言語処理学会 第25回年次大会 発表論文集 (2019年3月)

Relation between Word Order of Languages
and the Entropy of Mitochondrial DNA Haplogroups Distributionof the Speakers’ Population
言語の語順とその話者集団のミトコンドリア DNA ハプログループ分布のエントロピーとの関係
Terumasa EHARA
江原暉将
Ehara NLP Research Laboratory
江原自然言語処理研究室
http://www.ne.jp/asahi/eharate/eharate/

1 Introduction
We are investigating the relation between theword order of languages and the speakers’
thought pattern. We have approached it
through suicide rate and homicide rate (Ehara,
2015, 2017) and diversity of Y-chromosome haplogroups of the speakers’ population (Ehara,
2018).
Y-chromosome DNA reflects paternal lineage
and we measure its diversity with the entropyof Y-chromosome haplogroups (YHg) distribution. Higher entropy value means the population is rich in diversity and it relates to peaceful
thought pattern. Lower entropy value means
the population is poor in diversity and it relates
to warlike thought pattern (Sakitani, 2008).
In this paper we use another metrics. It is theentropy of mitochondrial DNA haplogroups(MtHg) distribution of the speakers’ population(shortly “MtHg entropy”). Mitochondrial DNA
reflects maternal lineage.
Our conjecture is that head final language
speakers’ population has higher entropy valueand head initial language speakers’ population
has lower entropy value. Ehara (2018) showed
the conjecture is right for the YHg entropy.

2 DataData for the word order features are obtained
from the WALS online database (Dryer, 2013).
We use two dominant word order features:・Order of Object (O) and Verb (V),・Order of Adjective (A) and Noun (N).

WALS online provides O and V feature values
for 1519 languages and A and N feature values
for 1366 languages. Head initial languages tend
to have VO and NA word order and head final
languages tend to have OV and AN word order.
MtHg data of populations are obtained from
Eupedia’s page of “Distribution of European mitochondrial DNA (mtDNA) haplogroups by region in percentage” (Eupedia.com, 2018). From
this page, we get the MtHg data from 80 populations in Europe and its surrounding regions.
We estimate languages spoken by these populations. We can recognize 62 languages in these
data. We merge population level data to language level data using the sample size of the
population and each MtHg’s relative frequencyof the population. More concretely, the merging
method is as follows. We use the set of all MtHg :={ ,, ,0 + , , 1, 2, 2, 3, 4, 5,, , , , , ℎ }. Let
be the set of populations,be the set of languages and ℒ ∶ →be a mapping which corresponds each population to its speaking language. For each ϵ
and ϵ , the probability (relative frequency) offor : ( , ) is calculated by∑ ∈ℒ ( ) ( , ) × ( )( , )=∑ ∈ℒ ( ) ( )where ( , ) is the probability (relative frequency) offor the populationand ( ) is
the sample size of the population .
The MtHg entropy for each language : ( )― 490 ―

Copyright(C) 2019 The Association for Natural Language Processing.
All Rights Reserved.

is calculated by

55

( )=−

( , ) log(, )Azerbaijani
Adyghe (Abzakh)∈

Appendix 1 shows the base data used in the
research sorted by the MtHg entropy.

Arabic (Iraqi)
50

3 Analysis and results
We conduct t-test for O and V word ordergroups and A and N word order groups. We discard “no dominant order” data. Results areshown in Table 1. For O and V case, the meanvalue of the entropy of the OV language groupis significantly (p<1%) higher than the meanvalue of the entropy of the VO language group.
On the other hand, for A and N case, mean values are not different significantly (p>1%). This
results shows our conjecture is true by half for
MtHg case. For YHg case, Ehara (2018) showed
for both O and V case and A and N case, themean values of the entropy values are significantly different. The mean value of the YHg entropy of OV language group is higher than themean value of the YHg entropy of VO language
group. The mean value of the YHg entropy of
AN language group is higher than the meanvalue of the YHg entropy of NA language group.

PersianKurdish (Central)
Georgian
Arabic (Syrian)45

Karachay-Balkar
ChechenDargwaCatalanAvar
40

Arabic (Egyptian)
Ossetic
TurkishTatar
Greek (Modern)
Romanian
UkrainianArabic (Moroccan)
Bulgarian
Italian

35

30

NoghayChuvashHungarianRussian
Czech
Serbian-CroatianPortuguese
Latvian
SpanishMacedonian
PolishFrenchSloveneBerber (Chaouia)
Albanian
FinnishEnglishSwedishIcelandic
Lithuanian
Irish

25

Table 1: Results of t-test
for the MtHg entropy
OV
n
mean
unbiased var.
t
p

VO
17
36
0.980
0.897
0.007
0.012
2.748
0.00828ANnmean
unbiased var.
t
p

NA
38
19
0.913
0.945
0.011
0.013
-1.0700.2892

Figure 1 shows the ranking of MtHg entropy
values for each language from lower to higher.
Many OV type languages have higher entropy
values and many VO type languages have lower
entropy values.

20

15

10
UdmurtNorwegianEstonian
Mordvin (Erzya)
Danish4 Correlation between MtHg entropy
and YHg entropy
In this section, we compare the YHg entropy
and the MtHg entropy. For 42 languages in the
Appendix 1, YHg entropy can be obtained from
Ehara (2018). They are listed in Appendix 2
with MtHg entropy and YHg entropy.
Scattering graph of MtHg entropy and YHg
entropy is shown in Figure 2.

― 491 ―

Arabic (Modern
Standard)

5
BasqueBashkir

Welsh
Saami (Northern)
0

OV

VO

Figure 1: Ranking of MtHg entropy ofeach language (lower to higher)

Copyright(C) 2019 The Association for Natural Language Processing.
All Rights Reserved.

exhaustive. They are spoken in Europe and its
surrounding regions. Analysis using languages
all over the world is one of the remaining issues.

0.9
0.8

YHg entropy

0.7
0.6
0.5

References

Saami (Northern)

0.4

Matthew S. Dryer. 2013. Order of Object and Verb andOrder of Adjective and Noun, In: Dryer, Matthew S.

0.3
0.2
0.1

& Haspelmath, Martin (eds.) The World Atlas ofLanguage Structures Online. Leipzig: Max Planck

Welsh

0
0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

MtHg entropy

Figure 2: Scattering graph of MtHg entropy
and YHg entorpy for 42 languages
Correlation coefficient of MtHg entropy andYHg entropy is 0.597. Excluding the outliers(Saami (Northern) and Welsh), correlation coefficient is 0.742.
T-test results for MtHg entropy and YHg entropy by restricted 42 languages are shown in
Table 2. MtHg entropy values divided by OV
and VO are significantly different (p<5%). However, MtHg entropy values divided by AN and
NA, YHg entropy values divided by OV and VO
and YHg entropy values divided by AN and NA
are not significantly different.
Table 2: Results of t-test for the MtHg entropy and the YHg entropy(a) MtHg entropy
OV
n
mean
standard dev.
t
p

VO
14
24
0.973
0.893
0.091
0.122
2.154
0.03805ANnmean
standard dev.
t
p

NA
29
13
0.922
0.924
0.111
0.121
-0.0380.96985

Institute for Evolutionary Anthropology.
(http://wals.info/chapter/83 and 87, Accessed on
2015-3-23).
Terumasa Ehara. 2015. Relation between Word OrderParameters and Suicide / Homicide Rates, Journal of
Yamanashi Eiwa College, Vol.13, pages 9-29.
Terumasa Ehara. 2017. Relation between the Word Order Characteristics and Suicide/Homicide Rates (6),Proceedings of The 23th Annual Meeting of The Association for Natural Language Processing, P4-4,
pp.190-193.
Terumasa Ehara. 2018. Relation between Word Orderof Languages and the Entropy of Y-chromosome
Haplogroup Distribution of the Speakers’ Population,

Proceedings of The 24th Annual Meeting of The Association for Natural Language Processing, P10-8,
pp.1019-1022.
Eupedia.com. 2018. Distribution of European mitochondrial DNA (mtDNA) haplogroups by region inpercentage, Eupedia. (https://www.eupedia.com/europe/european_mtdna_haplogroups_frequency.shtml,
Accessed on 2018-12-5).
Mitsuru Sakitani. 2008. A long journey of Japanese
populations revealed by DNA analysis, Showado,
Kyoto, pages 37-39 (in Japanese).
崎谷満. 2008. DNAでたどる日本人10万年の旅, 昭和
堂, 京都, pages 37-39.

(b) YHg entropy
OV
n
mean
standard dev.
t
p

VO
14
24
0.508
0.460
0.223
0.179
0.736
0.46621ANnmean
standard dev.
t
p

NA
29
13
0.495
0.455
0.169
0.232
0.631
0.53179

5 Conclusion
Relation between word order (Object (O) /Verb (V) and Adjective (A) / Noun (N)) of languages and the entropy of Mitochondrial DNA
haplogroups distribution of the speakers’ population is examined. T-test results show OV word
order language speakers’ population tend tohave higher entropy value than VO word orderlanguage speakers’ population.
The languages used in this analysis are not― 492 ―

Copyright(C) 2019 The Association for Natural Language Processing.
All Rights Reserved.

Appendix 1 Base data used in the research
Feature values for O and V are OV, VO, NDO (No dominant order), and blank (No data).
Feature values for A and N are AN, NA, and blank (No data).
Size means a number of the sample.
L, HV, ... , Other mean Mitochondrial DNA haplogroup (MtHg) names (cell values are relative frequency of MtHg in the sample).
LanguageL
HVH
HV0+V J
T1
T2
U2
U3
U4
U5
U
K
I
W
X
Other size
entropy O-V
Saami (Northern)
0.000 0.000 0.038 0.417 0.000 0.000 0.004 0.000 0.000 0.000 0.481 0.000 0.000 0.000 0.014 0.000 0.046
499 0.4625 VO
Welsh
0.000 0.000 0.598 0.044 0.152 0.022 0.011 0.000 0.000 0.000 0.044 0.000 0.076 0.033 0.000 0.011 0.011
92 0.6102 VO
Galician
0.038 0.011 0.595 0.038 0.087 0.011 0.038 0.016 0.000 0.005 0.054 0.016 0.049 0.005 0.016 0.011 0.005
185 0.7076
Bashkir
0.000 0.000 0.251 0.024 0.068 0.003 0.023 0.026 0.000 0.168 0.084 0.000 0.027 0.006 0.008 0.000 0.317
666 0.8044 OV
Basque0.003 0.008 0.490 0.079 0.076 0.015 0.060 0.010 0.003 0.008 0.117 0.019 0.053 0.007 0.011 0.023 0.018
618 0.8082 OVDanish
0.005 0.000 0.473 0.036 0.134 0.013 0.058 0.027 0.000 0.022 0.058 0.013 0.089 0.022 0.009 0.009 0.031
224 0.8197 VO
Mordvin (Erzya)
0.000 0.010 0.422 0.049 0.078 0.020 0.078 0.069 0.000 0.020 0.157 0.020 0.000 0.059 0.000 0.000 0.020
102 0.8278 VO
Estonian
0.000 0.008 0.458 0.015 0.107 0.015 0.076 0.015 0.008 0.057 0.134 0.012 0.019 0.012 0.031 0.008 0.027
262 0.8295 VO
Norwegian0.003 0.002 0.457 0.038 0.105 0.010 0.076 0.002 0.013 0.027 0.115 0.019 0.054 0.019 0.018 0.005 0.038
628 0.8386 VO
Udmurt0.000 0.000 0.218 0.000 0.020 0.149 0.238 0.099 0.000 0.040 0.089 0.020 0.000 0.000 0.000 0.000 0.129
101 0.8461 OV
Irish
0.000 0.013 0.442 0.057 0.107 0.013 0.054 0.013 0.010 0.013 0.084 0.003 0.120 0.030 0.023 0.007 0.013
299 0.8525 VO
Lithuanian
0.000 0.009 0.453 0.050 0.064 0.032 0.070 0.000 0.012 0.038 0.114 0.012 0.015 0.032 0.021 0.009 0.070
342 0.8594 VO
Dutch
0.003 0.000 0.409 0.073 0.100 0.018 0.109 0.014 0.000 0.059 0.068 0.005 0.091 0.023 0.023 0.005 0.002
1000 0.8620 NDO
Icelandic
0.002 0.037 0.386 0.021 0.140 0.005 0.103 0.000 0.000 0.029 0.079 0.002 0.100 0.040 0.005 0.015 0.034
998 0.8686 VO
Swedish
0.006 0.005 0.458 0.050 0.077 0.027 0.041 0.008 0.006 0.030 0.121 0.028 0.064 0.028 0.013 0.013 0.025
637 0.8686 VO
English0.001 0.001 0.444 0.031 0.120 0.019 0.061 0.014 0.008 0.025 0.087 0.026 0.074 0.040 0.009 0.020 0.021
4186 0.8690 VO
Finnish0.000 0.000 0.363 0.073 0.059 0.020 0.024 0.006 0.000 0.011 0.207 0.008 0.045 0.042 0.096 0.013 0.033
971 0.8719 VO
German0.003 0.005 0.451 0.038 0.092 0.029 0.079 0.011 0.010 0.031 0.086 0.008 0.067 0.019 0.017 0.012 0.041
3089 0.8753 NDO
Albanian
0.000 0.007 0.477 0.034 0.074 0.040 0.054 0.020 0.007 0.027 0.067 0.027 0.054 0.020 0.027 0.020 0.054
149 0.8841 VO
Bosnian
0.006 0.012 0.459 0.048 0.083 0.017 0.044 0.008 0.008 0.050 0.088 0.012 0.050 0.033 0.029 0.008 0.050
521 0.8853
Berber (Chaouia)
0.250 0.013 0.316 0.076 0.045 0.037 0.015 0.006 0.003 0.003 0.021 0.066 0.034 0.001 0.003 0.013 0.098
939 0.8862 VO
Yiddish0.014 0.050 0.229 0.041 0.068 0.000 0.048 0.000 0.010 0.010 0.020 0.022 0.318 0.013 0.016 0.008 0.134
836 0.8918
Slovene0.000 0.011 0.444 0.048 0.098 0.025 0.064 0.011 0.017 0.028 0.089 0.014 0.053 0.028 0.022 0.011 0.036
358 0.8934 VO
French0.007 0.015 0.449 0.048 0.074 0.019 0.069 0.014 0.013 0.024 0.082 0.018 0.085 0.022 0.023 0.009 0.030
1663 0.8955 VO
Slovak
0.003 0.019 0.427 0.031 0.095 0.012 0.081 0.009 0.009 0.055 0.110 0.012 0.038 0.029 0.021 0.010 0.041
581 0.8988
Polish0.001 0.010 0.439 0.049 0.079 0.022 0.069 0.013 0.007 0.052 0.101 0.014 0.040 0.019 0.036 0.019 0.031
883 0.8991 VO
Macedonian
0.005 0.000 0.450 0.045 0.075 0.045 0.040 0.030 0.010 0.020 0.080 0.020 0.050 0.015 0.040 0.030 0.045
200 0.9051 VO
Spanish0.027 0.009 0.440 0.081 0.070 0.019 0.058 0.010 0.013 0.019 0.083 0.018 0.062 0.013 0.013 0.017 0.049
3266 0.9083 VO
Latvian
0.000 0.022 0.420 0.027 0.061 0.017 0.063 0.036 0.022 0.087 0.100 0.012 0.024 0.046 0.041 0.002 0.019
412 0.9178 VO
Portuguese
0.064 0.001 0.439 0.048 0.068 0.033 0.063 0.012 0.009 0.017 0.065 0.030 0.061 0.022 0.018 0.020 0.029
1448 0.9179 VO
Serbian-Croatian
0.001 0.033 0.411 0.051 0.093 0.018 0.053 0.017 0.002 0.025 0.101 0.004 0.050 0.032 0.033 0.010 0.068
838 0.9190 VO
Czech
0.003 0.017 0.402 0.029 0.100 0.040 0.077 0.011 0.017 0.023 0.097 0.006 0.034 0.029 0.011 0.020 0.083
351 0.9224 VO
Russian
0.002 0.018 0.411 0.042 0.078 0.027 0.065 0.014 0.011 0.039 0.104 0.022 0.037 0.020 0.018 0.013 0.080
1768 0.9313 VO
Hungarian
0.003 0.008 0.392 0.049 0.101 0.025 0.087 0.008 0.005 0.033 0.074 0.014 0.068 0.019 0.052 0.011 0.052
367 0.9336 VO
Chuvash0.000 0.000 0.273 0.073 0.055 0.036 0.036 0.000 0.018 0.164 0.146 0.036 0.073 0.018 0.000 0.000 0.073
55 0.9419 OV
Noghay0.004 0.038 0.207 0.008 0.023 0.012 0.050 0.038 0.046 0.015 0.031 0.046 0.019 0.027 0.038 0.042 0.353
261 0.9445 OV
Italian
0.008 0.030 0.407 0.033 0.086 0.033 0.084 0.014 0.019 0.017 0.052 0.026 0.076 0.014 0.017 0.018 0.066
3127 0.9491 VO
Belorussian
0.000 0.020 0.391 0.060 0.089 0.023 0.051 0.023 0.020 0.031 0.091 0.066 0.023 0.017 0.037 0.003 0.054
350 0.9520 NDO
Bulgarian
0.003 0.038 0.419 0.036 0.077 0.047 0.047 0.013 0.020 0.039 0.069 0.025 0.058 0.014 0.025 0.018 0.052
989 0.9530 VO
Arabic (Moroccan)
0.206 0.020 0.281 0.074 0.047 0.007 0.042 0.005 0.010 0.007 0.040 0.099 0.048 0.000 0.006 0.025 0.083
836 0.9565 VO
Ukrainian
0.003 0.035 0.384 0.043 0.081 0.029 0.084 0.017 0.009 0.058 0.098 0.006 0.049 0.017 0.026 0.009 0.058
346 0.9572 VO
Romanian
0.002 0.020 0.369 0.040 0.104 0.035 0.051 0.012 0.010 0.029 0.071 0.024 0.079 0.009 0.039 0.019 0.090
1375 0.9677 VO
Greek (Modern)
0.004 0.027 0.388 0.016 0.089 0.032 0.065 0.006 0.040 0.028 0.047 0.031 0.071 0.022 0.016 0.040 0.083
1162 0.9708 VO
Tatar
0.000 0.009 0.307 0.040 0.075 0.026 0.092 0.009 0.022 0.070 0.105 0.031 0.057 0.009 0.018 0.000 0.132
228 0.9724 OVTurkish0.013 0.048 0.309 0.007 0.089 0.028 0.043 0.013 0.037 0.019 0.030 0.063 0.056 0.015 0.019 0.019 0.192
463 0.9885 OV
Ossetic
0.007 0.030 0.262 0.013 0.161 0.043 0.023 0.036 0.033 0.000 0.069 0.056 0.023 0.013 0.023 0.030 0.180
305 0.9900 OVArabic (Egyptian)
0.218 0.041 0.157 0.004 0.088 0.053 0.061 0.006 0.033 0.010 0.016 0.029 0.045 0.020 0.010 0.006 0.206
491 0.9965 VO
Avar
0.000 0.082 0.230 0.049 0.016 0.000 0.016 0.000 0.000 0.066 0.131 0.082 0.131 0.016 0.082 0.049 0.049
61 1.0037 OV
Catalan0.025 0.000 0.300 0.075 0.075 0.013 0.075 0.013 0.025 0.038 0.100 0.038 0.100 0.013 0.050 0.025 0.025
80 1.0135 VO
Armenian (Eastern)
0.000 0.059 0.291 0.000 0.096 0.055 0.050 0.009 0.050 0.041 0.036 0.082 0.073 0.014 0.009 0.036 0.100
220 1.0207 NDO
Dargwa0.000 0.091 0.236 0.009 0.109 0.018 0.018 0.036 0.018 0.064 0.136 0.027 0.118 0.064 0.009 0.036 0.009
110 1.0255 OV
Arabic (Modern Standard) 0.114 0.030 0.251 0.019 0.094 0.035 0.041 0.007 0.034 0.007 0.018 0.059 0.067 0.012 0.011 0.035 0.165
2914 1.0288 VO
Chechen
0.000 0.011 0.258 0.007 0.032 0.018 0.125 0.029 0.061 0.039 0.104 0.029 0.050 0.057 0.032 0.014 0.136
279 1.0298 OV
Karachay-Balkar
0.000 0.020 0.224 0.004 0.024 0.041 0.049 0.041 0.069 0.012 0.029 0.110 0.045 0.016 0.073 0.041 0.211
246 1.0306 OV
Kumyk
0.000 0.036 0.241 0.009 0.080 0.018 0.098 0.027 0.063 0.063 0.054 0.027 0.027 0.054 0.018 0.009 0.179
112 1.0333
Arabic (Syrian)
0.059 0.025 0.263 0.025 0.085 0.034 0.085 0.009 0.051 0.025 0.017 0.102 0.059 0.000 0.025 0.009 0.127
118 1.0375 VO
Georgian
0.000 0.038 0.199 0.004 0.031 0.046 0.088 0.012 0.046 0.084 0.042 0.046 0.119 0.004 0.031 0.027 0.184
261 1.0407 OV
Kurdish (Central)
0.023 0.084 0.169 0.006 0.140 0.051 0.034 0.011 0.028 0.006 0.017 0.152 0.051 0.034 0.028 0.011 0.163
178 1.0467 OV
Persian
0.002 0.075 0.169 0.006 0.138 0.031 0.049 0.016 0.028 0.010 0.033 0.118 0.073 0.024 0.024 0.028 0.177
509 1.0483 OV
Arabic (Iraqi)
0.076 0.092 0.172 0.002 0.132 0.054 0.035 0.009 0.050 0.028 0.014 0.073 0.052 0.009 0.021 0.012 0.168
424 1.0647 VO
Adyghe (Abzakh)
0.000 0.043 0.256 0.000 0.046 0.036 0.062 0.036 0.072 0.020 0.085 0.030 0.072 0.023 0.053 0.049 0.121
305 1.0653 OV
Azerbaijani
0.000 0.060 0.233 0.026 0.060 0.043 0.095 0.009 0.035 0.035 0.043 0.086 0.043 0.026 0.026 0.035 0.147
116 1.0743 OV

A-N
AN
NA
AN
NA
AN
AN
AN
AN
AN
NA
AN
AN
AN
AN
AN
AN
AN
NA
NA
AN
NA
AN
AN
NA
AN
NA
AN
AN
AN
AN
AN
AN
NA
AN
AN
NA
AN
NA
AN
AN
AN
AN
NA
AN
NA
AN
AN
NA
AN
AN
NA
AN
NA
NA
NA
NA
AN

Appendix 2 MtHg entropy and YHg entropy for 42 languages
Language
MtHG_ent YHG_ent OV-VO AN-NA
Albanian
0.88410.6170 VO
NA
Arabic (Egyptian)
0.99650.5830 VO
NA
Arabic (Modern Standard)
1.02880.5710 VO
NA
Armenian (Eastern)
1.02070.7250 NOD
AN
Avar
1.00370.4730 OV
AN
Azerbaijani
1.07430.7910 OV
AN
Bashkir
0.80440.0950 OV
AN
Basque0.80820.1680 OV
NA
Belorussian
0.95200.4890 NOD
AN
Bulgarian
0.95300.6990 VO
AN
Catalan
1.01350.4250 VO
NA
Chechen1.02980.3730 OV
ANChuvash
0.94190.5720 OV
AN
Dutch
0.86200.4780 NOD
AN

Language
EnglishFinnishFrenchGeorgianGermanGreek (Modern)
Hungarian
Icelandic
Irish
Italian
Karachay-Balkar
Kurdish (Central)
LatvianOssetic

MtHG_ent YHG_ent OV-VO AN-NA
0.86900.2540 VOAN
0.87190.3690 VOAN
0.89550.4380 VO
NA
1.04070.7030 OV
AN
0.87530.4480 NOD
AN
0.97080.6990 VOAN
0.9336
0.6030 VOAN
0.86860.2800 VOAN
0.85250.0000 VONA
0.94910.5670 VO
NA
1.03060.6300 OV
AN
1.04670.6590 OV
NA
0.91780.3240 VO
AN
0.99000.5350 OV
AN― 493 ―

Language
PersianPolishRomanian
Russian
Saami (Northern)
Serbian-Croatian
SloveneSpanishSwedishTatar
TurkishUdmurtUkrainianWelsh

MtHG_ent YHG_ent OV-VO
1.04830.6800 OV
0.89910.3890 VO
0.96770.6510 VO
0.93130.5390 VO
0.46250.4630 VO
0.91900.5710 VO
0.89340.4310 VO
0.90830.4960 VO
0.86860.4700 VO
0.97240.5120 OV
0.98850.7290 OV
0.84610.1920 OV
0.9572
0.5260 VO
0.61020.0620 VO

AN-NA
NA
AN
NA
AN
AN
AN
AN
NA
AN
AN
AN
AN
AN
NA

Copyright(C) 2019 The Association for Natural Language Processing.
All Rights Reserved.