Glottometrics 10, 2005, 16-29

Mathematical Modelling for Japanese Kanji Strokes in
Relation to Frequency, Asymmetry and Readings1
Katsuo Tamaoka, Hiroshima, Japan2
Gabriel Altmann, Lüdenscheid, Germany
Abstract: The present study investigates the relationship between of Japanese kanji strokes and theirprinted-frequencies of occurrence, compositional asymmetry and kanji multiple readings. First, distributions of kanji strokes in both samples of the 1,945 basic kanji and of 6,355 kanji appearing in the
Asashi Newspaper published between 1985 and 1998 followed a negative hypergeometric distribution
as demonstrated by Figure 1. The distribution of strokes of the 1,945 kanji with their printed-frequencies is rather rhapsodic, as shown in Figure 2, but a rough-fitting model is drawn in Figure 3. Mathematical modelling for kanji strokes with lexical compositional asymmetry reveals the interesting tendency of regressive compounding; that is, that the greater the number of strokes in a kanji, the more it
tends to produce two-kanji compound words by adding a kanji on the right side of the target kanji, as
shown in Figure 4. A kanji may often have multiple readings; this study also examines the number ofreadings in relation to the number of kanji strokes. As shown in Figure 6, the greater the number ofkanji strokes, the fewer the number of readings. In other words, the more visually complex the kanji is,
the more specialised its reading becomes. As such, kanji strokes, as one of the central characteristics
of kanji, are closely related to other properties such as frequency, asymmetry and readings. The present study uses mathematical modelling to indicate these relations.

Key words: mathematical modelling, kanji strokes, kanji frequency, lexical compositional
asymmetry, multiple readings, regressive compounding, self-regulation cycle
1. A self-regulation cycle of linguistic properties
A single property of a lingustic entity can hardly be isolated from its other properties. The associated properties make up a self-regulating cycle in which one change creates a chain of alterations. The change comes gradually, and its reactions follow with some delay. These seriesof changes, as a whole, maintain a balanced ‘equilibrium’ state. Although this conjecture isconsidered to be applicable to linguistic properties, it is still a preliminary hypothesis whichmust be validated empirically. The present study on Japanese kanji strokes attempts to explore
this conjecture.
A kanji represents the smallest unit of meaning, the ‘morpheme’. Unlike alphabetic languages, Japanese kanji are drawn in horizontal and vertical dimensions (for details on kanji ingeneral, see Kaiho & Nomura, 1983; Morton & Sasanuma, 1984; Tamaoka, 1991, 2003,
2005a, 2005b; Tamaoka, Kirsner, Yanase, Miyaoka & Kawakami, 2002; Tamaoka & Makioka, 2004; Tamaoka & Yamada, 2000; Wydell, Butterworth & Patterson, 1995; Wydell,Patterson & Humphreys, 1993). Among the 1,945 basic Japanese kanji, the simplest kanji is
1This study is supported by the Research Institute of Science and Technology for Society (RISTEX) at the Japan
Science and Technology Agency (JST), Kawaguchi, Japan.
2
Address correspondence to: Katsuo Tamaoka, International Student Center, Hiroshima University 1-1, 1-chome,
Higashihiroshima, Japan 739-8524. E-mail: ktamaoka@hiroshima-u.ac.jp

Kanji strokes in relation to other properties

17

drawn as a single horizontal line: − meaning ‘one’; whereas the most complex kanji requires
23 strokes to write: 鑑 meaning ‘appreciate’. As such, the number of strokes roughly indicatesthe visual complexity of a kanji. The question then is whether or not the number of kanji
strokes (or kanji visual complexity) has some relation to other properties of a kanji. In thepresent study, three kanji properties are examined as candidates for possibly being related to
the number of kanji strokes; namely, the printed-frequency of a kanji (hereafter, refer to ‘kanji
frequency’), asymmetry for two-kanji compound word production, and multiple readings.
2. Mathematical modeling for kanji strokes
The lexical database sampled from the Asahi Newspaper published between 1985 and 1998
(Amano & Kondo, 2000) contains a total of 6,355 different kanji. Kobayashi (1981) reportsthat the 1,000 most frequently used kanji represent 93.9 percent of all kanji printed in newspapers, and 90.0 percent of all kanji appearing in magazines. Knowledge of 2,000 kanji covers 99.6 percent of those found in newspapers and 98.6 percent in magazines. Knowledge of
3,000 kanji covers about 99.9 percent of kanji used in newspapers and magazines. Considering these figures, 6,355 kanji must encompass almost all the kanji seen in Japanese written
texts. Then, the question is whether the number of kanji strokes has some influence on the
kanji frequency.
As can be seen in Table 1, the distribution of the number of strokes is very regular. Using
the possibilities furnished by the Wimmer-Altmann theory (for details, see Wimmer, Altmann,2005) the 1-displaced negative hypergeometric distribution was chosen:(1)⎛ M + x − 2 ⎞⎛ K − M + n − x ⎞⎜ x − 1 ⎟⎜ n − x + 1 ⎟⎠⎝⎠,Px = ⎝⎛ K + n − 1⎞
⎜
n ⎟⎠
⎝

x = 1,2,3,..., n + 1 .

This formula describes the distribution of stroke numbers. Using the kanji database of 1,945
basic kanji (Tamaoka et al., 2002; Tamaoka & Makioka, 2004), the distribution of stroke
numbers is obtained as seen with the above 1-displaced negative hypergeometric distribution
with parameters K = 11.6517, M = 4.9490, n = 22, DF = 18, X2 = 22.53, P = 0.21, C = 0.01. If
the distribution is stable, it is automatically assumed that a larger sample must display thesame behaviour. As can be seen in Table 1, iterative fitting to the inventory of 6,355 kanjishows that the distribution of strokes is, in fact, relatively stable and the fit is satisfactory. As
the chi-square grows with increasing sample size, the values obtained for the larger sample
were X2 = 45.63 with 26 DF, corresponding to P = 0.01 and C = 0.0072. The result is displayed graphically in Figure 1. The increasing values of the parameters indicate that perhaps alimiting distribution would yield better results (e.g., Poisson or negative binomial) but an
investigation of this issue will be postponed until data from other writing systems such as
Chinese, Assyrian, Sumerian, and so on has also been examined. However, the chi-squarevalue decreases for n between 29 and 40, and it increases again above 40. These figuresindicate that the negative hypergeometric model is a very stable estimator for this purpose.
There exist different kinds of frequency counts: (a) using individual texts, (b) a list which
represents a part of a dictionary, (c) the complete dictionary, (d) a frequency dictionary and a
corpus representing a mixture of texts and being identical with a frequency dictionary. Here,
we analyze case (b).

18

K. Tamaoka, G. Altmann
Table 1
The negative hypergeometric model for the distribution of strokes in 6,355 kanji
X (Strokes)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

fx (Number of kanji)

NPx

3.61
5
17.46
29
48.11
57
98.91
109
168.45146
250.76202
336.85312416.84436
481.90469
525.74539
545.25579
540.53577
514.34537
471.26432
416.76427
356.37377
295.05300
236.71
205
184.12168
138.88136
101.57
110
72.00
67
49.43
47
32.84
38
21.08
20
13.06
12
7.79
8
4.46
6
2.44
3
2.43
2
K = 24.6877, M = 6.8335, n = 41, DF = 26
X2 = 45.63 , P = 0.0100, C = 0.0072

Figure 1. The negative hypergeometric model for the distribution of strokes of 6,355 kanjiKanji strokes in relation to other properties

19

3. Modelling the relationship between kanji strokes and kanji frequency
A further question is whether the above model continues to be suitable if the frequencies of
the given kanji in a corpus are taken into consideration. This question is of a rather methodological nature. The first problem is the sample size. In our case, 6,335 kanji were takenfrom a very large corpus of 86,542,349 word occurrences where no classical statistical test
can rescue the model from rejection. In other words, even if the model could perhaps be
accepted, cum grano salis, the characteristics of the Chi-square test, destroy our hope. Its
linear increase with increasing sample size is fatal for this investigation. The second problem
is that language laws hold for homogeneous data. However, a corpus is as heterogeneous as it
can be. It contains a mixture of frequencies which, in spite of statistical practice, do not levelout with the increasing sample size. On the contrary, the irregularities can escalate. We mighterroneously assume that a corpus is a sample from a population, but it has frequently been
claimed that there are no populations in language (cf. Orlov, Boroda, Nadarejšvili, 1982).
There is no population that can be called “Shakespeare” or “Akutagawa”, nor any such population as “the language of the Asahi Newspaper” or “the language of the Times”. Likewise,
there is no population that can be called “English texts”, “Japanese texts”, “the word stock ofGerman” or even “the word stock of Ainu”. Great dictionaries contain about one to two
hundred thousand lexical items, but the German word stock with all of its special dictionaries(terminology) is estimated to be approximately twenty million. A student of German found in
one volume of a German magazine “Der Spiegel” more than 8,000 compounds that were notfound in any German dictionary. Do they belong to the word stock if they are constructed ad
hoc and not in regular use? Is the size of the word stock a real or a potential number? Even
though some models yield good results if the lexicon size is considered infinite (cf. Kornai,2002), the usual models of vocabulary growth are of an exponential type, having finite limits(e.g., Piotrowski, Bektaev, Piotrowskaja, 1985).
As can be seen in Figure 2, the frequency of kanji with a given number of strokes is very
rhapsodic. It is multimodal. The main irregularities are in the middle of the distribution.
Deviations from any “honest” distribution are so enormous that in this case, no usual model isadequate.

Figure 2. Frequency of kanji with X strokes

20

K. Tamaoka, G. Altmann

Of course, there are different possibilities to capture this irregularity. One of them is the
mixing of distributions in different proportions. This technique, however, multiplies the number of parameters and it is not known how many components must be used. The second
possibility is smoothing by pooling two or more frequency classes. In this case, the characterof a discrete distribution is lost. This is, of course, merely a question of approximation, and inprinciple it is not relevant what kind of model – discrete or continuous – is used, as the data
are distorted. Even if the law behind their generation was known, any model of this law would
be forced to take account of many subsidiary conditions, which consequently yield a very special case. Therefore, it is best to consider modelling as a conceptual activity, taking into account simplifications, and even conscious distortions, at the beginning (Bunge, 1967, p. 388).
Table 2 presents the original data corresponding to Figure 2 and the data pooled by 2 using the mean X of the pertinent frequency classes (column 3). It can be seen that in the lattercase the “curve” is smooth and can be well approximated by either a continuous distribution
or a simple continuous curve.
The second case is preferable on several grounds. First, there is no need to derive a model
for false, distorted data – since mixed data are false data. It would be more correct to takeeach article of the Asahi Newspaper separately. Second, the first approximation should be as
concise as possible. Third, there is no problem in switching from discrete models to continuous ones or vice versa (cf. Mačutek, Altmann, in press). Since there is no software for abattery of continuous distributions, non-linear regression is simply used to find a preliminarymodel. Fitting is simpler if the absolute frequencies are transformed into relative ones, as
shown in the fifth column of Table 2. Starting from the above mentioned Wimmer-Altmanntheory (2001) the simplest case of its continuous version was chosen, namely(2)dy ⎛ b⎞= ⎜ − c ⎟ dx
y ⎝x⎠

which says that the relative rate of change of frequency (dy/y) is proportional to the relative
rate of change of stroke number (bdx/x) to which a disturbing constant c is added, originatingfrom distortions caused by mixing of texts and pooling of frequency classes. The solution of
the differential equation (2) yields(3)

y = axbe-cx

and the results given in the last column of Table 2. As can be seen, the high value of R = 0.97
corroborates this kind of approach. Of course, this is merely the first, not the last word.
Based on values in Table 2, formula (3) is calculated as:
y = 0.0040x3.8342e-0.5058x
This formula is depicted graphically in Figure 3. It shows that after smoothing very heterogeneous data, the relation between stroke number and frequency becomes increasingly rigorous, and almost bell-shaped (with a slight asymmetry).

Kanji strokes in relation to other properties

21

Table 2Frequencies of kanji with x strokes
X (Stroke)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

fx
(Kanji frequency)
530246┐1623017┘2172880┐
5667307┘5983444┐8385289┘
6722157┐
10304831┘
8835897┐6630512┘7687320┐7078129┘4663567┐3054986┘
3010830┐1332170┘
520867┐1126232┘
321022┐806007┘65617┐
11454┘
8568
86542349

x*

fx*

p*x

ptheor

1.52153263.0248810.008890

3.57840187.090594

0.083269

5.5

14368733.1660310.171322

7.5

17026988.1967470.204631

9.5

15466409.1787150.184202

11.5

14765449.1706150.139357

13.5

7718553.089188

0.093718

15.54343000.050183

0.057885

17.5

1647099.019032

0.033523

19.5

1127029.0130230.018460

21.5

77071

.0008900.009761

23.0

8568
86542349.000099

0.005920
a = 0.0040
b = 3.8342
c = 0.5058
R = 0.9712

Figure 3. Fitting (3) to the relationship between number of strokes and kanji frequency

22

K. Tamaoka, G. Altmann

5. Modelling the relationship between kanji strokes and lexical compositional symmetry
In a previous study by Tamaoka and Altmann (2004), the symmetry between left-hand andright-hand side compounding has been examined both for individual kanji and for the whole
field of 1,945 kanji. For instance, the kanji 学 /gaku/ meaning ‘to learn’ or ‘learning’ is combined with another kanji on the right-hand side position such as in 学校 (/gaQ koR/, ‘school’),
学生 (/gaku sei/, ‘student’) and 学者 (/gaku sja/, ‘scholar’). Combinations with other kanji on
the left-hand side position are also possible such as in 入学 (/njuR gaku/, ‘school admission’),
文学 (/bun gaku/, ‘literature’) and 私学 (/si gaku/, ‘private school’). Tamaoka and Altmann
investigated two questions regarding how two-kanji compound words were produced from asingle kanji by adding another kanji on either the left or the right side, and how symmetric
they are on both sides. Furthermore, the present study examines whether the number ofstrokes has some influence on the symmetry of compound building. Tamaoka and Altmann(2004) tested asymptotically the symmetry using the chi-square criterion(4)( nL − nR ) 2
X =nL + nR2

which is distributed as a chi-square with 1 degree of freedom. Formula (4) does not show
directly whether there is left or right symmetry. Therefore for use as a coefficient of symmetry we express (4) in the form
(5)S=(nL − nR )nL + nR

indicating left asymmetry if S > 0, and right asymmetry if S < 0, without evaluating its significance. Of course, any other coefficient that shows the direction of asymmetry and does notallow 0 in the denominator would also be appropriate. Eliminating all cases of kanji having nocompounding, we obtained 1,934 cases for which we computed the mean asymmetry at each x= 1,…,23. As shown in Figure 4, the results indicated that the greater the number of strokes,
the greater the left asymmetry of the kanji.
Table 3
Number of strokes of a kanji and its asymmetry
Numberof strokes
1
2
3
4
5
6
7
8
9
10

Formula (3)Lin.reg.

Lin.reg.

-9.52823359-0.93903791-1.65643326-0.81334384-0.59039237-0.37989045
0.02421970
0.20483855-0.21886048
0.22139170-2.431076-2.240516-2.049956-1.859395-1.668835-1.478275-1.287714-1.097154-0.906594-0.716034--------0.949802-0.851436-0.753069-0.654703-0.556337-0.457970-0.359604-0.261237-0.162871

Kanji strokes in relation to other properties
11
12
13
14
15
16
17
18
19
20
21
22
23

-0.18819254
0.05195306-0.00252829
0.28074040
0.28992592
0.02195363-0.00939287
0.50832419
0.85177389
1.13805872
0.59888953
0.65786894
1.78854380-0.525473-0.334913-0.144353
0.046208
0.236768
0.427328
0.617888
0.808449
0.999009
1.189569
1.380129
1.570690
1.761250
R = 0.3688
a = -2.6216
b = 0.1906

23-0.064504
0.033862
0.132229
0.230595
0.328962
0.427328
0.525695
0.624061
0.722427
0.820794
0.919160
1.017527
1.115893
R = 0.7611
a = -1.1465
b = 0.0984

The values (number of strokes vs. formula (3)) are given in Table 3 (first three columns).
Again, x = 1 displays a fully anomal value and can be left out from further computation.
There are only two kanji with one stroke, 一 and 乙. The kanji 乙 is rather uncommon, occurring only 418 times in the Asahi Newspaper corpus of 287,792,797 words (Amano &Kondo, 2000). This kanji only produced 11 two-kanji compound word-types with a joint
token frequency of 418 (this happened to be the same as the single kanji frequency); by contrast, the kanji 一 produces 381 two-kanji compound word-types with a joint token frequencyof 529,828 (Tamaoka & Makioka, 2004). This discrepancy of the two kanji with one stroke
created a deviated value of -9.52823359 in the formula (3). Excluding the case of these two
one-stroke kanji, we obtain a rather clearer picture in the fourth column of Table 3 and Figure
4. The F-test for linear regression is highly significant in both cases (with or without x = 1),
but we prefer the determination coefficient even if it is not so high (R = 0.76).

Figure 4. Kanji strokes vs. asymmetry (without x = 1)

24

K. Tamaoka, G. Altmann

This result shows that the greater the number of strokes in a given target kanji, the more it
tends to produce two-kanji compound words by adding a kanji on the right side (with thetarget kanji positioned on the left side). This tendency was called regressive compounding in
the kanji symmetry study of Tamaoka and Altmann (2004). In the case of a kanji with two
strokes, the kanji 大 meaning ‘big’ appears 503,023 times in the Asahi Newspaper (Amano &Kondo, 2000). When this kanji is positioned on the right side, 399 two-kanji compound words
are produced by adding other kanji on the left side, e.g. 巨大 meaning ‘huge’. The same kanji
produces 70 different two-kanji compound words by adding other kanji on the right side, e.g.
大学 meaning ‘a university’. Roughly five times more compounds were produced when thetarget kanji is positioned on the right side (adding kanji on the left side) than on the left side
(adding kanji on the right side). The token frequencies of these compounds show a similardifference; left-side compounds occur 374,376 times and right-side compounds 128,647 times.
In the case of a kanji with many strokes, this tendency reverses. For example, the kanji 警meaning ‘warn’ or ‘admonish’ appears 91,377 times in the Asahi Newspaper. When this kanji
is positioned on the right side, 10 different two-kanji compound words are produced by adding other kanji on the left side; for instance 夜警 meaning ‘night watch’. The same kanji produces 27 different two-kanji compound words by adding other kanji on the right side, forexample 襲来 meaning ‘invasion’. The number of compounds for the left side position of thetarget kanji (adding kanji on the right side) was 2.7 times greater than for the right side position of the target kanji (adding kanji on the left side). The difference in token frequencies ofthese compounds is not so great but still maintains a similar trend: left-side compounds occur
66,454 times and right-side compounds occur 91,377 times.
Although this overall tendency of regressive compounding between kanji strokes and kanji
compositional or compounding asymmetry is clearly shown in Figure 4, it is a surprising
discovery which lacks a clear explanation.
5. Mathematical modelling for kanji strokes with multiple readings
Kanji pronunciations can be divided into two types: the On-reading derived from the original
Chinese pronunciation, and the Kun-reading originating from the Japanese way of reading
kanji (for details see Kaiho & Nomura, 1983; Morton & Sasanuma, 1984; Tamaoka, 1991,
2003, 2005a, 2005b; Wydell, Butterworth & Patterson, 1995; Wydell, Patterson & Humphreys, 1993). The mixture of material with two phonological origins created multiple pronunciations of a single Japanese kanji. In dispensing with the Chinese tones, and adapting
three different sound systems from China, the Japanese created a great number of multiple
readings for the kanji. According to the Database for the 1,945 Basic Japanese Kanji (2ndedition) produced by Tamaoka, Kirsner, Yanase, Miyaoka and Kawakami (2002), the total
number of kanji which have only one pronunciation is 699 (667 kanji with a single Onreading and 32 kanji with a single Kun-reading). This is 35.94 percent of the 1,945 basic
Japanese kanji. Kanji which have only On- or only Kun-readings, regardless of the number ofpronunciations, total 779 (739 for On-reading and 40 for Kun-reading), or 40.05 percent of
the 1,945 basic Japanese kanji. In other words, although it is a commonly-held notion that akanji has both an On-reading and a Kun-reading, only 1,166 kanji or about 59.95 percent of
the 1,945 basic Japanese kanji have both types of pronunciations.
Starting from the well-known dependence between word length and word polysemy, we
conclude that the same relation must hold between the number of strokes (length) of a kanji
and the number of readings, which may be an analogue to polysemy. In Table 4 we see theKanji strokes in relation to other properties

25

relevant numbers based on 1,945 kanji (Tamaoka, et al., 2002). The usual relation is here (cf.
Köhler 1986); it can be derived from the differential equation (2) setting c = 0 :(6)

y = ax-b .

In order to obtain a more solid result, we leave out all mean values based on less than 10 kanji
in each case of strokes, i.e. in Table 4, two kanji with 1 stroke, 5 kanji with 21 strokes, 2 kanji
with 22 strokes and 1 kanji with 23 strokes. A total of 10 kanji were excluded from thecalculation of Formula (6). Iterative computing of (6) yields
y = 3.6382x-0.2454
giving a preliminarily satisfactory result. Again, we would like to point out that the choice of
1,945 kanji is not random but is based on specific criteria. The graph of observed and computed values is in Figure 6.
Table 4
Dependence of the number of readings on the number of strokes
Number of
strokes
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

Number of
kanji
2
12
30
66
93
111
141
187
179
199
195
199
147
105
104
68
34
33
21
11
5
2
1

Number of readings(On + Kun)
5
36
95
153
220
258
305
398
379
410
396
431
296
199
180
142
57
64
30
21
9
5
1

Mean number
Computedof readings
means (4)
2.50
3.07
3.00
2.78
3.17
2.59
2.32
2.45
2.37
2.34
2.32
2.26
2.16
2.18
2.13
2.12
2.12
2.07
2.06
2.02
2.03
1.98
2.17
1.94
2.01
1.90
1.90
1.73
1.87
1.84
2.09
1.82
1.68
1.79
1.94
1.77
1.43
1.74
1.91
1.80
2.50
1.00
a = 3.6382, b = 0.2454, R = 0.81

In order to show that this is parallel to Köhler’s approach, we draw the respective parts of
his scheme using our variables L-Strokes meaning the logarithm of stroke number and Lreadings meaning the logarithm of reading numbers. Since the relationship has a decreasing

26

K. Tamaoka, G. Altmann

character, we use a negative proportionality coefficient –b and add a factor A representing thespecification requirement. The scheme is shown in Fig. 5.
L-Strokes-b

L-Readings

A
Fig. 5. Systems theoretical scheme of the relationship between the number of strokes
and the number of readings of a kanji
The above scheme is analogous to that of the relationship between word length and meaning: in order to specify the meaning, one adds to the word an affix or another word (to build acompound) or reduplicates it. In case of kanji, the reading will be more specific the more
complex the kanji is, i.e. the more elementary strokes it contains.
Using the above scheme we haveor

L-Readings = A – b(L-Strokes)
log y = A – b log x.

Taking antilogarithms and denoting eA = a, we obtain formula (6).

Figure 6. Kanji strokes with mean numbers of kanji readings
Thus, taking pairs of kanji properties and examining their links, we can discover a partial
coincidence with Kohler’s cycle; but in general we must expect also other types of relationships which are characteristic of kanji only. A more general view of this issue might beachieved by incorporating analyses of Korean and even Assyrian script, both having analogous properties. In Köhler’s original self-regulating cycle, only four properties are set inrelation: frequency, length, polysemy and polytexty; and all are linked in the way given by

Kanji strokes in relation to other properties

27

formula (6). With the aid of Köhler’s cycle, formulas like (3) and (6) can be set up without theuse of differential equations; at the same time they yield a systems theoretical explanation.
The formulae express all the requirements imposed on language by its users (cf. Köhler 1986,
1987, 1990a,b,c, 2005). Usually they are captured by the parameters, which must be
interpreted; later on their value must be stated a priori.
6. Conclusions
It is known that word length is strongly related to frequency in many languages (e.g., Köhler
1986; Breiter 1994; Hammerl 1991; Leopold 2000; Miyajima 1992; Strauß, Grzybek, Altmann 2005). In the case of Japanese kanji, word length can be described as visual complexity
represented by kanji strokes which require drawing a whole picture of a kanji. The number ofkanji strokes is one of a kanji’s central properties. This study has investigated the relationships of the number of strokes in Japanese kanji to their printed frequencies of occurrence,compositional asymmetry and kanji multiple readings. Distributions of kanji strokes in both
samples (the 1,945 basic kanji, and the 6,355 kanji appearing in the Asashi Newspaper between 1985 and 1998) produced a negative hypergeometric distribution. The distribution of
strokes of the 1,945 kanji with their printed frequencies is rather rhapsodic. Looking at therelationship between kanji strokes and the kanji’s lexical compositional asymmetry uncovers
the interesting tendency of regressive compounding, which fits in with a previous proposal by
Tamaoka and Altmann (2004). It has been ascertained that the greater the number of strokes
in a kanji, the more strongly it tends to produce two-kanji compound words by adding a kanji
on the right side (i.e. with the target kanji positioned on the left side). Furthermore, we havealso examined the number of readings of a kanji in relation to the number of kanji strokes. A
kanji often has multiple On- and Kun-readings; but we found that the larger the number ofkanji strokes, the fewer the number of readings. In other words, the more visually complex thekanji is, the more specialised its reading becomes. As such, the number of strokes, as one of
the central characteristics of the kanji, has close relationships with other properties of the
kanji: frequency, asymmetry and reading. The present study has demonstrated these relationships. Since the number of kanji strokes can be considered an integral feature of the system of morphemes and lexical properties, we suggest that further investigations into this area
will strongly enhance our overall picture of the mutual relationships among different
properties of the kanji.
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