6 .3 for the Shakespeare corpus; the dimension for the word good is not very discriminative between plays; good is simply a frequent word and has roughly equivalent high frequencies in each of the plays.