EXTRACTION OF LINGUISTIC SPEECHPATTERNS OF JAPANESE FICTIONAL
CHARACTERS USING SUBWORD UNITS
Mika Kishino1 and Kanako Komiya2
1

2

Ibaraki University, Ibaraki, JapanTokyo University of Agriculture and Technology, Tokyo, Japan

ABSTRACTThis study extracted and analyzed the linguistic speech patterns that characterize Japanese
anime or game characters. Conventional morphological analyzers, such as MeCab,segment words with high performance, but they are unable to segment broken expressions
or utterance endings that are not listed in the dictionary, which often appears in lines ofanime or game characters. To overcome this challenge, we propose segmenting lines ofJapanese anime or game characters using subword units that were proposed mainly fordeep learning, and extracting frequently occurring strings to obtain expressions thatcharacterize their utterances. We analyzed the subword units weighted by TF/IDF
according to gender, age, and each anime character and show that they are linguistic
speech patterns that are specific for each feature. Additionally, a classification experimentshows that the model with subword units outperformed that with the conventional method.

KEYWORDS
Pattern extraction, Characterization of fictional characters, Subword units, Linguistic
speech patterns, word segmentation

1. INTRODUCTION
There is research in the field of natural language processing that focuses on linguistic styles andcharacterizes utterances of confined groups categorized by some features like gender or age.
Japanese is a language whose expressions vary depending on gender, age, and relationships with
dialog partners. In particular, Japanese anime and game characters sometimes speak withemphasis on character rather than reality. Furthermore, the way of talking of Japanese fictional
characters is sometimes different from real people. For example, Funassyi, a Japanese mascot
character, usually ends each utterance with “なっしー, nassyi” yet this ending is not found in a
Japanese dictionary. Additionally, a cat character tends to add “にゃん, nyan”, an onomatopoeia
that expresses a cry of a cat at the end of each utterance. Human characters also have characterspecific linguistic speech patterns in novels, anime, and games. They are known as role language[1] and it is related to characterization; the role language shows what role the speaker plays, andsometimes it is different from real conversation. For example, “僕, boku, I” is a first-person
singular usually used for boys in novels, anime, and games, but it is also used for men and boys
in real life. Therefore, in this study, we extracted and analyzed the linguistic speech patterns thatcharacterize these characters using utterances of anime or game characters. In Japanese,morphological analysis is a basic technology for natural language processing because Japanese
does not have word delimiters between words. Word segmentation and morphological analysis
David C. Wyld et al. (Eds): NLP, MLTEC, CLBD, SEAPP, NeTIoT, VLSIE, ITCS, ARIA, DMS - 2021
pp. 85-96, 2021. CS & IT - CSCP 2021
DOI: 10.5121/csit.2021.112307

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Computer Science & Information Technology (CS & IT)

are now widely performed using morphological analyzers like MeCab and Chasen and theirperformances are usually very high level. However, they are unable to segment broken
expressions or the endings of utterances that are not found in the dictionary, which often appears
in lines of anime or game characters (refer to Section 2). To hinder this problem, we propose
using subword units to segment lines of Japanese anime or game characters and extracting strings
that occur frequently (refer to Section 3). The subword units are usually used with deep learningtechnologies and their robustness for out-of-vocabulary words is often noted. However, they areless interpretable than the original words because the segmentation are depending on thefrequencies or occurrence probabilities rather than the meanings. In the current study, however,
we show that the expressions extracted using subword units are more interpretable than thoseusing the original words for the extractions of linguistic speech patterns of fictional characters,
which is the case where many words are not listed in the dictionary using data collected frompublications on the internet (refer to Section 4). We also show that the subword units are effective
even though no deep learning technology is used with them. In the experiment, we weighted thesubword units by TF/IDF according to gender, age, and each anime character (refer to Sections 5)
and show that they are linguistic speech patterns that are specific for each feature (refer to
Sections 7 and 8). Additionally, we performed a classification experiment using a support vector
machine (SVM) based on linguistic speech patterns we extracted to classify the characters into acharacter group (refer to Sections 6) and showed that a subword unit model outperformed aconventional morphological analyzer (refer to Sections 7 and 8). Finally, we conclude our workin Section 9.

2. RELATED WORKJapanese does not have word delimiters between words and word boundaries in Japanese areunspecific. Therefore, there has been much research on Japanese word segmentation ormorphological analysis and there are many morphological analyzers for Japanese texts like
MeCab [2], Chasen, Juman++ [3], and KyTea [4], These morphological analyzers segment words
with high performances but sometimes the performances decrease for the noisy texts. For
Japanese word segmentation of noisy texts, Sasano et al. [5] proposed a simple approach to
unknown word processing, including unknown onomatopoeia in Japanese morphologicalanalysis. Saito et al. [6] also recommend using character-level and word-level normalization toaddress the morphological analysis of noisy Japanese texts. Recently, algorithms for subword
unis such as Byte Pair Encoding (BPE) [7] and unigram language model [8] are proposed. They
are mainly proposed for neural machine translation and usually used with deep learningtechnologies. We used the unigram language model for word segmentation of Japanese lines offictional characters. There are some studies on interpretability and usability of words dependingon the word segmentation for information retrieval (IR). Kwok [9] investigated and compared 1gram, bigram, and short-word indexing for IR. Nie et al. [10] proposed the longest-matchingalgorithm with single characters for Chinese word segmentation for IR. In addition, there has
been much research on characterization. PERSONAGE (personality generator) developed by
Mairesse and Walker [11] as, the first highly parametrizable conversational language generator.
They produced recognizable linguistic variation and personality, and our work also focused oneach character's personality. Walker et al. [12] reported a corpus of film dialog collected andannotated for linguistic structures and character archetypes. Additionally, they conducted
experiments on their character models to classify linguistic styles depending on groups such as
genre, gender, directors, and film period. Miyazaki et al. [13] conducted a fundamental analysis
of Japanese linguistic expressions that characterize speeches for developing a technology tocharacterize conversations by partially paraphrasing them. In their subsequent research, Miyazaki
et al. [14] reported categories of linguistic peculiarities of Japanese fictional characters. Miyazaki
et al. [15] conducted an experiment to see whether the reader can understand the characterizationof a dialog agent by paraphrasing the functional part of each sentence with a probability suitable

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for the target character, as a way to characterize the speech and to enrich the variation of the
speeches. Another study focused on Japanese sound change expressions to characterize speechesof Japanese fictional characters; they collected these expressions and classified them [16].
Additionally, Okui and Nakatsuji [17] used a pointer generating mechanism to generate various
responses for a Japanese dialog system, referring to several different character responses. They
learned the characterization of the responses with a small amount of data.

3. EXTRACTION OF LINGUISTIC SPEECH PATTERNS USING SUBWORD UNITS
Many terms not included in the dictionary such as expressions with characterization at theendings of utterances and broken expressions appear in fictional character dialogs. As a result,using existing morphological analyzers with dictionaries to segment the lines of fictional
characters are challenging. Therefore, we propose using subword units for the segmentation oflines of fictional characters. The concept behind subword units is that the frequency ofoccurrence of a word is studied in advance, and low-frequency words are broken down into
letters and smaller words. In other words, using subword units, we can treat a string with a highfrequency of occurrence as a single unit, not a word in a dictionary. We used software referred toSentencePiece [18] for word segmentation of Japanese lines of fictional characters.
SentencePiece learns the segment method directly from the text and segments the text into
subword units. It supports BPE and unigram language model, but we employed unigram language
model because it slightly outperformed BPE when they were used for machine translation.

3.1. Unigram Language Model
We explain the algorithm of unigram language model quoting from [8]. The unigram language
model makes an assumption that each subword occurs independently, and consequently, theprobability of a subword sequence X = (xm, . . . , xm) is formulated as the product of the subword
occurrence probabilities p(xi ). The most probable segmentation X* for the input sentence 𝑋 is
obtained with the Viterbi algorithm. Because the vocabulary set V is unknown, they seek to findthem with the following iterative algorithm.
1.
2.

Heuristically make a reasonably big seed vocabulary V from the training corpus.
Repeat the following steps until |V| reaches a desired vocabulary size.
(a) Fixing the set of vocabulary, optimize p(x) with the EM algorithm.
(b) Compute the lossi for each subword xi , where lossi represents how likely the likelihood
is reduced when the subword xi is removed from the current vocabulary.
(c) Sort the symbols by lossi and keep top η % of subwords.

Unigram language model is a method whose objective function is maximization of log likelihoodof X.

3.2. Procedures
We extracted linguistic speech patterns that characterize the lines as follows:
1. Collect lines of fictional characters,
2. Segment the lines into subword units using Sentence Piece, and
3. Weighted the subword units using TF/IDF values and obtain the top ten subword units.

88Computer Science & Information Technology (CS & IT)

In addition to the extraction experiments, we conducted classification experiments of characters.
Finally, we compared the results of the method using SentencePiece with that of one of the defacto standard morphological analyzers for Japanese, MeCab. We used ipadic for Japanesedictionary of MeCab.

4. DATA
We collected dialogs of 103 characters from 20 publications on the internet. They are, Anohana:
The Flower We Saw That Day, Den-noh Coil, Dragon Quest IV-VIII, Neon Genesis Evangelion,
Mobile Suit Gundam, Howl's Moving Castle, Hyouka, Kaguya-sama: Love Is War, Kemono
Friends, Harem Days, Whisper of the Heart, Laputa: Castle in the Sky, Spirited Away,
Symphogear, My Neighbor Totoro, and The Promised Neverland. This corpus of dialogs isreferred as to the “Character Corpus.” The following three methods were used for the collection.
1. They were collected from a compilation site of anime and game dialog on the internet.
2. They were collected from anime video sites.
3. It was converted from manga e-books using a text detection application.
Priority was given to characters with many lines while choosing character in the work.
Furthermore, since it was assumed that the majority of the main characters would be mostly
classified as boys, girls or younger men or women, we aggressively collected child and older
characters with a significant number of dialogs during the selection process. Because we haveclassification experiments according to age, characters whose ages change drastically during thestory have been removed. An example of this is Sophie from Howl's Moving Castle. She changed
from 18 to 90 years old in the movie. We also eliminated characters with extremely low amountsof dialog. The minimum, maximum and average numbers of lines of a character are respectively
92, 6,797, and 1,187.17.

5. EXPERIMENTS OF LINGUISTIC SPEECH PATTERN EXTRACTION
The procedure of linguistic speech pattern extraction by SentencePiece is as follows. First, wedevelop a segmentation model by applying SentencePiece to each character's dialog. Notably, weapply SentencePiece to sub-corpus of each character rather than the entire corpus. This is because
the way of talking varies according to each character. The following formula calculates the
maximum number of subword units:1

L -5

Vocabulary_Size =Basic_VS * ( l )(1)Where, l denotes the number of letters of each character's lines, L denotes the total number ofletters of lines of all characters, and Basic_VS denotes basic vocabulary size. We set Basic_VS
to 3,000. Simultaneously with the creation of the model, a word list from the vocab file was also
constructed. We delete from the word list subword units that consist of a single Chinese character
except for the first-person singular (僕, 私, 俺) because we believed that they would not express acharacterization. We also deleted 1/5 of the subword units with less emission logarithmic
establishment, which is a measure of a subword unit’s occurrence probability. For the next step,
we segment the character corpus using the segmentation model we created. The word lists andsegmented character corpus were used to obtain the TF/IDF value, which was calculated using
the following formulaComputer Science & Information Technology (CS & IT)

tf(t, d)= ∑n(t, d)89(2)sϵd n(s, d)where, tf(t, d) denotes the term frequency of a subword unit t in document d, n(t, d) denotes thenumber of occurrences of a subword unit t in document d, ∑sϵd n(s, d) denotes the sum of thenumber of occurrences of all subword units in the document d.
N

idf(t)=log df(t)(3)

where, idf(t) denotes the inverse document frequency of a subword unit t, N denotes total numberof documents, df(t) denotes number of documents in which a subword unit t occurred.
TF/IDF = tf(t, d) *idf(t)(4)

We extracted linguistic speech patterns that characterize lines of gender, ages, and charactersusing TF/IDF value. We considered the lines of all characters of one gender as one document,
and the lines of all character of the opposite gender as another document when calculating the
TF/IDF value for a gender.

6. CLASSIFICATION EXPERIMENT
We performed a classification experiment to evaluate the extracted linguistic speech patterns
using a SVM. The obtained TF/IDF values were used as inputs to the SVM to classify thecharacters into groups categorized by gender and age. The characters were first divided into three
categories: children, adults, and seniors. Children and adults were further divided into two
categories: male and female whereas seniors have only one group because we had few charactersof the ages. As a result, we used five groups: boys, girls, men, women, and seniors. The numbersof the character according to the group are shown in Table1. The group classification was
performed based on the character’s characterization and not on their actual age or gender because
the profiles of fictional characters are sometimes extraordinary. The bias in the amount of data
for each category is affected by the bias in characters; Japanese anime and games we collectedhave a few children and senior characters. The experiment was conducted using five-fold crossvalidation. Sklearn was used as a libraryin this experiment. The computational complexity of
SVM in sklearn varies between O (number of dimensionality * number of data ^2) and O(number of dimensionality * number of samples ^3), depending on how efficiently the cache is
used.
Table 1. Amount of data for Classification Experiment
Boys
6

Girls
8

Men
40

Women
41

Senior7

7. RESULTS
The linguistic speech patterns with the top 10 TF/IDF values are shown in Tables 2-7. In thetables, E represents ending, and F means first-person singular. Tables 2 and 3 list the linguistic
speech patterns with gender characterization, and Tables 4 and 5 show those with age
characterization. In these tables, Italic means that the pattern is specific for each characterizationof fictional characters. Some of the character-specific linguistic speech patterns are also shown as
example results in Tables 6 and 7. The example characters are Emma from the anime “The
Promised Neverland,” Shinji from the anime “Neon Genesis Evangelion,” and Yangus from the90Computer Science & Information Technology (CS & IT)

game “Dragon Quest VIII.” For the experiment of characters, we had a questionnaire to evaluate
the linguistic speech patterns. Eight native Japanese speakers were asked if each linguistic speech
pattern seems specific for the character. Five people are men, and three were women, and sevenpeople are in their 20's, and one person is in her 30's. They were also asked if they knew eachanime or game that the character appears. Tables 8 and 9 summarize the results of thequestionnaire. Finally, the results of the classification experiment are shown in Table 10.
Table 2. Linguistic Speech Patterns with Gender Characterization Retrieved by SentencePiece. E
represents ending and F denotes the first-person singular. Italic means that the pattern is specific for thepeople of specific ages as lines of fictional characters.
Male
Patterns
ですね
でござる
だぜ
でござる
な
アルス
だな
なあ
ますね
でがすよ
でござる
よ

Soundsdesunedegozarudazedegozaruna

Notes
Polite E
Samurai E
Masculine E
Samurai E

FemalePatterns
わね
かしら
のかしら
だわ

Soundswane
kashiranokashira
dawa

Notes
Feminine E
Feminine E
Feminine E
Feminine E

Arusu
dana
naa
masunedegasuyodegozaruyo

Name
Masculine E
Old buddy
Polite E
Dialect E
Samurai E

よね
のね
ないわ
わよ
ないわね
アルス

yone
none
naiwa
wayo
naiwaneArusu

E
Feminine E
Feminine E
Feminine E
Feminine E
Name

Table 3. Linguistic Speech Patterns with Gender Characterization Retrieved by MeCab. E represents
ending and F denotes the first-person singular. Italic means that the pattern is specific for the people ofspecific ages as lines of fictional characters.
Male

FemalePatterns

Sounds

Notes

Patterns

SoundsNotes

ござるgozaruSamurai E

あたしatashiF for girls

ざる

zaru

Error

かしら

kashira俺ore

F for male

アルス

Arusu

Feminine
E
Name

アルス

Arusu

Name

私

watashi

F

オイラ

oira

F for boys

・

.

Mark

げす

gesu

Dialect E

しら

shira

Error

・

.

Mark

リュカ

Ryuka

Name

僕

boku

F for boys

たし

tashi

Error

ウィル

Will

Name

ましょ

masyo

俺

ore

F for male

ウィル

Will

FemminineE
Name

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Table 4. Linguistic Speech Patterns with Age Characterization Retrieved by SentencePiece. E represents
ending and F denotes the first-person singular. Italic means that the pattern is specific for the people ofspecific ages as lines of fictional characters.

Patterns
なあChildrenSoundsnaa

Patterns
ですねAdultsSoundsdeshune

Arusu

Notes
Old
buddy
Name

アルス

わね

wane

お父さ
ん
オイラ

otosan

Dad

これ

kore

Feminin
eE
This

oira

です

deshu

Polite E

だよ

dayo

F for
boys
E

だな

dana

いっぱ
い
だぞ

ippai

Many

かしら

kashira

dazo

なんて

nante

てる

teru

Boyish
E
E

アルス

Arusu

Masculine E
FeminineE
Exclamatory how
Name

るの

runo

どこ

doko

Where

だね

dane

Femin
ine E
E

さん

san

title

Notes
Polite E

Patterns
でござ
る
でござ
るな
でござ
るよ
でござ
るか
アルス
殿
殿

Seniors
Soundsdegozarudegozaruna
degozaruyo
degozaruka
Arushud
ono
dono

るでご
ざるよ
とは

rudegoz
aruyo
towa

るでご
ざる
わし

rudegoz
aru
washi

Notes
SamuraiE
SamuraiE
SamuraiE
Samurai
E
Sir
Arusu
Sir
SamuraiE
C.f.
with
SamuraiE
F for oldmen

Table 5. Linguistic Speech Patterns with Age Characterization Retrieved by MeCab. E represents ending
and F denotes the first-person singular. Italic means that the pattern is specific for the people of specificages as lines of fictional characters

Patterns
オイラ
僕
おっち
ゃん
ゃっ
ちゃっ
ちゃう

ChildrenSounds
Notes
oira
F for
boys
boku
F for
boys
ochan
Pops
ya
cha
chau

オラ

ora

うわ

uwa

Error
Error
End up ing
F for
boys
Wow

じんた
オッチ
ャン

jinta
ochan

Wrror
Pops

Patterns
俺

AdultsSoundsore

Patterns
ござろSeniors
Soundsgozaro

Will

Notes
F for
male
Name

ウィル

ござっ

goza

リュカ

Ryuka

Name

など

nado

げす
アムロ
ひすい

gesu
Amuro
hisui

Dialect
Name
Name

フム
うむ
やはり

humu
umu
yahari

ゃっ

ya

Error

ちゃっ

cha

Error

サント
ハイム
いかん

santohai
mu
ikan

アニキ
ドルマ
ゲス

aniki
Dhoulm
agus

Bro
name

むう
ふむ

muu
humu

Notes
SamuraiE
SamuraisuffixSuch asHm-humHmmm
As
expectedName
No forold men
Hmmm
Hm-hum92

Computer Science & Information Technology (CS & IT)

Table 6. Character-specific Linguistic Speech Patterns Retrieved by SentencePiece. E represents ending
and F denotes the first-person singular.

Patterns
てる

EmmaSounds
teru

Notes
E

Patterns
ですかShinjiSoundsdesuka

にも

nimo

And

ってこ
と
ちょ

ttekotoMisatosanbokuha

cho

That
means
Wait

ミサト
さん
僕は
ないよ

naiyo

いいよ

iiyo

OK

父さん

tosan

Name
with title
I am (Ffor men)
there
isn't
Dad

嫌だ

iyada

No

るんだ

runda

E

の手

note

Hand of

僕

boku

私たち
の
信じ

watashitachinoshinji

Our

だよ

dayo

F for
men
E

Believe

綾波

Ayanam
i

Name

もし

moshi

If

んです
か

ndesuka

E forquestion

Notes
Polite E

Patterns
でがすよ
でがす

YangusSoundsdegasuyodegasuでげすdegesuDialect

でげす
よ
でがす
ね
おっさ
ん
かい

degesuyodegasune
ossan

Dialect

kai

E

んでが
す
アッシ
は
アッシ
ら

ndegasu

DialectasshihaI am formenasshiraWe formen

Notes
Dialect
Dialect

Dialect
Pops

Table 7. Character-specific Linguistic Speech Patterns Retrieved by MeCab. E represents ending and F
denotes the first-person singular.
EmmaSoundswatashi

Notes
F

Patterns
僕

ShinjiSounds
boku

レイ
ノーマ
ン
マン

Rei
Noman

Name
Name

ミ
サト

man

Error

思う
うん

omou
un

近寄っ

chikayo

折れ

ore

寄っ

yo

そっ

so

Patterns
私

Patterns
げすYangusSoundsgesu

Notes
Dialect

mi
sato

Notes
F for
men
Error
Error

がす
アッ

gasu
a

Dialect
Error

父さん

tosan

Dad

アッシ

asshi

Think
Yes

さん
うわ

san
uwa

Title
Wow

すね
すか

sune
suka

Draw
near
Be
folded
Draw
near
Error

スカ

suka

Error

やしょ

yasho

F for
men
E
E for
question
Dialect

アスカ

Asuka

Name

ossan

Pops

トウジ

Touji

Name

nechanSis

僕らbokuraWe for
men

おっさ
ん
姉ちゃ
ん
ダンナ

danna

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Table 8. Number of People Who Think the Linguistic Speech Pattern Extracted by SentencePiece is
specific for the Character and Its Percentages. W/ represents with knowledge of the anime or game and w/o
indicates without knowledge. People represents number of people with and without knowledge of theanime or game.

Patterns
People
てる
にも

Emma
w/
4
0
0

ってこと
ちょ
いいよ
嫌だ
の手
私たちの
信じ
もし
Total
Percent

0
0
1
3
0
4
3
0
11
27.50%

Avarage15.00%

w/o
4
0
0
0
0
0
0
0
1
0
0
1
2.50%

Patterns
People
ですか
ミサトさ
ん
僕は
ないよ
父さん
るんだ
僕
だよ
綾波
んですか
Total
Percent
Avarage

Shinji
w/
7
0
1

w/o
1
0
0

Patterns
People
でがすよ
でがす

3
0
3
1
2
0
4
0
14
20.00%

1
0
1
1
1
0
0
0
4
40.00%

でげす
でげすよ
でがすね
おっさん
かい
んでがす
アッシは
アッシら
Total
Percent

22.50%

Avarage

Yangus
w/
3
3
3
3
3
3
1
1
3
3
3
26
86.6
7%
75.00%

w/o
5
4
4
4
4
4
2
2
4
3
3
34
68.00%

Table 9. Number of People Who Think the Linguistic Speech Pattern Extracted by MeCab is specific for
the Character and Its Percentages. W/ represents with knowledge of the anime or game and w/o indicateswithout knowledge. People represents number of people with and without knowledge of the anime or
game.

Patterns
People
私
レイ
ノーマン
マン
思う
うん
近寄っ
折れ
寄っ
そっ
Total
Percent
Avarage

Emma
w/
4
0
1
1
0
0
0
0
0
0
0
2
5.00%
2.50%

w/o
4
0
0
0
0
0
0
0
0
0
0
0
0.00%

Patterns
People
僕
ミ
サト
父さん
さん
うわ
スカ
アスカ
トウジ
僕ら
Total
Percent
Avarage

Shinji
w/
7
2
0
0
3
0
0
0
4
0
1
10
14.29%
16.25%

w/o
1
1
0
0
1
0
0
0
0
0
1
3
30.00%

Patterns
People
げす
がす
アッ
アッシ
すね
すか
やしょ
おっさん
姉ちゃん
ダンナ
Total
Percent
Avarage

Table 10. Results of Classification Experiment
SentencePiece
0.627

MeCab
0.451

Yangus
w/
3
3
3
0
3
2
2
2
1
0
1
17
56.67%
48.75%

w/o
5
4
4
0
3
1
1
1
2
3
3
22
44.00%

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8. DISCUSSION
Tables 2-7 shows that regardless of whether the SentencePiece or MeCab model is used, many
endings of utterances and first-person singulars are extracted as specific linguistic speech
patterns. We believe that they substantially characterize Japanese dialog. Many personal names
are also extracted, although they are not linguistic speech patterns, because they often appeared in
the lines of characters. MeCab found 13 error expressions whereas the SentencePiece model
found none. Here, an error means the expression has no meaning due to a segmentation error.
This result indicates that a conventional morphological analyzer sometimes fails to segment
unusual sentences such as lines of fictional characters. Furthermore, we can observe from thetables that the SentencePiece model can obtain linguistic speech patterns that consist of many
words. For example, “desune” consists of “desu” and “ne” and “wane” consists of “wa” and “ne.”The SentencePiece model could retrieve these linguistic speech patterns because it used subword
units.
Furthermore, the SentencePiece model retrieved seven masculine and nine feminine linguistic
speech patterns for the gender experiment, whereas MeCab retrieved six masculine and four
feminine linguistic speech patterns. For age experiment, the SentencePiece model obtained six,
two, and seven linguistic speech patterns that are children, adults, and seniors, respectively,
whereas MeCab retrieved six, one, seven linguistic speech patterns. For the experiment of ages,
the difference between the two models was smaller than that of gender. We believe that thesystems could not extract linguistic speech patterns specific for adults because their talking way
is considered normal.
Next, let us discuss the experiment of each character. This is more difficult than the discussion ofgender or age because the knowledge of the character can affect the results. Therefore, we had aquestionnaire for eight people. Tables 8 and 9 show that the SentencePiece model always obtains
more character-specific linguistic speech patterns than MeCab for every character. The
knowledge of the characters did not affect this result. However, the people with knowledge
considerably feel that the linguistic speech patterns are specific for Emma, but the people without
knowledge feel they are not so much. According to English Wikipedia, “The bright and cheerful
Emma is an 11-year-old orphan living in Grace Field House, a self-contained orphanage housing
her and 37 other orphans.” We believe that people without knowledge tend to think she is an
adult woman because Emma is a female name. People without knowledge could think that theextracted patterns are not character-specific because they include no feminine patterns.
Additionally, according to the Dragon Quest Wiki, “Yangus is a character in Dragon Quest VIII
who accompanies the Hero on his missions.” and “He serves as a powerful tank character overthe course of the game.” As for Yangus, the people with knowledge feel that the linguistic speech
patterns are more character-specific again. Moreover, the percentage where people think they arecharacter-specific is the highest among the three characters. We believe that this is because
Yangus speak a dialect-originated and specific language. According to English Wikipedia,“Shinji is a dependent and introverted boy with few friends, reluctant unable to communicate
with other people and frightened by contact with strangers.” Although there could be a bias
because only one person did not know him, people who did not know felt that the extracted
patterns were more character-specific. These results indicate that the extracted expressions using
subword units are more interpretable linguistic speech patterns than those using words. The
classification results also showed that the SentencePiece model outperformed MeCab for theclassification of character groups. Additionally, it indicates that the patterns are more specific foreach character group feature. Notably, the subword units are proposed for deep learningtechnologies but our classification did not use any of them. The experiments showed that the
subword units are effective when no deep learning technologies are used.

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9. CONCLUSIONSIn this study, we proposed using subword units to segment dialogs of fictional characters. The
experiments revealed that subword units weighted with TF/IDF values are character-specificlinguistic speech patterns, that cannot be obtained from existing morphological analyzers using
dictionaries. They also showed that the linguistic speech patterns retrieved using SentencePiece
are more specific for gender, age, and each character. It indicates that the extracted expressions
using subword units are more interpretable than those using words. We also conducted anexperiment that classifies the characters into character groups using the extracted linguistic
speech patterns as features, and the classification SentencePiece model’s accuracy was comparedto the case where MeCab was used to segment the dialogs. We showed that subword units are
effective even though no deep learning technologies are used with them. In the future, we would
like to consider parts of speech when segment terms. Also, we are interested in research togenerate sentences with characterization using the linguistic speech patterns extracted in thisstudy.

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