Kanako Komiya, Chikara Igarashi, Kazutomo Shibahara,
Koji Fujimoto, Yasuhiro Tajima, Yoshiyuki Kotani

WSEAS TRANSACTIONS on COMMUNICATIONSGenerating a Set of Rules to Determine
The Gender of a Speaker of a Japanese Sentence
KANAKO KOMIYA, CHIKARA IGARASHI, KAZUTOMO SHIBAHARA, KOJI FUJIMOTO*,YASUHIRO TAJIMA and YOSHIYUKI KOTANI
Department of Computer, Information and Communication Sciences
Tokyo University of Agriculture and Technology*Tensor Consulting Co.Ltd.
2-24-16, Nakacho, Koganei, Tokyo, 184-8588*Ichigaya KT bldg. 4-7-16 Kudanminami, ChiyodaKu, Tokyo, 102-0074
JAPAN{komiya, k-shiba, igaras-c}@fairy.ei.tuat.ac.jp, {ytajima, kotani}@cc.tuat.ac.jp*koji.fujimoto@tensor.co.jp
http://shouchan.ei.tuat.ac.jp/wiki/index.php?english%2Ffrontpage#d4c92ea8
Abstract: - Some work has been reported on the problem of automatically determining the gender of a document’s
author as a part of researches to extract features of a document’s author. Japanese language has expressions calledmasculine/feminine expression, and they can often indicate the gender of a speaker of a conversational sentence.
The computer system needs this mechanism in order to make or understand natural Japanese conversational
sentences.
The authors made a system that determines the suitable gender of a speaker of a single conversational sentence
and named it gender-determining system (GDS). It generates a set of rules to determine the more suitable genderof a speaker of a sentence automatically, by decision tree learning. The authors employed six linguistic features
for each of two morphemes at the end of a sentence and presence or absence of morphemes whose part of speech isa miscellaneous pronoun or a particle for ending as features of decision tree learning. The authors calculated theaccuracy of GDS using the cross validation method and it was approximately 69.3% when human could answer thesame problem with approximately 71.7%. The authors showed decision tree learning is more suitable than multiple
regression analysis or Bayesian estimation in order to classify the gender of the speaker of Japanese sentences andgenerate a set of rules to determine them, and selected the suitable features as the inputs of GDS. The set of rules
GDS generated indicates, for example, women speak more politely than men in Japan.
Key-Words: - Natural language processing, rule generation, decision tree learning, gender-determining,knowledge acquisition, data mining.
In this study, we focus on the genders of speakers of
single Japanese conversational sentences anddetermine the suitable gender of them. We generated a
set of rules to determine the suitable gender of a
speaker using decision tree learning depending on the
linguistic features. The system simulates the people’s
recognizing suitable gender of a speaker of a Japanese
sentence and the generated rules can be helpful toknow the mechanism of it.

1 Introduction
1.1 Related Work
Some work has been reported on not only categorizing
based on speaker's voice [1] but also categorizing
written texts by author gender [2~5]. More work has
been reported on authorship identification [6, 7] andsome of them are about Japanese texts [2, 6, 7]. The
methods are extensive from heuristic analysis [7] toSVM [6]. The target texts are also rich in variety forexample blogs [2], E-mail texts [3], books [4] and soon.

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about presence or absence of a morpheme because thedata we gathered included 45 pronouns and 19
particles for ending (cf. Table 1).
These features are used for the inputs of GDS. For
example in the case the sentence is “dat tara kekkou dayo”, we will use 12 features: six features for da and yo
and 64 more features. (cf. Fig. 1 in appendix).

1.2 The Masculine/Feminine Expression in
JapaneseJapanese language has expressions called masculine /feminine expression, and they can often indicate thegender of a speaker of a conversational sentence. They
are not grammatical rules in the languages such as
French, German and so on, but conventional usage
trends. For example “ore, I” is a pronoun only for men
and a particle for ending “wa” is mostly used by
women.
The computer system should imitate this mechanism
in order to make or understand natural Japanese
sentences.
Hence we developed a system that determines thesuitable gender and named it gender-determining
system (GDS). It generates a set of rules to determine
the suitable gender of a speaker of a single sentence
automatically, by decision tree learning from example
sentences, and gives us the suitable result for a set of
inputs, based on the rules that the system generated. It
is an artificial intelligence system that can simulate thepeople’s recognizing suitable gender of the speaker ofJapanese sentence. We can examine the rules that are
GDS generated explicitly and it can help us examine
those of humans.
We describe the set of selecting rules and the GDS inthis paper.

Table 1The Features
The linguistic featuresof the second last morphemeof the sentence
The linguistic featuresof the last morphemeof the sentence
Presence or absence of morphemes
whose POS is a miscellaneous
pronounPresence or absence of morphemes
whose POS is a particle for ending
Total

Number
6

6

45
19
76

2.2 Results for GDS
We prepared the same number of example sentences
for each gender. Table 2 shows the frequencies ofappearance of the genders of speakers of Japanese
sentences.
Table 2 The Frequencies ofAppearance of the Genders

2 Gender Determining System (GDS)
2.1 The Features and Their Values for GDS
For preparation to use GDS, 1230 sentences weregathered from 11 novels and morphological analysis
for them was conducted using ChaSen [8] (Matsumoto
et al (2000)). Then the linguistic features acquiring
system (LFAS) that we developed made linguisticfeatures.
We input the result file of morphological analysis into
the LFAS. The LFAS outputs two kinds of linguisticfeatures automatically for each sentence. They are 1)
Six features for each of two morphemes at the end of asentence (:a morpheme itself, a pronunciation, aprototype of a morpheme, a part of speech (POS), a
conjugation of a morpheme, a form of a morpheme)
and 2) Presence or absence of morphemes whose POS
is a miscellaneous pronoun or a particle for ending.
We employed these features because the first personalpronouns and the ending of sentences including theparticles for ending well indicate the gender of a
speaker. In this experiment, GDS used 64 features

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The Number of Features

The Gender

The Frequencies of Appearance

Male

615

Female

615

Total

1230

2.3 The System GDS
There are two stages to use GDS: generation ofdecision tree and performance. In the first stage: thegeneration of the decision tree, the user inputs thecontexts: the linguistic features to determine thegender of a speaker of a sentence and the gender itself
into GDS.
In the first stage, GDS performs decision tree learningand outputs a set of rules to determine the suitablegender of a speaker of a sentence.
In the second stage: the performance, the user inputs
a set of linguistic features to determine the gender of a
speaker of a sentence and GDS determines a suitablegender according to the rules which are obtained in thefirst stage. (cf. Fig. 2).

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tree automatically from leaning data (many examples),and branches lead to leaves that have the suitable
result from the root node.
The linguistic features were used in order to get themost type of gender as the output. Yes/noclassification (: whether the value of the datum is thesame as a certain value or not) was tried. C4.5
(Quinlan (1993) [9]) was used and binary decision tree
was generated, which gives us a result for an input thathas features, which are not appeared in learning data.
Many papers about classification in WSEAS [10-13]
and many are about decision tree [10, 11 and 13].

Finally, Fig. 3 (cf. appendix) shows the outline of all
the processes.
GDS generates a set of rules to determine the suitablegender of a speaker of a sentence automatically, bydecision tree learning from many example sentences,and gives us the suitable result for a set of features fora Japanese sentence, based on the rules the systemgenerated. Therefore GDS can simulate determining
the suitable gender of a speaker of a sentence, and theset of rules, which is generated by GDS, can help us
examine those of humans.
In addition, GDS can teach us the usages that Japanesepeople can hardly decide in detail, based on the rulesthat we generated.

4 Experiment1230 sentences were gathered from 11 novels, the setsof features were selected in order to determine suitablegender of a speaker of a sentence using LFAS and thecorrect meanings were determined manually.
Following termination conditions were used: 1)
Whether the information gain is zero or not and 2)
threshold values. Two kinds of threshold value weretried: 1) A value of entropy of a node and 2) A valuethat multiplies a value of entropy of a node andnumbers of data in the node together.

The Generation of the Decision Tree
Training Data (The gender of a speaker and Features)

GDS

A set of rules to determine
the gender of a speaker
Conduct decision tree learning

5 Evaluation
The accuracies of GDS were calculated many times
according to the threshold values, using the five-foldcross validation method, and their highest value was
69.3%. Fig. 4 and fig. 5 show the accuracies of GDS
according to the threshold values. The value reachedrecord high when threshold value: a value thatmultiplies a value of entropy of a node and numbers ofdata in the node together was 500.

Performance
A set of rules to determine
the gender of a speaker
Input：
Contexts

GDS

Output：
The gender ofa speaker

The accuracy

80

Fig.2 The System GDS (Gender determining
system)3 Decision Tree Learning Based on the
Gender of a Speaker

40
20
0
0

0.3

0.6

0.9

1.2

The entoropy

The decision tree is a way to describe someclassifications of data, and consists of query nodes. (cf.
Fig. 6 in appendix). Each node in the tree classifies
the inputs into a few classes according to the feature
values of the datasets. The decision tree learning is amachine learning using this property. It generates a

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60

Fig. 4 The accuracies of GDS according to thethreshold values; the entropy of a node

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The accuracy

WSEAS TRANSACTIONS on COMMUNICATIONSIn addition, both men and women answered correctly
only 710 problems (57.7 %) and GDS mistook 154
questions in them. It indicates that GDS can still be
improved though it is a difficult problem even forhumans to determine the gender of a speaker of asingle Japanese sentence. For example not only
morphemes at the end of the sentence but also
morphemes at the end of the clause can indicate thegender.
Moreover, men, women and GDS gave the sameanswer, which is not correct to 81 questions. These
cases are the cases that GDS mistook like humans. For
instance, little children’s utterance or dialects are
difficult to decide the gender of a speaker.
Finally, another experiment was run in order tocompare GDS to the other systems. GDS is targeted ata single sentence, and it makes difficult to compare theother systems that are targeted at a document.
Therefore we combined sentences that are spoken by
the same speaker together into a group, and calculatedtheaccuracy
for
each
group
using
maximum-likelihood method. The average of thesentences in a group is 7.3 sentences and it was 80.4%.
(Minimum is one sentence and maximum is 68.)

80
60
40
20
0
0

300

600

900

1200

The entoropy of a node * the number of datain the nodeFig. 5 The accuracies of GDS according to thethreshold values; the value the entropy of a node
multiplied by the numbers of data in the nodeAn evaluation test was run in order to evaluate GDS.
20 men and 20 women classified the gender of the
speaker of all the questions that GDS answered. (Eachman and each woman answered 61 or 62 questions.)
The accuracy of men was 71.1% and that of woman
was 72.4%. This is because it is difficult to decide thegender of a speaker when they talk formal. Table 3 andtable 4 show the breakdown lists of men’s answer andwomen’s answers. These tables show that GDS
provides less performance than humans, but thedifference between GDS and the average of human(71.7%) is only 2.4 points. These tables also show
both men and women tend to give their own gender as
the answers. Women answered more correctly because
they have a less tendency to do so than men.
Table 3

6 Comparison with Other Methods
6.1 Multiple Regression Analysis Method
Multiple regression analysis [14] was conducted inorder to compare with decision tree learning, aspreliminary experiments.
The following four kinds of methods were tried. 983
sentences were gathered from 11 novels andmorphological analysis for them was conducted using
ChaSen. Table 5 shows the frequencies of appearanceof the genders of preliminary experiments.

The breakdown list ofmen’s answers

The men’s answers
Correct answer
Answered “male” whencorrect answer is “female”
Answered “female” whencorrect answer is “male”
Total

Number874

Rate [%]
71.06

202

16.42

154

12.52

1230

100

Table 5 The Frequencies of Appearance of the
Genders of Preliminary Experiments
The Gender

Table 4 The breakdown list of
women’s answers
The women’s answers
Correct answer
Answered “male” whencorrect answer is “female”
Answered “female” whencorrect answer is “male”
Total

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Number890

Rate [%]
72.36

154

12.52

186

15.12

1230

100

Male
FemaleTotal

The Frequencies ofAppearance
607
376
983

Then two types of linguistic features were made by
LFAS. Table 6 shows the number of features of thefirst type and table 7 shows that of the second type.

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Table 6

Table 8 The 15 features of Preliminary
Experiments, the First TypeThe Number of Features of Preliminary
Experiments, the First TypeThe Features

The Morpheme

NumberPresence or absence of morphemes
whose POS is miscellaneous pronoun
Presence or absence of morphemes
whose POS is particle for ending
Presence or absence of morphemes
whose POS is miscellaneous noun affix
Presence or absence of morphemes
whose POS is verb affix

36
18
18
100

Total

172

Table 7 The Number of Features of Preliminary
Experiments, the Second Type and The Number of
Features of the Second Preliminary Experiments,
the First TypeThe Features
The linguistic featuresof the second last morphemeof the sentence
The linguistic featuresof the last morphemeof the sentence
Presence or absence of morphemes
whose POS is a miscellaneous
pronounPresence or absence of morphemes
whose POS is a particle for ending
Total

Number6

Kimi

Miscellaneous pronoun

Watashi

Miscellaneous pronoun

Boku

Miscellaneous pronoun

Minna

Miscellaneous pronoun

KochiraMiscellaneous pronoun

AchiraMiscellaneous pronoun

Wai

Miscellaneous pronoun

KanojoMiscellaneous pronoun

No

Particle for ending

Wa

Particle for ending

Koto

Miscellaneous noun affix

Iru

Verb affix

Teru

Verb affix

Shimawa

Verb affix

Kudasai

Verb affix

In other words, the following four preliminary
experiments were conducted.

61) Multiple regression analysis using 15 features ofthe first type of linguistic features as independent
variables.

36182) Decision tree learning using 15 features of the first
type of linguistic features (P < 0.05 by t-test).

66

The first type of features consists of four kinds offeatures: presence or absence of morphemes whose
POS is a miscellaneous pronoun, a particle for ending,a miscellaneous noun affix or a verb affix. Multiple
regression analysis was conducted using these features
as independent variables, and features decreased to 15
features by t test (P <0.05 by t-test). Table 8 showsthese 15 features. Decision tree learning was also
performed using these 15 features and 172 features
before conducted t test.
The second type of features also consists of fourkinds of features: the linguistic features of the second
last morpheme and the last morpheme of the sentence,and presence or absence of morphemes whose POS isa miscellaneous pronoun or a particle for ending. This
type of features is the same type of features as theexperiments in the chapter five but the number of themis different because the number of data was also
different.

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POS3) Decision tree learning using 172 features of thefirst type of linguistic features.
4) Decision tree learning using the second type of
linguistic features
The way to generate decision trees and theexperimental conditions were the same as these ofchapter three and chapter four expect the features andthe number of the sentences. Table 9 shows theaccuracies of these preliminary experiments.
The accuracies of GDS were also calculated manytimes according to the threshold values, using thefive-fold cross validation method.

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In conclusion, table 9 shows decision tree learning
is more suitable than multiple regression analysis inorder to determine the gender of the speaker ofJapanese sentences.

Table 9 The Accuracies of Preliminary
Experiments, Comparison between Multiple
Regression Analysis and Decision Tree Learning
Multiple regression analysis

The Number of
Features15

Accuracy[%]
69.48

Decision tree learning

15

63.88

Decision tree learning

172

62.50

Decision tree learning

66

71.82

Method

6.2 Bayesian Estimation
Bayesian estimation [15] was also conducted in order
to compare with decision tree learning. In this time,
1230 sentences gathered from 11 novels shown intable 2 were used. The accuracies of Bayesianestimation were also calculated using the five-foldcross validation method. The occurrence probabilities
of morphemes are used.
Table 12 shows the accuracies of the experiments. It
shows the accuracy of Bayesian estimation and that ofdecision tree are almost the same; the difference isonly 0.49 points though Bayesian estimation is a little
higher than decision tree learning.
We employed decision tree learning because it canalso generate a set of rules.

Table 9 shows the accuracy of multiple regression
analysis is higher than that of decision tree learning, if
the number of features is the same: 15.
The accuracy of the multiple regression analysis
using 15 features of first type of linguistic features as
independent variables was about 69.5%, that ofdecision tree leaning using the same features was
about 63.9%, (The value reached record high whenthreshold value: a value that multiplies a value ofentropy of a node and numbers of data in the node
together was 700.) and the difference is about 5.6
points. The accuracy of multiple regression analysis
was higher because the t test was conducted for
multiple regression analysis. The accuracies suggest t
test (P <0.05) was too strict to make features ofdecision tree learning. I think the difference is because
decision tree generates a set of rules using
combinations of features but multiple regression
analysis uses independent variables.

Table 12 The Accuracies of Experiments,
Comparison between Bayesian Estimation andDecision Tree Learning
MethodDecision tree learning
Bayesian estimation

6.3 Feature Selection
Additional experiments have done in order to selectsuitable features to generate a decision tree. Onceagain, 983 sentences were gathered from 11 novels
and morphological analysis for them was conductedusing ChaSen. Table 5 shows the frequencies ofappearance of the genders of preliminary experiments.
The following three kinds of features were tried. All
these linguistic features were made by LFAS.

However table 9 also shows the accuracy of decision
tree learning can be higher than that of multiple
regression analysis. The accuracy of decision tree
learning using 15 features of the first type of linguisticfeatures was about 71.8% and the difference between
this case and the last case; the number of features is 15
was about 7.9 points. Also, the difference between thiscase and the multiple regression analysis is still 2.3
points.
Moreover the accuracy of decision tree learning
using 172 linguistic features of the first type of
linguistic features is 62.5% and it is the worst. (Thevalue reached record high when threshold value: avalue that multiplies a value of entropy of a node andnumbers of data in the node together was 600.)
The accuracies among them suggest the 66
linguistic features were more suitable than the 15
linguistic features or the 172 features in order to
conduct decision tree learning.

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Accuracy [%]
69.27
69.76

1) The linguistic features of the second lastmorpheme and the last morpheme of the sentence.
2) The linguistic features in the first trial andpresence or absence of morphemes whose POS isa miscellaneous pronoun or a particle for ending.
3) The linguistic features in the second trial and the
linguistic features of the second last morphemeand the last morpheme of the first clause of thesentence.

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WSEAS TRANSACTIONS on COMMUNICATIONSThe second type of features is the same as theexperiments in the chapter 7.1 and it is shown in table
7. Also table 11 shows the number of features of thefirst type and table 12 shows that of the third type.
In the third type, all linguistic information of the last
and the second last morpheme of the first clause would
be “same as the last and the second last morpheme ofthe sentence” if the sentence consists of only one
clause.
The way to generate decision trees and theexperimental conditions were the same as these ofchapter three and chapter four expect the features andthe number of the sentences. The accuracies of GDS
were also calculated many times according to thethreshold values, using the five-fold cross validation
method.
Table 13 shows the accuracies of these preliminary
experiments. It shows these three accuracies arealmost the same; though the first one is a little lower
than the other two. (In the first trial the value reachedrecord high when threshold value: entropy of a node
was 0.74, and in the second and the third, the thresholdvalue: a value that multiplies a value of entropy of anode and numbers of data in the node together was400.) In addition, the new added features in the thirdfeatures: the linguistic features of the second lastmorpheme and the last morpheme of the first clause of
the sentence have not appeared in the upper part of thedecision tree. Therefore it seems these features are notnecessary. However, the new added features in thesecond trial: presence or absence of morphemes whose
POS is a miscellaneous pronoun or a particle forending have appeared in the upper part, for instance,
the root node of the decision tree. Therefore we
decided to adopt the second type linguistic features togenerate a decision tree in GDS.

Table 12 The Number of Features of the Second
Preliminary Experiments, the Third TypeThe Features
The linguistic featuresof the second last morphemeof the sentence
The linguistic featuresof the last morphemeof the sentence
Presence or absence of morphemes
whose POS is a miscellaneous
pronounPresence or absence of morphemes
whose POS is a particle for ending
The linguistic featuresof the second last morphemeof the first clause
The linguistic featuresof the last morphemeof the first clause
Total

Number

Total

12

6

6

36
18

6

6
78

Table 13 The Accuracies of the Second
Preliminary Experiments, Feature Selection
The Number of Features
12
66
72

Accuracy [%]
71.62
71.82
71.82

7 A Set of Rules to Determine theGender
The following questions were selected in the upper
part of the decision tree.
(Q1) Whether or not there is the particle for ending“wa” in the sentence.
(Q2) Whether or not “wa” is the last morpheme of thesentence.
(Q3) Whether or not there is the first person pronoun“wai , I” in the sentence.
(Q4) Whether or not “de, please don’t” is the lastmorpheme prototype of the sentence.
(Q5) Whether or not “gozai, be” is the second lastmorpheme of the sentence.
(Q6) Whether or not “no” is the last morpheme of thesentence.
(Q7) Whether or not there is the particle for ending“cho-dai, please” in the sentence.

Table 11 The Number of Features of the Second
Preliminary Experiments, the First TypeThe Features
The linguistic featuresof the second last morphemeof the sentence
The linguistic featuresof the last morphemeof the sentence

Number6

6

Fig. 6 shows the upper part of the best decision tree
derived from experiments. (cf. appendix) The questionin the root node is whether or not there is the particle
for ending “wa” in the sentence. Fig 6 shows, if it is

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rules to determine them, and selected the suitablefeatures as the inputs of GDS. The rules GDS
generated indicate, for example, women talk morepolitely then men do and men talk with less hesitation
than women do.

true, the sentence is an utterance of a woman. This isbecause the particle for ending “wa” is mostly used by
women. The second question: whether or not “wa” isthe last morpheme of the sentence is selected because
morphological analysis sometimes fails. (Q6) is
Whether or not “no” is the last morpheme of thesentence. This morpheme is also a particle for endingthat mostly used by women. The morphemes “de,please don’t” and “cho-dai, please” are particles forending used for asking politely and “gozai, be” is thatfor describing politely. These rules shows women talkmore politely than men do in Japan.
Meanwhile, “wai, I” in (Q3) is a first person pronoun
in dialect. It is not a common feature to determine thegender of a speaker, but GDS selected the feature
because women mostly use the morpheme in the
training data. GDS generates a set of rules dependingon the characteristics of training data because GDS
generates them from training data. In one hand thetraining data should be selected carefully. On the other
hand GDS is useful to know the features for texts ofspecialized area because of this property.
Finally, the features are not appeared in Fig.6 whichindicate that the gender of a speaker is a man are, forinstance, first person pronouns such as “ore, I” and“boku, I” and morphemes “da, is” and imperative
form. These pronouns are always used by men and “da,is” is used for answering without hesitation. It shows
men talk with less hesitation than women do.

References:[1] Dat Tran and Dharmendra Sharma, Automatic
Gender Recognition, WSEAS Transactions on
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8 Conclusion
We developed a system that determines the suitablegender of a speaker of a single Japanese sentence inorder to examine why Japanese people know thegender of a speaker from written sentences and named
it gender-determining system (GDS). GDS generates
a set of rules to determine the suitable meanings
automatically, by decision tree learning. The inputs of
GDS were selected automatically using linguisticfeature acquiring system (LFAS). We determined thecorrect gender manually. The accuracy of GDS was
69.3% when human could answer the same problem
with approximately 71.7%. The accuracy is 80.4%
when we combined sentences that are spoken by thesame speaker together into a group, and calculated theaccuracy for each groups using maximum-likelihood
method [16]. We showed decision tree learning ismore suitable than multiple regression analysis orBayesian estimation in order to classify the gender ofthe speaker of Japanese sentences and generate a set of

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Koji Fujimoto, Yasuhiro Tajima, Yoshiyuki Kotani

WSEAS TRANSACTIONS on COMMUNICATIONS

Proceedings of the 9th WSEAS International
Conference on Computers, 2005, pp. 1-6.
[11]Cheng-Jung Tsai, Chien-I Lee, Chiu-Ting Chen,
and Wei-Pang Yang, A Mmultivariate Decision
Tree Algorithm to Mine Imbalanced, Data.
WSEAS Transactions on Information Science andapplications. v4 i1., 2007, pp. 50-58.
[12]P. Povalej and P. Kokol. End User Friendly Data
Mining with Decision Trees: a Reality or a Wish?,
Proceedings of the 2007 annual Conference on
International
ConferenceonComputerEngineering and Applications, 2007, pp. 35-40.
[13] Tien-Chin Wang and Hsien-Da Lee,
Constructing a Fuzzy Decision Tree by Integrating
Fuzzy Sets and Entropy, WSEAS Transactions on
Information Science and Applications , vol.3, no.8,
2006, pp.1547-1552.
[14]Richard A. Berk, Regression Analysis: A
Constructive Critique, Sage Publications, 2004.
[15] Berger, J.O, Statistical Decision Theory andBayesian Analysis, Springer Verlag, New York,
Second Edition, 1985.
[16] A.W. van der Vaart, Asymptotic Statistics ,Cambridge Series in Statistical and Probabilistic
Mathematics, 1998.

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Issue 1, Volume 8, January 2009

Kanako Komiya, Chikara Igarashi, Kazutomo Shibahara,
Koji Fujimoto, Yasuhiro Tajima, Yoshiyuki Kotani

WSEAS TRANSACTIONS on COMMUNICATIONSAPPENDIX
Morphemes, Pronunciation, Prototypes, Parts of Speech, Conjugations, Forms
The second last morpheme of the sentence: da, da, da, auxiliary verb, special/da, end-form or adnominal form
The last morpheme of the sentence: yo, yo, yo, postposition for ending, no information, no information

Fig. 1 The 24 features of «dat tara kekkou da yo»

(Q1)

≠

=

female

(Q2)

≠

=

(Q3)

female≠=(Q4)

female=≠female

(Q5)

≠

=

(Q6)

female
=

≠
(Q7)

≠

female=female・・・

Fig. 3 The Outline of All theProcesses
Fig. 6 The Upper Part of the Best
Decision Tree Derived from
Experiments
Squares mean nodes and questions
are in them. These questions consistof a feature and a value, for example“Whether or not the feature is thevalue”. Circles mean the leaves andselected genders are in them.

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Issue 1, Volume 8, January 2009