Modifying Baayen: Can a Corpus Count Provide a
Reasonable Measure of Morphological Productivity?
Rebecca Brown [Scarborough]
UCLA
Spring 2001
The notion of morphological productivity is widely invoked and even somewhat intuitive.
But the formalization of this notion of the readiness with which various affixes combine with
roots is not at all straightforward. There are a number of definitions and measures of
productivity in the literature, but studies investigating productivity have generally involved some
method of counting affixed forms. These counting methods, however, leave questions about
which things should be counted among which countable things. Earlier studies, such as
Aronoff’s (1976), measured the productivity of affixes by counting the number of times a
particular affix appeared as part of any word in a dictionary, making the claim that productivity
was correctly conceived of as a ratio of ‘actual words’ to ‘possible words’. Baayen (with
Lieber1, 1991) expresses Aronoff’s approach in more formal terms as a ratio of the number of
actual forms with an affix x to the number of bases of a kind eligible for x-affixation. But as
Baayen also points out, such an approach in which the dictionary is used to make affix counts is
not altogether satisfactory. Dictionaries represent the ‘actual’ lexicon of only a particular
lexicographer, and furthermore, represent only standard, well-established elements of the
lexicon. Since productivity critically involves the formation of novel forms, dictionaries are
inappropriate and unreliable for studies of productivity, and other methods of quantifying
productivity must be explored.
1
Though Baayen often collaborates with various other linguists, the productivity index described in various works
bearing his name will, for the sake of simplicity, be referred to as Baayen’s throughout this paper.
1
Baayen’s Productivity Index
Baayen (1989, 1991, 1996) proposes instead a measure of morphological productivity
based on counts made from a corpus. By using a corpus, he alleviates the problem of
representing the lexicon of only a single speaker, as the corpus includes data from a large
number of sources. And because a number of different kinds of sources are incorporated, and all
forms found in those sources are included unedited, productive forms used in actual speech and
writing are accessible. Baayen’s measure is based on the number of hapax legomena, or singly
occurring forms, with a given affix in a corpus. Specifically, Baayen’s index of productivity is
calculated as a ratio of hapax legomena to tokens for a given affix:
P=
n1
N
n1 = number of hapax legomena with the affix
N = number of tokens with the affix
Of course, this sort of measure is still dependent upon the particular corpus used. In Baayen’s
study, CELEX, a corpus of more than 18,000,000 English words taken from a variety of written
(75%) and spoken (25%) sources, is used.
To understand this ratio as a measure of productivity, we must first consider what the two
components of the relation represent. The numerator, which is the number of x-affixed hapaxes,
represents novel forms with the affix x. Because they are created on the fly for use in a particular
situation rather than stored, individual novel forms appear only infrequently in a corpus. The
denominator, which is the total number of x-affixed tokens, represents the overall extent of
occurrence of the affix x, taking into account both the number of different types (or words) and
the frequency of each type. By calculating the ratio of hapaxes to tokens, Baayen gets an
estimate of the probability of coming across new, unobserved types (i.e., novel forms) with a
given affix. Since to Baayen, productivity is a measure of the likelihood of producing novel
forms (Baayen 1991), if it is assumed that the likelihood of encountering forms in the corpus
2
directly represents the likelihood of a speaker producing those forms, this ratio seems to provide
an index of productivity.
But a better indication of the appropriateness of this measure is whether or not it yields
satisfactory results. We consider first Baayen’s own means of evaluating the success of a
measure of productivity. He proposes that a good measure of productivity should meet the
following requirements (Baayen, et al. 1991):
1. it should reflect the linguist’s intuitions concerning productivity
2. it should express the ‘statistically determinable readiness with which an element
enters into new combinations’ (Bolinger)
The index is designed specifically to meet the second criterion, given the above-mentioned
assumption on Baayen’s part that the occurrence of forms in the corpus directly reflects the
likelihood of a speaker producing those forms. As for the first criterion, Baayen and Lieber
(1991, p. 801) claim explicitly that the results yielded by P “accord nicely with [their] intuitive
estimates of productivity.” The results of Baayen’s index for selected affixes are shown in
Table 1 below:
Table 1 Baayen's Results
affix
N
hapaxes dislegomena
P1
P2
-ous (n)
21861
13
10
0.0006
0.0011
-able (v)
15004
10
8
0.0007
0.0012
-er (v)
57683
40
40
0.0007
0.0014
-ness (adj)
17481
77
54
0.0044
0.0075
-ish (n)
1602
8
4
0.0050
0.0075
-ish (adj)
290
1
2
0.0034
0.0103
N = tokens, P1 = productivity index with hapaxes, P2 = productivity index with hapaxes +
dislegomena
These results do not, however, accord with the intuitions of all linguists, Van Marle
(1991) and this author among them. The following comparisons of Baayen’s indices, for
instance, do not satisfy my intuitions about the relative productivity of certain affixes:
3
1.
P(-ous) = 0.0006 ≈
P(-able) = 0.0007
P(-er) = 0.0007
2. P(-able) = 0.0007
P(-ness) = 0.0044
≠
P(-er) = 0.0007
P(-ish) = 0.0050
As indicated in the first comparison, Baayen’s measure assigns the suffix –ous a productivity
index of 0.0006, only marginally smaller than the index of 0.0007 assigned to both –able and
agentive deverbal –er. However, it is my impression that –ous is effectively unproductive,
appearing primarily in learned, listed words (e.g., ferrous, but not foamous), while both –able
and –er are essentially fully productive, attaching to virtually any semantically appropriate verb
(e.g., teachable, teacher, slurpable, slurper).
If we assume that the premise of Baayen’s index is sound, we must consider what could
be causing the index to yield unintuitive results in these cases. Given the nature of the index,
there are two obvious places to look for the source of error: in the counting of tokens and in the
counting of hapaxes.
The first potential error is that the number of relevant tokens is being over-counted.
Certain types (words) are very frequent, and as such, are stored non-compositionally in the
lexicon. Baayen himself (with Lieber, 1991) acknowledges that derived forms are more likely to
be stored as token frequency increases, regardless of productivity. It does not make sense to
count tokens of these words as instances of a particular affix since they are not stored with any
reference to the affix. By counting all tokens containing the phonological string of a given affix,
Baayen counts the non-compositional forms as well, and thus the number of relevant tokens is
over-counted. Because the denominator of the index is artificially increased, some words are
made to seem less productive than they ought to be.
The second potential error is that the number of relevant hapax legomena is being over-
4
counted. There are two, actually three, reasons that a word might occur only once in a corpus:
1.
The word is a novel formation, created productively by adding an affix to a stem to
which it has not been added before. This is what Baayen intends.
2.
The word is simply very infrequent. It is stored in its entirety, but it is only stored in
the lexicons of a few people or only used in very limited contexts.
3.
The infrequent occurrence of the word is a pure statistical accident of the particular
corpus (e.g., picky). This sort of gap, statistically, should not happen often.
By counting all tokens with a given affix appearing exactly once in the corpus, Baayen counts
hapaxes of type 2 and 3 as well those of type 1, and thus the number of relevant hapaxes is overcounted. Because the index numerator is artificially increased, some words are made to seem
more productive than they ought to.
A Revised Index of Productivity
In an effort to address these problems so that the basic core of Baayen’s index of
productivity can be better evaluated, the methods used for counting forms were modified and
Baayen’s calculation of the index of productivity was reapplied. Three basic modifications were
made independently and in combination.
First, high frequency tokens were eliminated. In an attempt to get rid of the bulk of high
frequency, listed types, all words with a frequency of 100 or more were excluded from the token
count.
Secondly, low frequency hapax roots were eliminated. In an attempt to get rid of noncompositional hapaxes that are simply obscure words, words with low frequency roots were
excluded from the hapax count. If a root (the part of the word excluding the relevant affix, and
sometimes excluding another productive affix attached to the same stem) occurred fewer than 20
5
times in the corpus alone or with any affix, the root was considered to be too infrequent to be
separately accessible to a speaker who might be searching for roots and affixes to productively
combine. It was assumed that these forms must be listed in their entirety, with their affix strings,
and are therefore not instances of morphological productivity.
Finally, dislegomena were included. A number of types occurring twice in the corpus
looked just as novel as the singly-occurring forms. It is possible that in some cases, speakers
repeat a newly coined word again in the same conversation, or, given the size of the corpus, that
two speakers might have individually produced the same novel form. In such cases, dislegomena
are just as indicative of morphological productivity as hapaxes are. Thus, productivity indices
were calculated on the basis of the number of hapaxes plus dislegomena as well as on the basis
of the number of hapaxes alone.
Incorporating these modifications, productivity indices for selected affixes were recalculated from the CELEX corpus. Results are shown in the following tables. Table 2 shows
the modified P values based on hapax counts, and Table 3 shows the modified indices based on
counts of hapaxes plus dislegomena.
Table 2
Modified P values based on hapaxes
Suffix
N
Ned
n1
n1-ed
P
Unedited
Without
High Freq.
Tokens
-ous
-able
-ness
-ish (n)
-ish (adj)
-er
36068
18401
14915
1592
452
32651
8921
4366
8582
780
452
12406
34
22
206
9
2
142
13
21
177
6
2
126
0.00094
0.00120
0.01381
0.00566
0.00442
0.00435
0.00381
0.00504
0.02400
0.01154
0.00442
0.01145
tokens
hapaxes
Without
Low Freq.
Hapaxes
Without
Both
0.00036
0.00114
0.01187
0.00377
0.00442
0.00386
0.00146
0.00481
0.02062
0.00769
0.00442
0.01016
N = tokens, Ned = edited number of tokens, n1 = hapaxes, n1-ed = edited number of hapaxes, P
Unedited = n1/ N, Without High Freq. Tokens = n1/ Ned, Without Low Freq. Hapaxes = n1-ed/ N,
Without Both = n1-ed/ Ned
6
Table 3 Modified P values based on hapaxes + dislegomena
tokens
Suffix
N
Ned
hap + dislegomena
n2
n2-ed
P
Unedited
Without
High Freq.
Tokens
Without
Low Freq. Without
Hap & Dis Both
-ous
-able
-ness
-ish (n)
-ish (adj)
-er
36068
8921
55
28 0.00152
0.00616
0.00078
0.00314
18401
4366
41
38 0.00223
0.00939
0.00207
0.00870
14915
8582 307 270 0.02058
0.03577
0.01810
0.03146
1592
780
13
8 0.00817
0.01667
0.00503
0.01026
452
452
4
4 0.00885
0.00885
0.00885
0.00885
32651 12406 242 221 0.00741
0.01951
0.00677
0.01781
N = tokens, Ned = edited number of tokens, n2 = hapaxes + dislegomena, n2-ed = edited number
of hapaxes + dislegomena, P Unedited = n2/ N, Without High Freq. Tokens = n2/ Ned, Without
Low Freq. Hap & Dis = n2-ed/ N, Without Both = n2-ed/ Ned
Recall that we were dissatisfied with the results from Baayen’s productivity calculations
whereby the productivity of –ous is assessed to be approximately the same as the productivity of
–able and –er, and –able and –er appear to be far less productive than the intuitively similarly
productive affixes –ness and denominal –ish. The specific goals of the modified index of
productivity, then, were to bring out the difference in productivity between unproductive –ous
and productive –able and –er and to neutralize the difference in productivity between –able and
–er and –ness and –ish.
With regard to the first goal, the current modifications prove to be generally helpful. The
degree to which –able and –er are more productive than –ous is illustrated in the following
tables, where the number of times that P(-able) and P(-er) are greater than P(-ous) are shown for
both hapax and hapax plus dislegomena counts:
Table 4 Relative Degrees of Productivity: –ous vs. –able
P(–ous)
unedited
.00094
w/o hi freq token .00381
w/o lo freq hapax .00036
w/o both
.00146
vs.
P(–able)
.00120
.00504
.00114
.00481
7
hapax
1.28 x
1.32 x
3.17 x
3.29 x
hap+disleg
1.47 x
1.52 x
2.65 x
2.77 x
Table 5
Relative Degrees of Productivity: –ous vs. –er
unedited
w/o hi freq token
w/o lo freq hapax
w/o both
P(–ous)
.00094
.00381
.00036
.00146
vs.
P(–er)
.00435
.01145
.00386
.01016
hapax
4.63 x
3.01 x
10.72 x
6.96 x
hap+disleg
4.88 x
3.17 x
8.68 x
5.67 x
As can be seen in Table 5, the unedited productivity index of –er is already greater than that of –
ous in the counts made for this study (P(–ous)=0.00094 vs. P(–er)=0.00435).2 Even so, for both
–able and –er, the present modifications increase the degree to which these affixes are
demonstrably more productive than –ous. This change in the measured productivity of the suffix
–ous and the suffixes –able and –er can be attributed to the large number of –ous hapaxes and
dislegomena in the unedited list having low-frequency roots, combined with the high frequencies
of many –able and –er forms. By eliminating low-frequency roots from the –ous hapax counts,
the numerator of the hapax to token ratio is reduced, and thus P(–ous) is reduced. At the same
time, eliminating high-frequency tokens from the counts for –able and –er reduces the
denominator of the hapax to token ratio, thus increasing the productivity index for these affixes.
The combined result is the desired expansion of the difference between the measured
productivity of –ous an that of –able and –er.
With regard to the second goal of neutralizing the difference in productivity between –
able and –er and –ness and denominal –ish, the results are slightly less straightforward than in
the previous case. The various P values for these affixes can be compared in Table 6 below3:
2
It is not clear why the current measures differ from those obtained in the Baayen studies. Baayen does not specify
which version of CELEX he has used in his calculations. As the corpus is continually updated, some of the
differences between his counts and the current counts may arise from consulting different versions of the corpus.
Additional disparities may arise from differences in the determination of which forms have a “noun root” in the case
of –ous or a “verb root” in the case of –able and –er, etc.
3
The data in Table 6 are taken from Tables 2 and 3 above.
8
Table 6 Comparison of P values for –able, –er, –ness, and –ish
P
Unedited
Without High
Freq. Tokens
Without Low
Freq. Hap & Dis
Without Both
Suffix
hapax
hap+dis
hapax
hap+dis
hapax
hap+dis
hapax
hap+dis
-able
-er
-ish (n)
-ness
0.00120
0.00435
0.00566
0.01381
0.00223
0.00741
0.00817
0.02058
0.00504
0.01145
0.01154
0.02400
0.00939
0.01951
0.01667
0.03577
0.00114
0.00386
0.00377
0.01187
0.00207
0.00677
0.00503
0.01810
0.00481
0.01016
0.00769
0.02062
0.00870
0.01781
0.01026
0.03146
In the current counts, –able has a lower P value lower than that of –er, as well that of as –ness or
–ish, in all conditions, and –ness has a much higher P value than the other affixes regardless of
the condition. The most obvious effect of the modifications of P is that for all modified indices
(except for the hapax-only count ‘without high frequency tokens’), –er and –ish trade positions,
so that –er is characterized as being at least slightly more productive than –ish. The degree of
measured difference between the productivity of the two affixes, however, is not much affected;
and where it is, the difference is not lessened. As for the productivity of –er relative to –ness,
both the ‘without high frequency tokens’ and the ‘without both’ conditions lessen the measured
productivity difference somewhat, though P(–ness) is still approximately two times greater than
P(–er) in both cases (compared with a difference of approximately three times in the unedited
counts). Likewise, the productivity difference between –able and the affixes –ness and –ish is
reduced somewhat in the ‘without high frequency tokens’ and the ‘without both’ conditions: P(–
ness) goes from 11.5 times P(–able) to 4.8 and 4.3 times the affix, and P(–ish) goes from 4.7
times to 2.3 and 1.6 times P(–able), counting hapaxes. Thus, we see that in the conditions in
which high frequency tokens are not included in the count, the differences between the measured
productivity of –able and –er and –ness and denominal –ish are generally reduced relative to the
differences in the unedited counts; however, the differences are certainly not neutralized as we
had predicted.
To better evaluate the effect of the modifications to Baayen’s productivity index
9
considered here, it is useful to summarize the effect of each with respect to individual affixes, as
in Table 7:
Table 7 The effect of individual modifications on P of individual affixes
High Frequency Tokens
Removed
-able, -er, -ness, -ish(n)
are all approx. doubled
or more in this condition
-able is most effected
Low Frequency Hapaxes
Removed
-ous is most effected
little or no effect on
other affixes in any
condition
Both
-able, -er and -ous are
most effected (-ous in
the opposite direction
from the other two)
As mentioned above, the effect of the removal of high frequency tokens on –able and the effect
of the removal of low frequency hapaxes on –ous combine to bring about the widening of the
gap in productivity between the two affixes. But because all of the intuitively productive affixes,
–able, –er, –ness, and denominal –ish, are affected by the removal of high frequency tokens, but
the removal of low frequency hapaxes has little effect on any of these affixes, the current
modifications are not very effective in neutralizing differences among these affixes.
Given the imperfect results of the current modifications, we must consider carefully
whether these adjustments, though motivated, really capture the psychological dynamic that
determines productivity, or whether they merely seize upon some emergent property of this
dynamic. We have noted that the productive affixes, –ish, –er, –able, and –ness, show increased
measured productivity when the affix counting methods are modified. It is not the case,
however, that –er and –able show a greater increase in productivity than –ish and –ness, thus
effecting the neutralization of the productivity of these affixes. To satisfy our intuitions about
relative productivity, then, we must assume that there are other factors that systematically
differentiate the modification of different productive affixes. Insofar as it is these other factors
(which may well be dependent on or at least highly correlated with the factors manipulated in the
10
current study) that would bring about the desired result, we might infer that it is these factors
which more closely represent some psychologically real phenomenon of productivity.
Further, despite the fact that removing hapaxes with low root frequencies seems to have
worked well in lowering the measured productivity of –ous, when the individual forms actually
eliminated from the count are considered, it is not clear that this is the most reasonable sort of
means of effecting this result. The roots of –ous hapaxes are not simply less frequent than the
roots of hapaxes with other affixes; the roots of –ous words (hapaxes as well as more frequent
forms) are of an intrinsically different kind. While the roots of other forms tend to be words
(e.g., bashfulness, dwarfish, weaver), the roots of –ous words are fundamentally less word-like
(e.g., opprobrious, contumacious). Thus, although these non-word-like roots tend to have
lower frequencies, not all low frequency roots are of the –ous type. Some, particularly among
the forms suffixed with –able, –er, –ness, and –ish, are simply obscure (or even just accidentally
infrequent) words that a speaker may add productive morphology to. Surely such forms (e.g.,
heinousness, kissable) should not, by their removal from the calculation, count against
productivity.
Corpus-Based Measures of Productivity in General
As we have established, modifications to the method of counting in the calculation of the
index of productivity seem to bring relative P values at least somewhat closer to our intuitions
about the relative productivity of certain affixes, but their success is far from complete. Though
we may consider slight adjustments to the modifications to the productivity index, as suggested
above, the results of the current study lead us to consider as well whether even a perfectly
counted corpus-based measure could ever be sufficient to capture the nature of morphological
productivity. It is clear that certain characterizations can be made of productivity that are not
11
accessible to a model that can only count forms. There are a number of specific constraints on
productivity that require more complicated, less quantitative sorts of knowledge. For example,
some affixation is parasitic; in other words, some affixes attach primarily to particular other
affixes (e.g., –ity attaches to –ic and –able forms). Some affixes are more likely in the semantic
context of another particular affix (e.g., –ee forms appear often in the context of –er forms)
(Barker 1999). Other affixes attach only to roots of a certain etymological type (e.g., –ous
attaches only to Latinate roots). Still other affixes are restricted to particular registers of usage
(e.g., –ish is found primarily in spoken language, and not in written texts) (Plag, et al. 1999). An
intuitive notion of productivity clearly includes such factors, but though their effect may in some
sense be counted in the current index, their substance is in no way represented by the index.
The argument could be made that it is not a flaw of the current index that it does not
account for such conditions on productivity. One could imagine, for example, that factors like
these do not directly affect productivity; rather, they act as a kind of post-application constraint
on the possible outputs of Word Formation Rules, which in turn have access only to the P values
of affixes to determine whether or not to apply to a particular root and affix. In other words, the
constraints have a sort of veto power over the less discriminating Word Formation Rules, which
produce far more forms than are actually attested. Under this system, roots like gold and ferr
and the affix –ous are combined by a general Word Formation Rule, but the morphologically
complex output form goldous is rejected by a constraint on –ous words with non-latinate roots,
while ferrous, with its latinate root, is accepted. Insofar as productivity is understood to be the
likelihood of a particular affix entering into a morphological process, these constraints do not, in
fact, affect productivity.
But under such an analysis, “productivity”, as measured by the productivity index, seems
12
almost meaningless. Such a notion of productivity is not at all accessible to intuition, and given
Baayen’s own requirement that a measure of productivity should “reflect…intuitions concerning
productivity,” it can be inferred that an analysis with such a deliberate exclusion of factors which
influence intuitions on productivity is not Baayen’s intention. We might assume instead that
these factors were simply not considered to be a necessary part of this model, which is based on
the theory that the critical properties of productivity are statistical in nature. However, it might
be precisely factors like these which contribute in a non-quantitative way to the real productivity
of an affix, influencing the likelihood that a Word Formation Rule will even apply to a particular
root and affix to yield an output, and rendering the current model, which cannot consider such
conditions, empirically inadequate.
There are means other than an appeal to intuition by which the empirical adequacy of a
corpus-based, statistical view of productivity might be evaluated as well. A statistical view of
productivity carries certain consequences which must be considered and which provide the basis
for potential experimental evaluation of such models. Perhaps the most obvious consequence is
that a statistical model of productivity suggests that productivity is inherently gradient. To
illustrate, if we choose the maximally edited version of P (hapaxes + dislegomena with revised
numerator and denominator), –ness is shown to be more productive than –er, which is more
productive than –ish (n), and so on, as illustrated in Table 8 below:
Table 8 The relative modified P values of various affixes in decreasing order of
productivity
-ness
-er
-ish (n)
-ish (adj)
-able
-ous
0.03146
0.01781
0.01026
0.00885
0.00870
0.00314
13
Furthermore, –er, for example, is calculated to be twice as productive as –able, while –ish (n) is
1.2 times as productive. Moreover, the difference in productivity between –able and –ous is less
(P(–able) is 2.77 times P(–ous)) than the difference in productivity between –ness and –able
(P(–ness) is 3.62 times P(–able)).
These consequent facts, however, do not accord with the intuitions of this author. In fact,
the intuitions which serve as the basis for the current evaluation of Baayen’s index represent
productivity as essentially categorical rather than gradient. One of the goals of the modification
of the productivity index was to neutralize the P values of the affixes –ness, –er, –able, and –ish,
effectively making them all equal members of a category of fully productive affixes. The other
goal was to differentiate between the P value for –ous and the values for –able and –er in order
to allow –ous to fall into a separate category of non-productive affixes.
Though there is no explicit discussion of the gradient or categorical nature of productivity
in Baayen’s studies, Baayen’s claim is that the productivity index is not merely a descriptive
statistic, but rather “P expresses…in a very real sense the linguistic notion of productivity”
(Baayen 1991). We assume, then, that he considers the gradient nature of his assessment of
productivity to be psychologically real. The conflict between this consequence of the model and
its alternative lends itself to experimental investigation. Carefully designed lexical decision
experiments involving low frequency (hapax and dislegomena) affixed words and like-affixed
nonwords could reveal differences which might exist among novel or near-novel forms with
various affixes (as manifested by low-level differences in reaction times). The underlying
assumption is that forms with more productive affixes would be more quickly accepted and more
slowly rejected because it would simply be more likely that any given root could occur with a
more productive affix than with a less productive one. Systematic differences between words
14
with different affixes would offer support to the position that productivity is gradient, while a
clustering of groups of affixes around two distinct reaction time points would offer support to the
categorical position. With this more representative sample of more objectively gathered
intuitions, the relative ordering of affixes by their productivity (if systematic differences are
found) could also be compared with Baayen’s results.
Of course, a gradient model could be interpreted as somewhat categorical. One could
imagine, for example, that there is a threshold of productivity, above which an affix is marked as
productive, and below which it is not. A learner could keep track of statistical information about
an affix until it reached the P threshold and was labeled as ‘productive’. After that, such detailed
knowledge would be unnecessary, as the affix would already be marked as able to participate in
word formation processes. Statistical differences among productive affixes would then be
meaningless, although varying degrees of productivity should still exist among less productive
affixes.
The linguistic and psychological phenomenon of morphological productivity might well,
finally, be comprised of such knowledge of the lexicon and the frequency of occurrence of its
members as are represented in Baayen’s index of productivity. But even when it is most
successfully modified--given its best chance, Baayen’s index is inadequate to meet the standard
of satisfying linguist intuitions about the relative productivity of certain affixes. Thus, we are
left with two possible conclusions. First, it could be that important, perhaps non-quantitative
factors are missing from the corpus-based statistical model, and incorporating more factors
would improve its success. But because we know that, after a point, the more complications that
are introduced, the less likely the model is to be psychologically plausible, due to limitations on
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human computational abilities, we must consider a second possible conclusion as well. If,
ultimately, the only knowledge necessary for the proper application of Word Formation Rules is
whether or not an affix is productive and any specific conditions on a particular affix’s
occurrence, why would the effort for such detailed calculation be wasted? Perhaps then the
nature of morphological productivity is actually very different from the way that Baayen’s index
represents it, and the critical factors, whatever they are, cannot be counted, however carefully,
from any corpus. We must await further experimental studies to make a determination.
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References
Aronoff, M. (1976) Word formation in Generative Grammar. Cambridge, MA: MIT Press.
Baayen, H. (1991) Quantitative aspects of morphological productivity. Yearbook of
Morphology, 1991, 109-149.
Baayen, H. & Lieber, R. (1991) Productivity and English derivation: a corpus-based study.
Linguistics, 29, 801-843.
Baayen, R.H. & Renouf, A. (1996) Chronicling the Times: productive lexical innovations in an
English newspaper. Language, 72, 69-97.
Barker, C. (1998) Episodic –ee in English: a thematic role constraint on new word formation.
Language, 74, 695-727.
Plag, I., Dalton-Puffer, C., Baayen, H. (1999) Morphological productivity across speech and
writing. English Language and Linguistics, 3, 209-228.
Van Marle, J. (1991) The relationship between morphological productivity and frenquency: a
comment on Baayen’s performance-oriented conception of morphological productivity.
Yearbook of Morphology, 1991, 151-163.
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