The morphological productivity of selected ... - Helda - Helsinki.fi

Not only vocabulary growth curves, but also frequency spectra
provide a useful tool to assess the productivity of different word-formation
processes. In particular, a frequency spectrum provides information on the
distribution of the type frequencies of a given word-formation process, because
productive ones tend to behave in a certain way in this respect, producing a
skewed, rapidly decreasing plot. Even looking at the frequency spectra per se can
reveal interesting facts, but it is even more useful to compare the observed values
to the expected, average values. Again, this can be done with the ZipfR package
of the R statistics software. The actual formula to calculate the expected values for
a frequency spectrum is explained by Baayen (2001: 92).
Figure 5 shows the expected (black) versus observed (red) frequency
spectra for the combining form data. The figure only displays the first fifteen
frequency classes, but it can nevertheless give a useful visualization of how the
different types are distributed with each combining form. Even a brief glance at
the figure shows that all the combining forms display similar, rapidly decreasing
plots with a long tail of high frequency classes that are realized by only few types.
In addition, as the figure shows, almost all the observed frequency spectra line up
relatively beautifully with the expected, average values.
The only exception is -nomy, which displays quite drastic
differences between the expected and the observed values at times. For example,
frequency class 2 (i.e., the number of types that occur twice in the corpus) has
many more members than expected. The same holds true for frequency class 5.
Scarcity of data cannot be the reason for this inconsistency, since the token
frequency of -nomy is the second largest of all the selected combining forms. On
the other hand, despite its high token frequency, -nomy has a particularly low type
frequency, only 46 types (the most frequent combining forms having over 300
types). In a situation like this, even a small deviation from the expected values
might have an impact. Another striking feature with -nomy, which might have an
impact on the results is that the most frequent type, economy, accounts for over 70
per cent of all the words with -nomy. With -graphy, the corresponding percentage
is roughly 20% (the most frequent type being geography), and with hyper, no
more than 13% (hypertension).
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