MORPHOLOGICAL ANALYSIS OF
JAPANESE HIRAGANA SENTENCES USING
THE BI-LSTM CRF MODEL
Jun Izutsu1 and Kanako Komiya2
1

2

Ibaraki University, JapanTokyo University of Agriculture and Technology, Japan

ABSTRACTThis study proposes a method to develop neural models of the morphological analyzer forJapanese Hiragana sentences using the Bi-LSTM CRF model. Morphological analysis is atechnique that divides text data into words and assigns information such as parts of speech. This
technique plays an essential role in downstream applications in Japanese natural language
processing systems because the Japanese language does not have word delimiters between
words. Hiragana is a type of Japanese phonogramic characters, which is used for texts forchildren or people who cannot read Chinese characters. Morphological analysis of Hiragana
sentences is more difficult than that of ordinary Japanese sentences because there is lessinformation for dividing. For morphological analysis of Hiragana sentences, we demonstrated
the effectiveness of fine-tuning using a model based on ordinary Japanese text and examined theinfluence of training data on texts of various genres.

KEYWORDS
Morphological analysis, Hiragana texts, Bi-LSTM CRF model, Fine-tuning, Domain
adaptation.

1. INTRODUCTION
1.1. Components of the Japanese Language and the Acquisition Process
Japanese sentences contain various kinds of character, such as Kanji (Chinese character),Hiragana, Katakana, numbers, and alphabet, making it difficult to learn. Japanese speakers
usually learn Hiragana first in their school days because the number of characters is much smaller
than the Kanji; Hiragana has 46 characters, and Japanese use thousands of Kanji. Most Japanese
sentences are composed of all kinds of characters and are called Kanji-Kana mixed sentences.
However, it is difficult for many non-Japanese speakers to learn thousands of Kanji, so children
and new Japanese language learners use Hiragana.

1.2. Morphological Analysis of Hiragana Sentences
Morphological analysis is a technique that divides natural language text data into words andassign information such as parts of speech. In Japanese, morphological analysis is one of the coretechnologies for natural language processing because the Japanese language does not have worddelimiters between words. Morphological analyzers like MeCab[1], Chasen and Juman++[2,3]David C. Wyld et al. (Eds): NLP, MLTEC, CLBD, SEAPP, NeTIoT, VLSIE, ITCS, ARIA, DMS - 2021
pp. 123-135, 2021. CS & IT - CSCP 2021
DOI: 10.5121/csit.2021.112310

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are now commonly used for morphological analysis. However, since the above systems target
Kanji-Kana mixed sentences, it is challenging to perform morphological analysis using only
Hiragana sentences.
Morphological analysis of Hiragana-only sentences is more challenging than morphologicalanalysis of Kanji-Kana mixed sentences. If the text is a mixture of Kanji and Kana (Hiragana andKatakana), it will be divisible between Kanji, Hiragana, and Katakana. However, if the text
consists only of Hiragana, there will be less information for dividing. Therefore, we propose tofine-tune the model of Kanji-Kana mixed sentences and investigated whether the accuracy ofmorphological analysis of Hiragana sentences can be improved by inheriting the information to
be divided into words.
The Bi-LSTM CRF model was used to develop a morphological analyzer for Hiragana sentencesin this paper. We used two types of training data: Wikipedia and Yahoo! Answers in the
Balanced Corpus of Contemporary Written Japanese, in our studies to investigate the influence ofthe genre of the training data. We also fine-tune both data to examine the effect of various genreson the text. [4]The following are the four contributions of this paper.


Developed a morphological analyzer for Hiragana sentences using the Bi-LSTM CRF model,
Demonstrated the effectiveness of fine-tuning using a model based on Kanji-Kana mixedtext,
Examined the influence of training data of morphological analysis on texts of various genres,
andDemonstrated the effectiveness of fine-tuning using data from Wikipedia and Yahoo!
Answers.

In this paper, we report the results of these experiments.

2. RELATED WORK
Izutsu et al. (2020) [5] converted MeCab’sipadic dictionary into Hiragana and performed
morphological analysis on Hiragana sentences using a corpus consisting only of Hiragana. To ourknowledge, only this work developed a morphological analyzer for only Hiragana sentences.We
also developed a morphological analyzer for only Hiragana sentence, but we try to achieve thisgoal without any dictionary.
There are some studies focus on morphological analyzers for Hiragana-highly-mixed sentences
and most of them treated a lot of Hiragana words as noises or broken Japanese. For example,
Kudo et al. (2012) [6] used generative model to model the process of generating Hiragana noisemixed sentence. They proposed using a large-scale web corpus and EM algorithm to estimate themodel’ s parameters to improve the analysis of Hiragana noise-mixed sentences. Osaki et al.
(2016) [7] constructed a corpus for broken Japanese morphological analysis. They defined newparts of speech and used them for broken expressions. Fujita et al. (2014) [8] proposed an
unsupervised domain adaptation technique that uses the existing dictionaries and labeled data tobuild a morphological analyzer by automatically transforming them for the features of the target
domain. Hayashi and Yamamura (2017) [9] reported that adding Hiragana words to thedictionary can improve the accuracy of morphological analysis.

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We used Bi-LSTM model to develop a morphological analyzer. Ma et al. (2018) [10] developed
a word segmentation model for Chinese using the Bi-LSTM model.They reported that word
segmentation accuracy achieved better accuracy on public datasets than the Bi-LSTM model,
compared to models based on more complex neural network architectures.
Also, Thattianaphanich and Prom-on (2019) [11] developed the Bi-LSMT CRF model andperformed named entity recognition extraction in Thai. In Thai, there are linguistic problems suchas lack of linguistic resources and boundary indices between words, phrases, and sentences.
Therefore, they prepared word representations and learned text sequences using Bi-LSTM andCRF to address these problems.
In work on Japanese morphological analyzers, Tolmachev et al. created a morphological modelusing neural networks and semi-supervised learning, and showed that their method performed
comparably to traditional dictionary-based state-of-the-art methods, and could even outperformthem when trained on a combination of human-annotated and automatically annotated data [12].
In addition, Chen et al. created an LSTM-based neural network model for Chinese word
segmentation [13]. Although most of the current state-of-the-art methods for Chinese word
segmentation are based on supervised learning, they report that their LSTM-based model
outperforms traditional neural network models and state-of-the-art methods.

3. METHODSIn this research, we used the Bi-LSTM CRF model to generate a morphological analyzer for
Hiragana sentences.We trained and compared the following five models for Hiragana sentencesin our studies.
1.
2.
3.
4.
5.

the Hiragana Wiki model
the Hiragana Yahoo! model
the Kanji-Kana Wiki+ Hiragana Wiki model
the Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana Yahoo! model
the Hiragana Wiki+ Hiragana Yahoo! model

The generation processes of the five models for Hiragana sentences are shown in Figure 1.

3.1. Hiragana Wiki Model
The Hiragana Wiki model is the model generated in training B (Figure 1). We used the data from
Wikipedia, where Kanji-Kana mixed sentences are converted to Hiragana, for training data.
Hiragana is a phonogram, and Kanji could be converted into Hiragana according to its
pronunciation. We used MeCab's reading data as a pseudo-correct answer for the conversion
because Wikipedia does not have Hiragana-only data. Here, UniDic was used as a dictionary of
MeCab. [14]3.2. Hiragana Yahoo! Mode
The model generated in training D (Figure 1) is the Hiragana Yahoo! model. We used reading
data from Yahoo! Answers as training data. We compared its accuracy to a Hiragana Wiki +Hiragana Yahoo! model (training F in Figure 1) and Kanji-Kana Wiki+ Hiragana Wiki+
Hiragana Yahoo! model (training E in Figure 1).

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3.3. Kanji-Kana Wiki+ Hiragana Wiki ModelIn this paper, we proposed fine-tuning using a model with Kanji-Kana mixed sentences. The
Kanji-Kana Wiki+ Hiragana Wiki model is generated using the original Wikipedia data andHiragana Wikipedia data, which are the actual data converted into only Hiragana. We fine-tuned
Kanji-Kana Wiki model, the model trained by the original Wikipedia data (training A in Figure1), with Hiragana Wikipedia data, which is Wikipedia data automatically converted into Hiragana
sentences (training C in Figure 1). Kanji-Kana mixed sentences contain many clues formorphological analysis, such as borderline between Kanji and Hiragana and information about
Kanji. Therefore, it is expected to improve accuracy. By comparing the Hiragana Wiki model andthis model, we can assess the effectiveness of fine-tuning based on Kanji-Kana mixed Wikipedia
data, when we only have Wikipedia data.

3.4. Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana Yahoo! Model
Training E in Figure 1 generates the Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana Yahoo! model.
To improve accuracy, we used both Wikipedia and Yahoo! Answers as training data.
We fine-tuned the Kanji-Kana Wiki+ Hiragana Wiki model with Hiragana Yahoo! Answers data
for this model.

3.5. Hiragana Wiki+ Hiragana Yahoo! Model
The Hiragana Wiki+ Hiragana Yahoo! model is the model generated in training F in Figure 1. Astraining data, we used Hiragana Wikipedia data and Hiragana Yahoo! Answers data. We finetuned the Hiragana Wiki model with Hiragana Yahoo! Answers.
We can see the effectiveness of the Kanji-Kana Wiki+ Hiragana Wiki model, i.e., how much the
Kanji-Kana mixed model affects this model, when we have both Wikipedia data and Yahoo!
Answers by comparing the accuracy of this model to Kanji-Kana Wiki+ Hiragana Wiki+
Hiragana Yahoo! model.

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Figure 1. Model generation processes of the five models for Hiragana sentences

4. EXPERIMENT
4.1. Network Architecture
We developed Bi-LSTM CRF models using Bi-LSTM CRF module of Pytorch.
Table 1 summarizes the hyperparameters used in this experiment. The dimensions for the
network architecture were determined according to the preliminary experiments.
Table 1. Hyperparameters used in the experiment
EMBEDDING_DIM
HIDDEN_DIM
Lr
Weight decay
Optimizer
Epoch number

5
4
0.01
1e-4
sgd
15

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The tag size is 38 for all models and the vocabulary size varies according to the model because itis the total character type number of the training corpus. The vocabulary size of the models thatuse only Hiragana sentences are 192 and that of the model that uses Kanji-Kana Wiki data was
2,742.

4.2. Training Data
We used two kinds of training data in this experiment: Japanese Wikipedia data and “Yahoo
Answers Data” from BCCWJ of the National Institute for Japanese Language and Linguistics.
For this experiment, the data from Wikipedia were extracted fromjawiki-latest-pages-articles.xml.bz2
which is published on the website for this experiment.
We preprocessed the Wikipedia data before training to obtain Hiragana-only sentences. The
preprocessing procedures are as follows. First, we conducted morphological analysis on the
Wikipedia data using MeCab. The output of MeCab has features: word segmentation, part ofspeech, part of speech subdivision 1, part of speech subdivision 2, part of speech subdivision 3,
conjugation type, conjugated and basic forms, reading, and pronunciation.
Table 2. MeCab's analysis result for “僕は君と遊ぶ"
君(Kimi)
Noun
pronounGeneralは(Ha)
Particle
Binding
particle*

遊ぶ(Asobu)Verb
Non-bound

Generalと(To)
ParticleCaseparticle
General

*

*

*

*

*

Conjugation type
Conjugated form
Basic form

*
*

*
*

*
*

*
*

Godan_verb_ba_column
Basic form

僕
(Boku)

は(Ha)君(Kimi)と(To)

遊ぶ(Asobu)Reading

ボク(Boku)

ハ(Ha)

キミ(Kimi)ト(To)

アソブ(Asobu)Pronunciation

ボク(Boku)

ワ(Wa)

キミ(Kimi)ト(To)

アソブ(Asobu)Part of speechPart-of-speech
subdivision 1
Part-of-speech
subdivision 2
Part-of-speech
subdivision 3

僕(Boku)
Noun
Pronoun*Let us take the example of the sentence, “僕は君と遊ぶ" (Bokuwakimi to asobu). Table 2 shows
the output result of MeCab when we input this example sentence. This is a Kanji-Kana mixed
sentence, which means “I play with you." If this sentence is expressed entirely in Hiragana, thatwould be “ぼくはきみとあそぶ. "We obtained Hiragana data by replacing the surface forms of words with their readings. Next, we
split the Hiragana data into individual characters and assign a part-of-speech tag to each of them.

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Here, B-{Part-of-Speech} is assigned to the first Hiragana character, and I-{Part-of-Speech} isassigned to the following Hiragana characters, if the Kanji consisted of more than two syllables.
By formatting this Hiragana sentence as described above, we can obtain the character data andtags corresponding to the character data with one-to-one correspondence (Table 3).
Table 3. Example of splitting “ぼくはきみとあそぶ"
ぼ(bo)B-Nounく(ku)
I-Nounは(ha)
B-Particle

き

み

(ki)
B-Noun(mi)
I-Nounと(to)
B-Particle

あ(a)B-Verb

そ
(so)
IVerb

ぶ
(bu)
I-VerbPlease note that Japanese Kanji characters often have more than one reading. For example, “君"could be Kimi or Kun in Japanese. Therefore, sometimes this conversion makes some errors. The
number of characters in the training data is 1,183,624.
We used the original Japanese Wikipedia data for the model that uses Kanji-Kana mixed
sentences. Table 4 shows the characters and their tags of the Kanji-Kana mixed sentence, “僕は君と遊ぶ."
Table 4. Example of splitting “僕は君と遊ぶ"
僕(boku)
B-Nounは(ha)
B-Particle

君(kimi)
B-Nounと(to)
B-Particle

遊ぶ
(aso)
B-Verbぶ(bo)I-VerbFor Yahoo! Answers data, we extracted the reading data from the corpus.
Native Japanese speakers manually annotate them, but sometimes they could be different fromthe authors' intent. For example, “日本" (Japan in Japanese) has two readings, “Nihon" and“Nippon," and both of them are correct in most sentences. Therefore, in these cases, only theauthor can guarantee which one is correct. In BCCWJ, some words had a predefined reading to
reduce the burden of annotators.

4.3. Test Data
We also used the BCCWJ for the test data.
The BCCWJ provides sub-corpora and we used 12 of them.
The number of characters used in this experiment for each dataset is shown in Table 5.
Table 5. Number of characters for each sub-corpus in BCCWJ
Dataset Name
Books (Library sub-corpus)
Bestsellers
Yahoo! Answers

Number of characters
1,374,216
1,093,860
830,960

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Legal Documents
National Diet Minutes
PR Documents
Textbooks
Poems
Reports
Yahoo! Blogs
Books
Newspapers

2,316,374
2,050,400
2,151,126
956,927
466,878
2,546,307
1,305,660
1,281,251
1,301,728

The test data are tagged Hiragana characters with a one-to-one correspondence between the
Hiragana character and the tag-based on reading and part-of-speech information, were created in
the same way as the training data.
The data from “Yahoo! Answers" are also used as training data for creating the Hiragana Yahoo!
model, the Hiragana Wiki + Hiragana Yahoo! model, and the Kanji-Kana Wiki+ Hiragana Wiki+
Hiragana Yahoo! model, however we used different parts for the training and testing.

5. RESULT
According to genres of the test data, Tables 6 and 7 summarize the accuracies of the Hiragana
Wiki model, Kanji-Kana Wiki+ Hiragana Wiki model, Kanji-Kana Wiki+ Hiragana Wiki+
Hiragana Yahoo! model, Hiragana Yahoo! model, and Hiragana Wiki+ Hiragana Yahoo! model.
For the evaluation of each test dataset, we used macro and micro-averages of accuracy. Macrorepresents the macro-averaged accuracy, and micro represents the micro-averaged accuracy inTables 6 and 7. In Table 6,blue numbers indicate that the accuracy of the Kanji-Kana Wiki+
Hiragana Wiki model or the Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana Yahoo! model was
lower than that of the Hiragana Wiki model, and an asterisk indicates that this difference wassignificant according to a chi-square test for accuracy at the 5% significance level. In Table 7,
Magenta numbers indicate that they are lower than the accuracy of the Kanji-Kana Wiki +Hiragana Wiki+ Hiragana Yahoo! model, and italics indicate that they are lower than theaccuracy of the Hiragana Yahoo! model.An asterisk indicates that the difference was significant
according to a chi-square test for accuracy at the 5% significance level. A plus indicates that theHiragana Wiki+ Hiragana Yahoo! model was different from the Hiragana Yahoo! modelaccording to a chi-square test for accuracy at the 5% significance level.The bold numbers are thebest results of the models in Tables 6 and 7.
For the Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana Yahoo! model, the Hiragana Yahoo! model,
and the Hiragana Wiki+ Hiragana Yahoo! model, the accuracies of Yahoo! Answers, are resultsof the closed test, and that is why they are written in parentheses.Also, the Yahoo! Answers
evaluation data are removed from the macro and micro averages of all models. Therefore, they
are average of 11 sub-corpora except for Yahoo! Answers data.
Table 6. Accuracy of Hiragana Wiki model, Kanji-Kana Wiki+ Hiragana Wiki model, and Kanji-KanaWiki+ Hiragana Wiki+ Hiragana Yahoo! model according to each text genre.
Hiragana Wiki

Books (Library subcorpus)
Bestsellers

Kanji-Kana Wiki+
Hiragana Wiki

56.98

*57.1550.14*50.71

Kanji-Kana Wiki+
Hiragana Wiki+
Hiragana Yahoo!
*62.76*60.48

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Yahoo! Answers
Legal Documents

48.32
63.39

*50.2363.32(*65.50)*60.55National Diet Minutes
PR Documents
Textbooks
Poems
Reports

48.49
64.27
57.43
41.96
65.83

*49.59*63.93*58.01*42.45*65.57*60.63*64.55*62.04*48.82*65.15

Yahoo! Blogs
Books
Newspapers

57.12
55.73
62.58

57.10
55.77
*62.30*62.97*62.83*64.50

Macro
Micro

56.72
58.67

56.90
58.80

61.39
62.44

Blue numbers mean they are lower than the accuracy of the Hiragana Wiki model and an asterisk
indicates that the model was different from the Hiragana Wiki model according to a chi-square
test at the 5% level of significance. The bold numbers are the best results of the models.
Table 7. Accuracy of Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana Yahoo! model, Hiragana Yahoo!
model, and Hiragana Wiki+ Hiragana Yahoo! model according to each text genre.

Books (Library subcorpus)
Bestsellers
Yahoo! Answers
Legal Documents
National Diet Minutes

Kanji-Kana Wiki+
Hiragana Wiki+
Hiragana Yahoo!
62.76

Hiragana Yahoo!

Hiragana Wiki+
Hiragana Yahoo!

*63.11*+63.5260.48(65.50)
60.55
60.63

*60.16(*65.85)*60.17*60.49*+61.32*+66.38*+62.63*+61.97

PR Documents

64.55*65.49*+63.91

Textbooks
Poems
Reports

62.04
48.82
65.15

61.97
*47.61*66.89*+62.66*+49.55*+64.89

Yahoo! Blogs
Books
Newspapers

62.97
62.83
64.50

+62.90*63.36+64.51

Macro
Micro

61.39
62.44

*64.02*63.28*65.54
61.70
62.93

61.93
63.01

Magenta numbers indicate that they are lower than the accuracy ofthe Kanji-Kana Wiki +Hiragana Wiki+ Hiragana Yahoo! model, and italics indicate that they are lower than theaccuracy of the Hiragana Yahoo! model. An asterisk indicates that the model was different fromthe Kanji-Kana Wiki + Hiragana Wiki+ Hiragana Yahoo! model according to a chi-square test at
the 5% significance level. A plus indicates that the model is different from Hiragana Yahoo!
model at the 5% significance level. The bold numbers are the best results of the models.
We also evaluated the training data using MeCab. For the dictionary of MeCab, we used ipadic
with the conversion of Kanji into Hiragana. The macro-averaged accuracy was 79.71%, and the132Computer Science & Information Technology (CS & IT)

micro-averaged accuracy was 80.10%.Please note that this result cannot simply be comparedwith results in Tables 6 and 7 because our system did not use a dictionary for the morphologicalanalysis itself.

6. DISCUSSION
Table 6 shows that the Kanji-Kana Wiki+ Hiragana Wiki model's macro and micro-averaged
accuracies (56.90% and 58.80%) are higher than those of the Hiragana Wiki model (56.72% and58.67%). The macro-averaged accuracy of the Kanji-Kana Wiki+ Hiragana Wiki model
improved by 0.18 points, while the micro-averaged accuracy by 0.13 points. This result indicatesthat the fine-tuning using the Kanji-Kana Wiki model is somehow effective.
Furthermore, the macro and micro-averaged accuracies of the Kanji-Kana Wiki+ Hiragana Wiki+
Hiragana Yahoo! model (61.39% and 62.44%) are superior to those of the Kanji-Kana Wiki+
Hiragana Wiki model (56.90% and 58.80%). The Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana
Yahoo! model improved the accuracy by 4.49 points on the macro-averaged accuracy and by
3.64 points on the micro-averaged, indicating that further fine-tuning using Hiragana Yahoo! dataconsiderably improve the permanence. Additionally, from Tables 6 and 7, we can confirm thatthe macro- and micro-averaged accuracies of the Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana
Yahoo!, Hiragana Yahoo!, and Hiragana Wiki+ Hiragana Yahoo! models, the models using
Yahoo! Answers as training data, are higher than those of Wikipedia and Kanji-Kana Wiki+
Hiragana Wiki models, the models using only Wikipedia as training data. In other words, theperformance of the model is better when using Yahoo! Answers. We believe there could be two
reasons for this result, the quality of the corpus and the similarity of the training and test data.
The Hiragana Yahoo! data quality could be better than the Hiragana Wiki data because Hiragana
Yahoo! data are manually annotated, whereas Hiragana Wiki data are automatically generated.
Moreover, because the training and test data are both sub-corpora of BCCWJ, they can be moresimilar than when the training and test data are Wikipedia and BCCWJ.
Additionally, comparing the macro- and micro-averaged accuracies of the Kanji-Kana Wiki+
Hiragana Wiki+ Hiragana Yahoo! model with those of the Hiragana Yahoo! model and theHiragana Wiki+ Hiragana Yahoo! model in Table 7, we confirmed that the Hiragana Wiki+
Hiragana Yahoo! model is the best, and the Hiragana Yahoo! model is the second best, and the
Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana Yahoo! model is the last. This result indicates thatwhen the Hiragana Yahoo! data are available, the fine-tuning using both the Kanji-Kana Wikimodel and the Hiragana Wiki model is not effective, although the fine-tuning using only theHiragana Wiki model is useful. Notably, the accuracy of some types of test data is improved
while others are decreased. The Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana Yahoo! model was
higher than the Hiragana Yahoo! model for the “Bestsellers," “Legal Documents," “National Diet
Minutes," “Textbook," and “Poems", and it was higher than the Hiragana Wiki+ Hiragana
Yahoo! model for “PR Documents", “Reports", and “Yahoo! Blogs" but there was no genre
where the Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana Yahoo! model was the best in these threemodels.Therefore, we think that the reason why the Kanji-Kana Wiki model was useful when
only the Hiragana Wiki data were available could be that the Hiragana Wiki data wereautomatically generated.
Now, let us discuss the difference among the genres of the texts. The genres where the accuracyof the Hiragana Wiki model was more than 60% were four genres: “Legal Documents," “PRDocuments," “Reports," and “Newspapers."

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We believe this is because the writing style of these test data is close to that of Wikipedia.
Therefore, we marked these genres with underline in Tables 6 and 7. Additionally, we can see
that, as for these genres, the fine-tuning is not useful. The accuracies of the Kanji-Kana Wiki+
Hiragana Wiki model decreased for these four genres, and those of the Kanji-Kana Wiki+
Hiragana Wiki+ Hiragana Yahoo! model also decreased for two genres. As for the remaining two
genres, the improvements are less than two points.
Figure 2 shows the effects or the differences of the accuracies of Hiragana Yahoo! data according
to the genres of the texts. The blue line is its effects when the base model is the Kanji-KanaWiki+ Hiragana Wiki model and the orange line is when the base model is the Hiragana Wikimodel. This figure shows that the effects are the same even though the base models are different.

Figure 2. Effect of Hiragana Yahoo! data

Hiragana Yahoo! data was considerably effective for “Yahoo! Answers," “Books," and “National
Diet Minutes." Hiragana Yahoo! data is text data of the question answering sites. Therefore, thewriting style is rather like spoken language. We believe that it is effective for “Books" and“National Diet Minutes" because the Hiragana Books include dialogues and National Diet
Minutes is transcription of the discussion of the Diet. In particular, Wikipedia data rarely include
question sentences but question answering sites contain many. Therefore, Hiragana Yahoo! data
is effective for “National Diet Minutes" that includes many questions.
Figure 3 shows the effects or the differences of the accuracies of Kanji-Kana Wiki data according
to the genres of the texts. The blue line is its effects when the base model is the Hiragana Wikimodel, and the orange line is when the base model is the Hiragana Wiki+ Hiragana Yahoo!
model. As contrasted to Figure 2, the effects of the data are almost opposite depending on thebase model. The blue line is similar to the lines in Figure 2 but the orange line is very different
from those. The orange line, the effects of the Hiragana Yahoo! data when it was added to theHiragana Wiki+ Hiragana Yahoo! model shows that the data is effective for “PRDocuments,”“Reports," and“Yahoo! Blogs." The Kanji-Kana Wiki data tends to effective when
the fine-tuning of the Hiragana Yahoo! is not effective. These facts indicate that the fine-tuning iseffective when the original accuracy was poor, regardless of whether it used Kanji-Kana Wikitext or Hiragana Yahoo! data.

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Figure 3. Effect of Kanji-Kana Wiki data

Finally, the accuracy of the Bi-LSTM CRF model still has room for improvement compared tothe existing morphological analyzer for Kanji-Kana mixed text. We think that the use ofdictionary and much more data should be attempted in the future.

7. CONCLUSIONSThis study developed a morphological analysis model for Japanese Hiragana sentences using the
Bi-LSTM CRF model.We showed that the performance of morphological analysis of Hiragana
sentences outperformed when we use in-domain data manually annotated for fine-tuning by theexperiments using Hiragana data of Wikipedia and Yahoo! Answers data. We also showed thatthe performance of Hiragana morphological analysis is improved when we fine-tune the model ofHiragana sentences with Kanji-Kana mixed sentences. This fine-tuning is effective when theoriginal accuracy was low. Additionally, we showed that the performance of morphologicalanalysis varied according to the text genre. In the future, we would like to increase the number oftraining data to improve the accuracy of the model for analyzing Hiragana sentences.
Additionally, we would like to make the system capable of outputting word reading,pronunciation, and part-of-speech classification.In this experiment, we used the Yahoo! Answers
as a training example for the Kanji-Kana Wiki+ Hiragana Wiki+ Hiragana Yahoo! model andHiragana Yahoo! models to measure the accuracy, but we would like to train the models using
other data to analyze the effect of genre differences.Furthermore, we would like to investigate theeffective domain adaptation method when the original accuracy is not bad.

REFERENCES
[1]

[2]

[3]

Kudo, Taku., Yamamoto, K., and Matsumoto, Y. (2004). “Applying conditional random fieldsto
Japanese morphological analysis.” In Proceedings of the 2004 Conference on Empirical Methods in
Natural Language Processing, pp. 230–237.
Morita, Hajime., Kawahara, D., and Kurohashi, S. (2015). “Morphological analysis for unsegmented
languages using recurrent neural network language model.” In Proceedings of the 2015 Conferenceon Empirical Methods in Natural Language Processing, pp. 2292–2297.
Tolmachev, Arseny, Daisuke Kawahara, and Sadao Kurohashi. "Juman++: A morphological analysis
toolkit for scriptio continua." Proceedings of the 2018 Conference on Empirical Methods in Natural
Language Processing. 2018, pages 54-59.

Computer Science & Information Technology (CS & IT)

135

[4]

Kikuo Maekawa, Makoto Yamazaki, Toshinobu Ogiso,Takehiko Maruyama, Hideki Ogura, Wakako
Kashino, Hanae Koiso, Masaya Yamaguchi, Makiro Tanaka, and Yasuharu Den. 2014. “Balanced
Corpus of Contemporary Written Japanese”.Language resources and evaluation, 48(2):345–371.
[5] Jun Izutsu, Riku Akashi, Ryo Kato, Mika Kishino, Taichiro Kobayashi, Yuta Konno, and Kanako
Komiya. 2020. “Morphological analysis of hiragana-only sentences using MeCab”. Inthe
Proceedings ofNLP2020, pages 65–68 (In Japanese).
[6] Taku Kudo, Hiroshi Ichikawa, David Talbot, and Hideto Kazawa. 2012. “Robust morphologicalanalysis for hiragana sentences on the web”. In the Proceedings of NLP 2012, pages 1272–1275 (InJapanese).
[7] Ayaha Osaki, Syouhei Karaguchi, Takuya Ohaku, Syunya Sasaki, Yoshiaki Kitagawa, Yuya
Sakaizawa, and Mamoru Komachi. 2016. “Corpus construction for Japanese morphological analysis
in twitter”. In the Proceedings of NLP 2016, pages 16–19 (In Japanese).
[8] Sanae Fujita, Hirotoshi Taira Tessei Kobayashi, and Takaaki Tanaka. 2014. “Morphological analysis
of picture book texts”.Journal of Natural Language Processing, 21(3):515–539 (In Japanese).
[9] Masato Hayashi and Takashi Yamamura. 2017. “Considerations for the addition of hiragana words
and the accuracy of morphological analysis”. In Thesis Abstract of School of Information Science andTechnology, Aich Prifectual University, pages 1–1 (In Japanese).
[10] Ji Ma, Kuzman Ganchev, and David Weiss. 2018. “State-of-the-art Chinese word segmentation with
Bi-LSTM”.In Proceedings of the 2018 Conference on Empirical Methods in Natural Language
Processing, pages4902–4908. Association for Computational Linguistics.
[11] Suphanut Thattinaphanich and Santitham Prom-on.2019. “Thai named entity recognition usingBiLSTM-CRF with word and character representation”. In 2019 4th International Conference on
Information Technology (InCIT), pages 149–154.
[12] Tolmachev, Arseny, Daisuke Kawahara, and Sadao Kurohashi. "Shrinking Japanese morphologicalanalyzers with neural networks and semi-supervised learning." Proceedings of the 2019 Conferenceof the North American Chapter of the Association for Computational Linguistics: Human Language
Technologies, Volume 1 (Long and Short Papers). 2019, pages 2744–2755[13] Chen, Xinchi, Xipeng Qiu, Chenxi Zhu, Pengfei Liu, and Xuanjing Huang. "Long short-term memoryneural Networks for Chineseword segmentation." Proceedings of the 2015 Conference on Empirical
Methods in Natural Language Processing. 2015, pages 1197-1206[14] Kikuo Maekawa, Makoto Yamazaki, Takehiko Maruyama, Masaya Yamaguchi, Hideki Ogura,
Wakako Kashino, Toshinobu Ogiso, HanaeKoiso, and Yasuharu Den. 2010. “Design, compilation,and preliminary analyses of Balanced Corpus ofContemporary Written Japanese”. In Proceedings of
the Seventh International Conference on Language Resources and Evaluation (LREC 2010),
pages1483–1486.

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