Comparison of Distributed Representations andContext Vectors for Japanese Onomatopoeia
Classification
Kanako Komiya1 Minoru Sasaki1 and Hiroyuki Shinnou1
Ibaraki University, 4-12-1 Nakanarusawa, Hitachi-shi, Ibaraki, 316-8511 JAPAN,{kanako.komiya.nlp,
minoru.sasaki.01,hiroyuki.shinnou.0828}@vc.ibaraki.ac.jp

Abstract. Japanese language has thousands of onomatopoeias and they
convey a subtle diﬀerence of the senses. [7] proposed to classify onomatopoeias depending on the context vectors. On the other hand, thedistributed representation has been studied intensively in recent years.
Therefore, we propose to classify the onomatopoeias depending on the
distributed representations instead of the context vectors. We compare
the results of the classifications and evaluate them based on the correctclassification developed by humans in this paper.

1

Introduction

Onomatopoeias are words that mimic the sound they describe and are often usedin order to expresses a feeling or a sensibility of the atmosphere they describe[7]. Japanese has thousands of onomatopoeias and they are used in daily lifeand have recently started to attract a lot of attention of researchers on natural
language processing in Japan (Section 2). There are onomatopoeia dictionaries(cf. [16] and [1]) in Japan. [16] mentions they consist of GIONGO, i.e., words that
express the sound and GITAIGO i.e., words that express the situation and aregrouped into three major types: (1) words that express the sound from outsideof humans vocal equipments, (2) words that express the sound or the voice fromhumans vocal equipments and cannot be separated into each sounds, and (3)words express the situation of things with no sound by a sensibility the soundsuggests.
Some onomatopoeias are semantically or phonologically similar each otherand the choice of these onomatopoeias sometimes gives a big diﬀerence among
Japanese sentences. For example, both NIKONIKO にこにこ and NIYANIYA
にやにや are onomatopoeias that indicate someone smiles. However NIKONIKO
indicates that it is aﬀable, and NIYANIYA indicates that it is not. On the other
hand, NIKONIKO and NIKORI(TO) にこり（と）have almost the same meaning.
Therefore, [7] proposed a clustering method to classify onomatopoeias and visualize the relationships of them depending on their contexts such as surroundingwords, their part-of-speeches and their meanings from the perspective of word
senses.

2

On the other hand, work using distributed representations, e.g., word2vec, isprevalent in the latest years. Distributed representations [11] are the vector representations of the word meanings. They are calculated based on contexts of eachword and used for comparisons of similarities of word meanings and compositionsof them. Therefore, this paper proposes to classify the onomatopoeias dependingon them instead of the context vectors, such as the surrounding words and thepart of speeches (Section 3). We conducted clustering for the onomatopoeias
on the same corpora using the same settings as [7] (Section 4) and compared
the results and evaluated them based on the correct classification developed byhumans (Section 5). We discuss the results in Section 6 and conclude the paper
in Section 7.

2

Related WorkAs we described above, onomatopoeias have recently started to attract a lotof attention of researchers on natural language processing in Japan. [12] focused on onomatopoeias have interesting sounds and rhythms and proposed an
educational system named “Onomato Planets” for children. [14] conducted atrend analysis of appearance of onomatopoeias in defect reports in English andJapanese. [8] described the way to narrow down the examples of onomatopoeias
on the Web using co-occurrence and improved the quality of the examples ofthem. There are some works on construction of onomatopoeia dictionary. [15]reported the automatic way to construct Japanese onomatopoeia dictionary using examples on the Web. [3] reported an onomatopoeia usage dictionary they
compiled based on examples.
[2] proposed an onomatopoeia dictionary. They proposed an online onomatopoeia example-based dictionary named ONOMATOPEDIA, that comprises
extensive example sentences collected from the Web, for learners of Japanese.
These papers propose a clustering algorithm for onomatopoeias, but they focusedon the onomatopoeias that have several meanings depending on the contexts.
On the other hand, this paper proposes a clustering method of onomatopoeias,
instead of each instances of them. [13] classified the onomatopoeias that express
sensibilities using SOM. [18] proposed and developed an onomatopoeia thesaurus
map. They labeled objects with onomatopoetic words on the thesaurus map
in order to visualize the similarity relationships. However they focused on thephonological features of onomatopoeias and not the context features of them.
[5] also automatically classified onomatopoeias. They collected examples fromthe Web and used co-occurrence and phonological features of onomatopoeias.
However they did not use context features such as surrounding words again.
[9] classified Japanese psychomimes, a kind of onomatopoeia, using pLSA andSMO. They used verbs which located immediately after the psychomimes as afeature. [19] analyzed the onomatopoeias from the two perspective, i.e., using
physical image and Web corpora, in order to develop a Japanese onomatopoeia
dictionary. [6] used Japanese onomatopoeias for sentiment analysis. [4],3

The closest work to ours is [7]. They proposed a clustering method of onomatopoeias based on contexts such as surrounding words, their part-of-speeches
and so on with the aim of classifying onomatopoeias and visualizing the relationships of them. They classified the onomatopoeias using the method of word
sense disambiguation to analyze them from the perspective of the word senses.
On the other hand, the distributed representations are also eﬀective for word
sense disambiguation as [17] reported. Therefore, this paper proposes to classify
the onomatopoeias depending on the distributed representations instead of thecontext vectors, compare the results of the classifications, and evaluate thembased on the correct classification developed by humans.

3

Classification of Onomatopoeia

The clustering algorithm of the onomatopoeias in this paper consists of thefollowing three steps.
1. Generate the distributed representations of each onomatopoeia from the corpus.
2. Calculate the distances among the distributed representations of the onomatopoeias.
3. Perform clustering based on the distances among onomatopoeias.
We used the Balanced Corpus of Contemporary Japanese (BCCWJ) [10]and Mecab 1 as a morphological analyzer 2 . The distributed representations
were generated using Word2Vec3 . The window size of each target word was set
to five and the size of dimensionality of the distributed representation was set
to 50. We used default settings for the other parameters. In addition, the cosine
similarities among the distributed representations were used to measure the distance among the onomatopoeias. On the other hand, [7] conducted clustering
using the Jensen-Shannon divergence of the context vector sets whose features
were bag-of-words, part-of-speeches, abstracted semantic classes, and a syntactic
feature4 . The window size of each target word was set to two.
We used single-link, bottom-up, and hierarchical clustering, followed [7].
Single-link clustering is a clustering method where the similarity between two
clusters is the similarity of two most similar members of each cluster. In otherwords, if clusters cu and cv are merged into cw = cu ∪ cv , then the similarity of
cw and another cluster ck is the maximum of the two individual similarities:
sim(cw , ck ) = max(sim(cu , ck ), sim(cv , ck ))1(1)

http://mecab.googlecode.com/svn/trunk/mecab/[7] used the same corpus but used Chasen as a morphological analyzer.
3
http://word2vec.googlecode.com/svn/trunk/4
We cannot combine the dense continuous representation and the sparse representation because the former uses a vector and the latter uses a set of vectors to represent
a word type.
2

4

Bottom-up clustering starts with a separate cluster for each onomatopoeia andthe most similar clusters are merged into a new cluster in each step of theclustering.

4

Experiment

We extracted the following four types of onomatopoeias which was chosen from[16] and classified them, followed [7]. We used the ones which appeared 10 times
or more in the corpus.
– Onomatopoeias about the sunshine– Onomatopoeias about the coldness– Onomatopoeias about the rain, the snow, and the ice– Onomatopoeias to express that someone gets upset
Accordingly, we used 12 onomatopoeias about the sunshine, which can be classified into four groups:
1. Sunny, bright: ぎらぎら GIRAGIRA, てかてか TEKATEKA, じりじり JIRIJIRI, ぽかぽか POKAPOKA, かっと KATTO, かんかん KANKAN
2. Sunshine: おっとり OTTORI, ぽっと POTTO,
3. Fair weather: からっと KARATTO, すかっと SUKATTO, からり KARARI,
4. Sundown: とっぷり TOPPURI.
GIRAGIRA and TEKATEKA represent the situation that something (cf.
the sun) shines very brightly. JIRIJIRI means that the sun shines too brightly
and sometimes it is too hard for people. POKAPOKA represents the warmth.
KATTO indicates that something burns up. KANKAN is the onomatopoeia that
represents the situation that the sun shines very brightly. The intensity of fire
was focused on as for this onomatopoeia. OTTORI represents calmness, typicallyused for nature of person or climate. POTTO means that something (cf. a light)
burst into light. KARATTO and SUKATTO are very similar each other andthey both indicate that the clear sky and the sunshine. KARARI represents theclear sky. TOPPURI indicates the sunset.
We used six onomatopoeias about the coldness, which can be classified into
three groups:
1. Chill: ぞくぞく ZOKUZOKU,
2. Coldness: じわじわ JIWAJIWA, しんと SHINTO, りんと RINTO,
3. Iciness: ひんやり HINYARI, ひやひや HIYAHIYA.
ZOKUZOKU represents the situation that someone feels a chill because offever, fear or cold air. ZIWAZIWA suggests that it get cooler and cooler. SHINTO
can be used to express the coldness but it is mainly used to express the silence.
RINTO is similar case to SHINTO, but it is mainly used to express the bravery.
HINYARI indicates the situation that someone feels cold. HIYAHIYA indicatesthat someone feels cold on one’s skin.
We used 13 onomatopoeias about the rain, the snow, and the ice, which can
be classified into three groups:

5

1. Rain: じめじめ JIMEJIME, どんより DONYORI, ぽつぽつ POTSUPOTSU,
ぱらぱら PARAPARA, ばらばら BARABARA, しょぼしょぼ SHOBOSHOBO,
しとしと SHITOSHITO, びしょびしょ BISHOBISHO, さっと SATTO, ざっと ZATTO,
2. Snow: ちらほら CHIRAHORA, はらはら HARAHARA,
3. Thunder: ごろごろ GOROGORO.
JIMAJIME and DONYORI mean the cloudy sky but it may not rain yet.
Other onomatopoeias except GOROGORO indicate that it rains now. POTSUPOTSU and PARAPARA describe the sound of the rain and suggest alight rain. BARABARA describes the sound of large drops of rain. SHOBOSHIOBO indicates the continuous rain. SHITOSHITO represents the situation thatthe rain falls silently. BISHOBISHO is an onomatopoeia that suggests something/someone is wet. SATTO indicates the rain falls in a short time and ZATTO
means the rain falls heavily in a short time. CHIRAHORA represents that thesituation the light snow falls. HARAHRA represents the situation that a little
snow or rain falls silently. GOROGORO describes the sound of the thunder.
Finally, we used 19 onomatopoeias to express that someone gets upset, which
can be classified into five groups:
1. Anger: むっと MUTTO, むしゃくしゃ MUSHAKUSHA, かっと KATTO, ぐ
らぐら GURAGURA, むかむか MUKAMUKA, ぷりぷり PURIPURI, かんかん KANKAN, がみがみ GAMIGAMI, ぷんと PUNTO, ぷんぷん PUNPUN,
ぞっと ZOTTO
2. Frustration: ぐつぐつ GUTSUGUTSU, かちん KACHIN
3. Bad mood: ぶすっと BUSUTTO, つんと TSUNTO, つんけん TSUNKEN,
4. Unfriendliness: むっつり MUTTSURI, つっけんどん TUKKENDON
5. Hardening of attitudes: きっと KITTO.
MUTTO suggests that someone gets upset with the other person’s speechor behavior. MUSHAKUSHA suggests that someone loses one’s composure because of anger, and KATTO indicates that something burns up and it can beused for person like “That burns me up”. GURAGURA indicates the rage andthis onomatopoeia can be used to describe the sound of a boiling. MUKAMUKA suggests the sudden anger. PURIPURI represents someone gets ratty
and KANKAN represents something burns intensely and it can be used for people as well. GAMIGAMI is an onomatopoeia that describes someone berates.
PUNTO represents the situation where someone gets sulky. PUNPUN meansthat someone is in a fume. GUTSUGUTSU represents that someone gets a flashof anger about something. KACHIN suggests that someone gets upset with what
they are said. BUSUTTO describes the sulky face, TSUNTO represents the badtemper, and TUKKENDON represents the unfriendly attitude. MUTTSURI
suggests the glum mood. TSUNTSUN indicates that someone is cranky. KITTO
represents the grim look.
These types are manually classified according to the description in Dictionaryof Japanese Onomatopoeias [16]. In addition, we followed [7] and used the onomatopoeias with the suﬃx TO like KATTO and KARATTO for the distributed

6Type of onomatopoeia Dictionary Final exp.
Sunshine
17
12
Coldness
17
6
Rain, snow, ice
28
13
To get upset
40
19
Table 1. The number of onomatopoeias in the dictionary and in the final experimentType of onomatopoeia Min Max Avg.
Sunshine
17 228 80.33
Coldness
34 640 235.50
Rain, snow, ice
12 1,154 225.62
To get upset
15 5,812 384.05
Table 2. The minimum, maximum, and average number of instances of onomatopoeias
in the final experiment using distributed representation

representations except for RINTO. We used RIN, without the suﬃx TO, because
the new morphological analyzer did not extract RINTO but extracted RIN.
Table 1 shows the number of the types of onomatopoeias in the dictionary
and in the final experiment. The maximum, minimum, and average number ofthe instances of onomatopoeias in the final experiment is summarized in Table 2.
These numbers are slightly diﬀerent from those in [7] (shown in Table 3) because
we used a new morphological analyzer.

5

Results

Figures 1 and 2 in appendix show the results of the hierarchical clustering of
onomatopoeias about the sunshine based on the distributed representations andthe context vectors, respectively. Figures 3 and 4 in appendix show the results of
those about the coldness. Figures 5 and 6 in appendix show the results of thoseabout the rain, the snow, and the ice. Figures 7 and 8 in appendix show theresults of those to express that someone gets upset. We evaluated the entropy

Type of onomatopoeia Min Max Ave
Sunshine
12 228 79.00
Coldness
24 243 139.83
Rain, snow, ice
13 1,175 230.46
To get upset
15 5,894 390.58
Table 3. The minimum, maximum, and average number of instances of onomatopoeias
in the final experiment using context vectors

7
Onomatopoeia

Entoropy
PurityDistributed rep. Context vec. Distributed rep. Context vec.
Sunshine
0.62
0.54
0.67
0.67
Coldness
0.63
0.71
0.67
0.50
Rain, snow, ice
0.56
0.54
0.79
0.79
To get upset
0.47
0.50
0.74
0.68
Table 4. Entropies and Purities of Clusters by Distributed Representations and Context Vectors

and purity based on the manually labeled correct answers, which are the groups
in Section 4. Table 4 shows the results. The better results are written in bold.
The cluster numbers used for the evaluation of the entropy and purity are also
shown in the figures in the appendix.

6

Discussion

According to Figures 1 and 2, these two results of clustering bear little resemblance to each other. However, the most similar onomatopoeias are the samein Figures 3 and 4 (ZOKUZOKU and HINYARI)，in Figures 5 and 6 (JIMEJIME and DONYORI)，and in Figures 7 and 8 (ZOTTO and MUTTO). In
addition, when Figures 3 and 4 are compared, they are almost the same but
one exception: HIYAHIYA is grouped with SHINTO first or it is integrated into
the group of ZOKUZOKU, HINYARI, and JIWAJIWA first. Moreover, the only
diﬀerence between Figures 5 and 6 is that ZATTO, SATTO, BARABARA, and
BISHOBISHO are grouped into the first and the largest group or not, and thediﬀerence between Figures 7 and 8 is KACHIN, KANKAN, and GUSTUGSTU.
The part-of-speeches, abstracted semantic classes, and a syntactic feature were
not used for the distributed representations, and the window size of each target
word for the context vectors was set to two whereas that for the distributed representations was set to five. We think that the common feature, i.e., bag-of-words
adjacent to the onomatopoeias greatly contributed the results of clustering, because the results are sometimes similar each other despite these diﬀerences.
In addition, Table 4 shows that the entropies of the clusters developed using
the context vectors slightly outperformed those using the distributed representations when the onomatopoeias about the sunshine or those about the rain, thesnow, and the ice are compared, but those using the distributed representations
slightly outperformed those using the context vectors when the onomatopoeias
about the coldness or those to express that someone gets upset are compared.
Moreover, Table 4 shows that the purity of the clusters developed using the distributed representations slightly outperformed those using the context vectors
when the onomatopoeias about the coldness or those to express that someone
gets upset are compared, and there was no diﬀerence between the context vectors
and the distributed representations when the onomatopoeias about the sunshineor those about the rain, the snow, and the ice are compared. Therefore, we can8see that the clustering based on the distributed representations classified theonomatopoeias more similarly to manually classified onomatopoeias in general.
In addition, some onomatopoeias have more than one sense. For example,
KATTO and KANKAN are included in both onomatopoeias about the sunshine
and onomatopoeias to express that someone gets upset. We could not use theword sense tagged data of onomatopoeia and did not consider them like [7].
Therefore, the word sense disambiguation of the onomatopoeias is our future
work.

7

Conclusion

This paper proposed to conduct clustering for Japanese onomatopoeias based on
the distributed representations and compared the results to the method where
the clustering was conducted based on the context vectors, which was proposedin [7]. The single link and hierarchal clustering was employed as the clustering
method and the cosine similarities were used to measure the distance between the
onomatopoeias. The following four types of onomatopoeias were classified: (1)Onomatopoeias about the sunshine, (2) onomatopoeias about the coldness, (3)and onomatopoeias about the rain, the snow, and the ice, and (4) onomatopoeias
to express that someone gets upset. Although the features for clustering were
diﬀerent, the most similar onomatopoeias were the same when the distributed
representation were used and when the context vectors were used, for the three
type of the onomatopoeias, which are those about the coldness, those about
the rain, the snow, and the ice, and those to express that someone gets upset.
In addition, the experiments revealed that the distributed representation wasslightly better than the context vectors in general when the entropies and thepurities of the clusters were evaluated based on the manually labeled data. The
problems to analyze the onomatopoeias, e.g., the suﬃx of them, remain because
it is diﬃcult for morphological analyzer to correctly delimit and extract the
onomatopoeias. The word sense disambiguation of the onomatopoeias should
also be considered in the future.

Acknowledgement
This work was partially supported by some grants, which will be discribed later.

References
1. Amanuma, Y.: Giongo, Gitaigo Jiten. Tokyodo Shuppan publisher (In Japanese)(1993)
2. Asaga, C., Mukarramah, Y., Watanabe, C.: Clustering technique with considerataion of modification relation for classifying sentences by meaning of onomatopoeia on online onomatopoeia example-based dictionary onomatopedia. In:
Proceedings of DEWS2008 (In Japanese). pp. A3–4 (2008)

9
3. Furutake, Y., Sato, S.: Yorei ni motodsuku onomatope youhou jiten no hensan. In:
Proceedings of NLP 2010 (In Japanese). pp. 994–997 (2010)
4. Hashimoto, K., Takeuchi, K.: A method to estimate subjective context-sensitive
kansei polarities of japanese onomatopoeic expressions. In: Proceedings of the 25th
Annual Conference of the Japanese Society for Artificial Intelligence (In Japanese).
pp. 1C2–OS4b–6 (2011)
5. Ichioka, K., Fukumoto, F.: Graph-based clustering for semantic classification of
onomatopoetic words. In: Proceedings of the 3rd Textgraphs Workshop on GraphBased Algorithms for Natural Language Processing. pp. 33–40 (2008)
6. Igarashi, T., Sasano, R., Takamura, H., , Okumur, M.: Use of sound symbolism
in sentiment classification. Journal of Natural Language Processing Vol.20(No.2),
183–200 (2013)
7. Komiya, K., Kotani, Y.: Classification of japanese onomatopoeias using hierarchicalclustering depending on contexts. In: Proceedings of the 2011 English International
Joint Conference on Computer Science and Software Engineering. pp. 108–1013(2011)
8. Kurosawa, Y., Mera, K., Takezawa, T.: Ziko shoshikika mappu som niyoru shinzyo
wo arawasu onomatope bunrui no saikentou. In: Proceedings of NLP 2010 (InJapanese). pp. 1058–1061 (2010)
9. Kurosawa, Y., Takezawa, T.: Psychomime classification by using self-organizing
map and probabilistic latent semantic analysis (in japanese). In: Proceedings ofthe 25th Annual Conference of the Japanese Society for Artificial Intelligence. pp.
1C2–OS4b–7 (2011)
10. Maekawa, K.: Balanced corpus of contemporary written japanese. In: Proceedingsof the 6th Workshop on Asian Language Resources (ALR). pp. 101–102 (2008)
11. Mikolov, T., tau Yih, W., Zweig, G.: Linguistic regularities in continuous space
word representations. In: Proceedings of NAACL 2013. pp. 746–751 (2013)
12. Miyazaki, A., Tomimatsu, K.: Onomato planets: Physical computing of japanese
onomatopoeia. In: Proceedings of the 3rd International Conference on Tangible
and Embedded Interaction. pp. 301–304 (2009)
13. Morita, K., Suzuki, Y.: Onomato planets: Physical computing of japanese onomatopoeia. In: Proceedings of NLP 2010 (In Japanese). pp. 924–927 (2010)
14. Nasukawa, T., Unno, Y., Murakami, A.: Kikino fuguai wo kijutsu shita niongo toeigo no kopasu ni okeru onomatope. In: Proceedings of NLP 2010 (In Japanese).
pp. 154–158 (2010)
15. Okumura, M., Okumura, A., Saito, S.: Automatic construction of a japanese onomatopoeic dictionary using text data on the www. In: Proceedings of the 11th
International Conference on Applications of Natural Language to Information Systems. pp. 209–215 (2006)
16. Ono, M.: Nihongo Onomatope Jiten (Dictionary of Japanese Onomatopoeias).
Shogakukan publisher (In Japanese) (2007)
17. Sugawara, H., Takamura, H., Sasano, R., Okumura, M.: Context representation
with word embeddings for wsd. In: Proceedings of PACLING 2015 (2015)
18. Tomoto, Y., Nakamura, T., Kanoh, M., Komatsu, T.: Visualization of similarity
relationships by onomatopoeia thesaurus map. In: Proceedings of the IEEE World
Congress on Computational Intelligence. pp. 3304–3309 (2010)
19. Uno, R., Kaji, N., Ogai, Y., Ikegami, T., Kitsuregawa, M.: Semantic stability andsubjcectivity in mimetics. In: Proceedings of the 25th Annual Conference of theJapanese Society for Artificial Intelligence (In Japanese). pp. 1C2–OS4b–8 (2011)

10
TEKATEKA(1)POKAPOKA(1)POTTO(1)KARATTO(1)GIRAGIRA(1)KATTO(1)
KARARI(1)JIRIJIRI(1)TOPPURI(1)KANKAN(2)SUKATTO(3)OTTORI(4)Fig. 1. Hierarchical clustering of onomatopoeias about the sunshine using distributed
representations
GIRAGIRA(1)TEKATEKA(1)OTTORI(1)JIRIRJIRI(1)POKAPOKA(1)
KATTO(1)KARATTO(1)SUKATTO(1)POTTO(1)TOPPURI(2)KANKAN(3)KARARI(4)Fig. 2. Hierarchical clustering of onomatopoeias about the sunshine using context vectors
ZOKUZOKU(1)HINYARI(1)JIWAJIWA(1)HIYAHIYA(1)SHINTO(2)RIN(3)

Fig. 3. Hierarchical clustering of onomatopoeias about the coldness using distributed
representations
ZOKUZOKU(1)HINYARI(1)JIWAJIWA(1)HIYAHIYA(3)SHINTO(3)RONTO(2)Fig. 4. Hierarchical clustering of onomatopoeias about the coldness using context vectors

11

JIMEJIME(1)DONYORI(1)POTSUPOTSU(1)CHIRAHORA(1)PARAPARA(1)GOROGORO(1)HARAHARA(1)SHOBOSHOBO(1)SHITOSHITO(1)BISHOBISHO(1)BARABARA(1)SATTO(2)ZATTO(3)Fig. 5. Hierarchical clustering of onomatopoeias about the rain, the snow, and the ice
using distributed representations

JIMEJIME(1)DONYORI(1)HARAHARA(1)GOROGORO(1)PARAPARA(1)POTSUPOTSU(1)SHOBOSHOBO(1)CHIRAHORA(1)ZATTO(1)SATTO(1)BARABARA(3)BISHOBISHO(3)SHITOSHITO(2)Fig. 6. Hierarchical clustering of onomatopoeias about the rain, the snow, and the ice
using context vectors

12

ZOTTO(1)
MUTTO(1)
MUKAMUKA(1)TSUNTO(1)GURAGURA(1)MUTTSURI(1)BUSUTTO(1)MUSHAKUSHA(1)PURIPURI(1)TSUNTSUN(1)KATTO(1)PUNPUN(1)PUNTO(1)
KACHIN(1)KANKAN(1)GUTSUGUTSU(5)TSUKKENDON(4)GAMIGAMI(3)KITTO(2)Fig. 7. Hierarchical clustering of onomatopoeias to express that someone gets upset
using distributed representations

ZOTTO(1)
MUTTO(1)
BUSUTTO(1)MUSHAKUSHA(1)KATTO(1)GURAGURA(1)MUKAMUKA(1)TSUNTO(1)PURIPURI(1)PUNPUN(1)MUTTSURI(1)TSUNTSUN(1)GUTSUGUTSU(1)PUNTO(1)
KANKAN(5)TUKKENDON(5)KITTO(4)
KACHIN(3)GAMIGAMI(2)Fig. 8. Hierarchical clustering of onomatopoeias to express that someone gets upset
using context vectors