Modelling Chinese dialect evolution Johann-Mattis List, Shijulal Nelson-Sathi, Dagan Tal
To cite this version:
Johann-Mattis List, Shijulal Nelson-Sathi, Dagan Tal. Modelling Chinese dialect evolution. Beyond Phylogeny: Quantitative diachronic explanations of language diversity (SLE Workshop), Aug 2012, Stockholm, Sweden. hprints-00758549
HAL Id: hprints-00758549 https://hal-hprints.archives-ouvertes.fr/hprints-00758549 Submitted on 28 Nov 2012
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Johann-Mattis List∗, Shijulal Nelson-Sathi+, and Tal Dagan+
∗Institute for Romance Languages and Literature +Institute for Genomic Microbiology Heinrich Heine University Düsseldorf
2012/08/31
1 / 30 Structure of the Talk
. . .1 Languages Languages Diasystems Change . . .2 Modelling Language History Trees Waves Networks . . .3 Modelling Chinese Dialect History Data Analysis Results
2 / 30 Languages
语言 язык
Languages
language språk
1 13 / 30 Languages Languages Languages and Dialects
Norwegian, Danish, and Swedish are different languages. . . Beijing-Chinese, Shanghai-Chinese, and Hakka-Chinese are dialects of the same Chinese language.
4 / 30 Languages Languages Languages and Dialects
Beijing Chinese 1 iou²¹ i⁵⁵ xuei³⁵ pei²¹fəŋ⁵⁵ kən⁵⁵ tʰai⁵¹iaŋ¹¹ t͡ʂəŋ⁵⁵ ʦai⁵³ naɚ⁵¹ t͡ʂəŋ⁵⁵luən⁵¹ Hakka Chinese 1 iu³³ it⁵⁵ pai³³a¹¹ pet³³fuŋ³³ tʰuŋ¹¹ ɲit¹¹tʰeu¹¹ hɔk³³ e⁵³ au⁵⁵ Shanghai Chinese 1 ɦi²² tʰɑ̃⁵⁵ ʦɿ²¹ poʔ³foŋ⁴⁴ taʔ⁵ tʰa³³ɦiã⁴⁴ ʦəŋ³³ hɔ⁴⁴ ləʔ¹lə²³ʦa⁵³
Beijing Chinese 2 ʂei³⁵ də⁵⁵ pən³⁵ liŋ²¹ ta⁵¹ Hakka Chinese 2 man³³ ɲin¹¹ kʷɔ⁵⁵ vɔi⁵³ Shanghai Chinese 2 sa³³ ɲiŋ⁵⁵ ɦəʔ²¹ pəŋ³³ zɿ⁴⁴ du¹³
Norwegian 1 nuːɾɑʋinˑn̩ ɔ suːln̩ kɾɑŋlət ɔm Swedish 1 nuːɖanvɪndən ɔ suːlən tv̥ɪstadə ən gɔŋ ɔm Danish 1 noʌ̯ʌnvenˀn̩ ʌ soːl̩ˀn kʰʌm eŋg̊ɑŋ i sd̥ʁiðˀ ʌmˀ
Norwegian 2 ʋem ɑ dem sɱ̩ ʋɑː ɖɳ̩ stæɾ̥kəstə Swedish 2 vɛm ɑv dɔm sɔm vɑ staɹkast Danish 2 vɛmˀ a b̥m̩ d̥ vɑ d̥n̩ sd̥æʌ̯g̊əsd̥ə
5 / 30 Languages Languages Languages and Dialects
From the perspective of the lexicon and the sound system, the Chinese dialects are at least equally if not more different than the Scandinavian languages.
5 / 30 A linguistic diasystem requires a “roof language” (Goossens 1973:11), i.e. a linguistic variety that serves as a standard for interdialectal communication.
Languages Diasystems Language as a Diasystem
Languages are complex aggregates of different linguistic systems that ‘coexist and influence each other’ (Coseriu 1973: 40, my translation). . .
6 / 30 Languages Diasystems Language as a Diasystem
Languages are complex aggregates of different linguistic systems that ‘coexist and influence each other’ (Coseriu 1973: 40, my translation). . . A linguistic diasystem requires a “roof language” (Goossens 1973:11), i.e. a linguistic variety that serves as a standard for interdialectal communication.
6 / 30 Languages Diasystems Language as a Diasystem
Standard Language
Diatopic Varieties
Diastratic Varieties
Diaphasic Varieties
7 / 30 expected Mandarin [ma₅₅po₂₁lou]
attested Mandarin [wan₅₁paw₂₁lu₅₁]
explanation Cantonese [maːn₂₂pow₃₅low₃₂]
Languages Change Change
8 / 30 attested Mandarin [wan₅₁paw₂₁lu₅₁]
explanation Cantonese [maːn₂₂pow₃₅low₃₂]
Languages Change Change
expected Mandarin [ma₅₅po₂₁lou]
8 / 30 explanation Cantonese [maːn₂₂pow₃₅low₃₂]
Languages Change Change
expected Mandarin [ma₅₅po₂₁lou]
attested Mandarin [wan₅₁paw₂₁lu₅₁]
8 / 30 Languages Change Change
expected Mandarin [ma₅₅po₂₁lou]
attested Mandarin [wan₅₁paw₂₁lu₅₁]
explanation Cantonese [maːn₂₂pow₃₅low₃₂]
8 / 30 Languages Change Change
English Cantonese Mandarin
maːlboʁo maːn22pow35low32 wan51paw21lu51
“Road of 1000 Tre- “Road of 1000 Tre- Proper Name asures” asures”
万宝路
9 / 30
1 Modelling Language History
Modelling Language History
10 / 30 Modelling Language History Trees Dendrophilia
August Schleicher (1821-1868) 11 / 30 Modelling Language History Trees Dendrophilia
These assumptions that logically follow from the results of our re- search can be best illustrated with help of a branching tree. (Schle- icher 1853: 787, my translation)
August Schleicher (1821-1868) 11 / 30 Modelling Language History Trees Dendrophilia
Schleicher (1853)
12 / 30 No matter how we look at it, as long as we stick to the assumption that today’s languages originated from their common proto-language via multiple furcation, we will never be able to explain all facts in a scientifi- cally adequate way. (Schmidt 1872: 17, my translation)
Modelling Language History Waves Dendrophobia
Johannes Schmidt (1843-1901) 13 / 30 Modelling Language History Waves Dendrophobia
No matter how we look at it, as long as we stick to the assumption that today’s languages originated from their common proto-language via multiple furcation, we will never be able to explain all facts in a scientifi- cally adequate way. (Schmidt 1872: 17, my translation)
Johannes Schmidt (1843-1901) 13 / 30 Modelling Language History Waves Dendrophobia
I want to replace [the tree] by the im- age of a wave that spreads out from the center in concentric circles be- coming weaker and weaker the far- ther they get away from the center. (Schmidt 1872: 27, my translation)
Johannes Schmidt (1843-1901) 14 / 30 Modelling Language History Waves Dendrophobia
Schmidt (1875)
15 / 30 Modelling Language History Waves Dendrophobia
Meillet (1908) Bloomfield (1933)
Hirt (1905) Bonfante (1931)
16 / 30 Waves are bad because they are difficult to nobody knows how to reconstruct...... reconstruct them languages do not separate in languages still separate, even split processes if not in split processes they are boring, since they they are boring, since they only capture certain aspects of only capture certain aspects of language history, namely the language history, namely, the vertical relations horizontal relations
Modelling Language History Networks Phylogenetic Networks
Trees are bad because
17 / 30 Waves are bad because nobody knows how to reconstruct them languages do not separate in languages still separate, even split processes if not in split processes they are boring, since they they are boring, since they only capture certain aspects of only capture certain aspects of language history, namely the language history, namely, the vertical relations horizontal relations
Modelling Language History Networks Phylogenetic Networks
Trees are bad because they are difficult to reconstruct......
17 / 30 Waves are bad because nobody knows how to reconstruct them languages still separate, even if not in split processes they are boring, since they they are boring, since they only capture certain aspects of only capture certain aspects of language history, namely the language history, namely, the vertical relations horizontal relations
Modelling Language History Networks Phylogenetic Networks
Trees are bad because they are difficult to reconstruct...... languages do not separate in split processes
17 / 30 Waves are bad because nobody knows how to reconstruct them languages still separate, even if not in split processes they are boring, since they only capture certain aspects of language history, namely, the horizontal relations
Modelling Language History Networks Phylogenetic Networks
Trees are bad because they are difficult to reconstruct...... languages do not separate in split processes they are boring, since they only capture certain aspects of language history, namely the vertical relations
17 / 30 languages still separate, even if not in split processes they are boring, since they only capture certain aspects of language history, namely, the horizontal relations
Modelling Language History Networks Phylogenetic Networks
Trees are bad because Waves are bad because they are difficult to nobody knows how to reconstruct...... reconstruct them languages do not separate in split processes they are boring, since they only capture certain aspects of language history, namely the vertical relations
17 / 30 they are boring, since they only capture certain aspects of language history, namely, the horizontal relations
Modelling Language History Networks Phylogenetic Networks
Trees are bad because Waves are bad because they are difficult to nobody knows how to reconstruct...... reconstruct them languages do not separate in languages still separate, even split processes if not in split processes they are boring, since they only capture certain aspects of language history, namely the vertical relations
17 / 30 Modelling Language History Networks Phylogenetic Networks
Trees are bad because Waves are bad because they are difficult to nobody knows how to reconstruct...... reconstruct them languages do not separate in languages still separate, even split processes if not in split processes they are boring, since they they are boring, since they only capture certain aspects of only capture certain aspects of language history, namely the language history, namely, the vertical relations horizontal relations
17 / 30 We connect the branches and twigs of the tree with countless horizon- tal lines and it ceases to be a tree (Schuchardt 1870 [1900]: 11)
Modelling Language History Networks Phylogenetic Networks
Hugo Schuchardt (1842-1927) 18 / 30 Modelling Language History Networks Phylogenetic Networks
We connect the branches and twigs of the tree with countless horizon- tal lines and it ceases to be a tree (Schuchardt 1870 [1900]: 11)
Hugo Schuchardt (1842-1927) 18 / 30 Modelling Language History Networks Phylogenetic Networks
19 / 30 Modelling Chinese Dialect History
魚 魚 魚 ?
Modelling Chinese Dialect History 首 首1 1首 1首
20 / 30
1 1 1 1 The data for this study was taken from the Xiàndài Hànyǔ Fāngyán Yīnkù (Hou 2004). It consists of 180 items (“meanings”) translated into 40 contemporary Chinese dialects. The data is available on a CD in RTF format along with recordings for all dialect entries. For this study, the transcriptions in RTF were converted to Unicode. Every word was compared with the recordings in order to minimize errors resulting from the extraction process and the original encoding itself.
Modelling Chinese Dialect History Data Data
21 / 30 It consists of 180 items (“meanings”) translated into 40 contemporary Chinese dialects. The data is available on a CD in RTF format along with recordings for all dialect entries. For this study, the transcriptions in RTF were converted to Unicode. Every word was compared with the recordings in order to minimize errors resulting from the extraction process and the original encoding itself.
Modelling Chinese Dialect History Data Data
The data for this study was taken from the Xiàndài Hànyǔ Fāngyán Yīnkù (Hou 2004).
21 / 30 The data is available on a CD in RTF format along with recordings for all dialect entries. For this study, the transcriptions in RTF were converted to Unicode. Every word was compared with the recordings in order to minimize errors resulting from the extraction process and the original encoding itself.
Modelling Chinese Dialect History Data Data
The data for this study was taken from the Xiàndài Hànyǔ Fāngyán Yīnkù (Hou 2004). It consists of 180 items (“meanings”) translated into 40 contemporary Chinese dialects.
21 / 30 For this study, the transcriptions in RTF were converted to Unicode. Every word was compared with the recordings in order to minimize errors resulting from the extraction process and the original encoding itself.
Modelling Chinese Dialect History Data Data
The data for this study was taken from the Xiàndài Hànyǔ Fāngyán Yīnkù (Hou 2004). It consists of 180 items (“meanings”) translated into 40 contemporary Chinese dialects. The data is available on a CD in RTF format along with recordings for all dialect entries.
21 / 30 Every word was compared with the recordings in order to minimize errors resulting from the extraction process and the original encoding itself.
Modelling Chinese Dialect History Data Data
The data for this study was taken from the Xiàndài Hànyǔ Fāngyán Yīnkù (Hou 2004). It consists of 180 items (“meanings”) translated into 40 contemporary Chinese dialects. The data is available on a CD in RTF format along with recordings for all dialect entries. For this study, the transcriptions in RTF were converted to Unicode.
21 / 30 Modelling Chinese Dialect History Data Data
The data for this study was taken from the Xiàndài Hànyǔ Fāngyán Yīnkù (Hou 2004). It consists of 180 items (“meanings”) translated into 40 contemporary Chinese dialects. The data is available on a CD in RTF format along with recordings for all dialect entries. For this study, the transcriptions in RTF were converted to Unicode. Every word was compared with the recordings in order to minimize errors resulting from the extraction process and the original encoding itself.
21 / 30 Modelling Chinese Dialect History Data Data
ITEM 太阳 tàiyáng “sun” . Dialect Pronunciation Characters Cognacy Shanghai tʰa³⁴⁻³³ɦiã¹³⁻⁴⁴ 太阳 1 Shanghai ȵjɪʔ¹⁻¹¹dɤ¹³⁻²³ 日头 2 Wenzhou tʰa⁴²⁻²²ji 太阳 1 Wenzhou ȵi²¹³⁻²²dɤu 日头 2 Guangzhou jit²tʰɐu²¹⁻³⁵ 热头 3 Guangzhou tʰai³³jœŋ²¹ 太阳 1 Haikou zit³hau³¹ 日头 2 Beijing tʰai⁵¹iɑŋ¹ 太阳 1
22 / 30 Modelling Chinese Dialect History Data Dialect Locations in the Xiàndài Hànyǔ Fāngyán Yīnkù
01 ShanghaiShanghai 上海 Data0202 SuzhouSuzhou 苏州 0303 HangzhouHangzhou 杭州 0404 WenzhouWenzhou 温州 0505 GuangzhouGuangzhou 广州 0606 NanningNanning 南宁 0707 XianggangXianggang 香港 0808 XiamenXiamen 厦门 0909 FuzhouFuzhou 福州 1010 Jian'ouJian'ou 建瓯 11 Shantou 汕头 1212 HaikouHaikou 海口 35 1313 TaibeiTaibei 台北 1414 Meixian 梅县 1515 TaoyuanTaoyuan 桃园 40 1616 NanchangNanchang 南昌 1717 Changsha 长沙 18 Xiangtan 18 Xiangtan 湘潭 23 19 Shexian 19 Shexian 歙县 24 2020 Tunxi 屯溪 21 37 25 21 TaiyuanTaiyuan 太原 21 22 Pingyao 22 Pingyao 平遥 22 2323 Huhehaote 呼和浩特 39 26 38 27 2424 BeijingBeijing 北京 2525 Tianjin 天津 30 Tianjin 36 2626 Jinan 济南 2727 QingdaoQingdao 青岛 28 01 2828 Nanjing 南京 29 02 2929 HefeiHefei 合肥 32 31 03 3030 ZhengzhouZhengzhou 郑州 19 17 20 31 WuhanWuhan 武汉 16 04 3232 ChengduChengdu 成都 10 18 3333 GuiyangGuiyang 贵阳 33 11 09 3434 Kunming 昆明 Kunming 34 13 Guanhua 3535 Haerbin 哈尔滨 14 08 Jin Kejia 3636 Xi'anXi'an 西安 05 15 06 Yue 37 Yinchuan 37 Yinchuan 银川 07 Gan 3838 LanzhouLanzhou 兰州 Hui Min 39 39 Xining 西宁 12 Xiang 4040 WulumuqiWulumuqi 乌鲁木齐 Wu
23 / 30 The data was analyzed with help of Dagan and Martin’s (2008) method for phylogenetic network reconstruction, that was applied to linguistic data before (Nelson-Sathi et al. 2011). Given a binary reference tree reflecting the vertical history of a language family and a list of homologs (“cognates”) distributed over the languages, the method reconstructs horizontal relations between the languages and the internal nodes of the tree. The reconstruction of horizontal relations is done by seeking specific evolutionary models (loss and gain of characters) that fit the given distribution best. The main criterion by which the fitness of the distributions is evaluated is the “vocabulary size”, i.e. the distribution of word forms over a set of meanings. Comparing the vocabulary sizes of different models that infer different amounts of lateral events, the model that comes closest to the vocabulary sizes of the contemporary languages is chosen.
Modelling Chinese Dialect History Analysis Analysis
24 / 30 Given a binary reference tree reflecting the vertical history of a language family and a list of homologs (“cognates”) distributed over the languages, the method reconstructs horizontal relations between the languages and the internal nodes of the tree. The reconstruction of horizontal relations is done by seeking specific evolutionary models (loss and gain of characters) that fit the given distribution best. The main criterion by which the fitness of the distributions is evaluated is the “vocabulary size”, i.e. the distribution of word forms over a set of meanings. Comparing the vocabulary sizes of different models that infer different amounts of lateral events, the model that comes closest to the vocabulary sizes of the contemporary languages is chosen.
Modelling Chinese Dialect History Analysis Analysis
The data was analyzed with help of Dagan and Martin’s (2008) method for phylogenetic network reconstruction, that was applied to linguistic data before (Nelson-Sathi et al. 2011).
24 / 30 The reconstruction of horizontal relations is done by seeking specific evolutionary models (loss and gain of characters) that fit the given distribution best. The main criterion by which the fitness of the distributions is evaluated is the “vocabulary size”, i.e. the distribution of word forms over a set of meanings. Comparing the vocabulary sizes of different models that infer different amounts of lateral events, the model that comes closest to the vocabulary sizes of the contemporary languages is chosen.
Modelling Chinese Dialect History Analysis Analysis
The data was analyzed with help of Dagan and Martin’s (2008) method for phylogenetic network reconstruction, that was applied to linguistic data before (Nelson-Sathi et al. 2011). Given a binary reference tree reflecting the vertical history of a language family and a list of homologs (“cognates”) distributed over the languages, the method reconstructs horizontal relations between the languages and the internal nodes of the tree.
24 / 30 The main criterion by which the fitness of the distributions is evaluated is the “vocabulary size”, i.e. the distribution of word forms over a set of meanings. Comparing the vocabulary sizes of different models that infer different amounts of lateral events, the model that comes closest to the vocabulary sizes of the contemporary languages is chosen.
Modelling Chinese Dialect History Analysis Analysis
The data was analyzed with help of Dagan and Martin’s (2008) method for phylogenetic network reconstruction, that was applied to linguistic data before (Nelson-Sathi et al. 2011). Given a binary reference tree reflecting the vertical history of a language family and a list of homologs (“cognates”) distributed over the languages, the method reconstructs horizontal relations between the languages and the internal nodes of the tree. The reconstruction of horizontal relations is done by seeking specific evolutionary models (loss and gain of characters) that fit the given distribution best.
24 / 30 Modelling Chinese Dialect History Analysis Analysis
The data was analyzed with help of Dagan and Martin’s (2008) method for phylogenetic network reconstruction, that was applied to linguistic data before (Nelson-Sathi et al. 2011). Given a binary reference tree reflecting the vertical history of a language family and a list of homologs (“cognates”) distributed over the languages, the method reconstructs horizontal relations between the languages and the internal nodes of the tree. The reconstruction of horizontal relations is done by seeking specific evolutionary models (loss and gain of characters) that fit the given distribution best. The main criterion by which the fitness of the distributions is evaluated is the “vocabulary size”, i.e. the distribution of word forms over a set of meanings. Comparing the vocabulary sizes of different models that infer different amounts of lateral events, the model that comes closest to the vocabulary sizes of the contemporary languages is chosen. 24 / 30 Modelling Chinese Dialect History Analysis Analysis
Shanghai Wenzhou Suzhou Tianjin Hangzhou
Jinan Shexian
Qingdao Tunxi
Beijing Nanchang
Harbin Taoyuan
Zhengzhou Meixian
Xi'an Xianggang
Xining Guangzhou
Lanzhou Nanning
Yinchuan Shantou
Wulumuqi Xiamen
Nanjing Taibei
Hefei Haikou
Wuhan Fuzhou
Guiyang Jian'ou
Chengdu Changsha
Kunming Xiangtan Taiyuan Huhehaote Pingyao “sun” 日头 rìtou
25 / 30 Modelling Chinese Dialect History Analysis Analysis
Shanghai Wenzhou Suzhou Tianjin Hangzhou
Jinan Shexian
Qingdao Tunxi
Beijing Nanchang
Harbin Taoyuan
Zhengzhou Meixian
Xi'an Xianggang
Xining Guangzhou
Lanzhou Nanning
Yinchuan Shantou
Wulumuqi Xiamen
Nanjing Taibei
Hefei Haikou
Wuhan Fuzhou
Guiyang Jian'ou
Chengdu Changsha
Kunming Xiangtan Taiyuan Huhehaote Pingyao “sun” 太阳 tàiyáng
25 / 30 Modelling Chinese Dialect History Analysis Analysis
Shanghai Wenzhou Suzhou Tianjin Hangzhou
Jinan Shexian
Qingdao Tunxi
Beijing Nanchang
Harbin Taoyuan
Zhengzhou Meixian
Xi'an Xianggang
Xining Guangzhou
Lanzhou Nanning
Yinchuan Shantou
Wulumuqi Xiamen
Nanjing Taibei
Hefei Haikou
Wuhan Fuzhou
Guiyang Jian'ou
Chengdu Changsha
Kunming Xiangtan Taiyuan Huhehaote Pingyao
“become sick” 生病 shēngbìng
25 / 30 Modelling Chinese Dialect History Analysis Analysis
Shanghai Wenzhou Suzhou Tianjin Hangzhou
Jinan Shexian
Qingdao Tunxi
Beijing Nanchang
Harbin Taoyuan
Zhengzhou Meixian
Xi'an Xianggang
Xining Guangzhou
Lanzhou Nanning
Yinchuan Shantou
Wulumuqi Xiamen
Nanjing Taibei
Hefei Haikou
Wuhan Fuzhou
Guiyang Jian'ou
Chengdu Changsha
Kunming Xiangtan Taiyuan Huhehaote Pingyao “aubergine” 茄子 qiézi
25 / 30 Modelling Chinese Dialect History Results Results
1200
1000
800
600 Genome size
400
200
0 p<0.05 p<0.05 p<0.05 p=0.2 p<0.05 p<0.05
26 / 30 Modelling Chinese Dialect History Results Results
The BOR3-model fits the distribution best. It allows up to three lateral connections per homolog. Out of 1152 homologs distributed over the Chinese dialects, 264 are monophyletic, 328 require one, 355 two, and 177 three lateral links in order to explain the distribution neatly. This corresponds to a borrowing rate of 0.5286 borrowing events per homolog per lifetime. For 78 percent of all homologs in the dataset the method reconstructs lateral links and therefore suggests that these have been involved in borrowing events during their history. Suprisingly, the 48 homologs that correspond to basic vocabulary concepts in the dataset do not show significant differences in their borrowing rates compared to the non-basic items.
26 / 30 Modelling Chinese Dialect History Results Results: General Results
Shanghai Wenzhou Suzhou Tianjin Hangzhou
Jinan Shexian
Qingdao Tunxi
Beijing Nanchang
Harbin Taoyuan
Zhengzhou Meixian
Xi'an Xianggang
Xining Guangzhou
Lanzhou Nanning
Yinchuan Shantou
Wulumuqi Xiamen
Nanjing Taibei
Hefei Haikou
Wuhan Fuzhou
Guiyang Jian'ou
Chengdu Changsha
Kunming Xiangtan Taiyuan Huhehaote Pingyao
Whole Dataset 27 / 30 Modelling Chinese Dialect History Results Results: General Results
Shanghai Wenzhou Suzhou Tianjin Hangzhou
Jinan Shexian
Qingdao Tunxi
Beijing Nanchang
Harbin Taoyuan
Zhengzhou Meixian
Xi'an Xianggang
Xining Guangzhou
Lanzhou Nanning
Yinchuan Shantou
Wulumuqi Xiamen
Nanjing Taibei
Hefei Haikou
Wuhan Fuzhou
Guiyang Jian'ou
Chengdu Changsha
Kunming Xiangtan Taiyuan Huhehaote Pingyao
Swadesh Subset 27 / 30 Modelling Chinese Dialect History Results Results: General Results
Shanghai Wenzhou Suzhou Tianjin Hangzhou
Jinan Shexian
Qingdao Tunxi
Beijing Nanchang
Harbin Taoyuan
Zhengzhou Meixian
Xi'an Xianggang
Xining Guangzhou
Lanzhou Nanning
Yinchuan Shantou
Wulumuqi Xiamen
Nanjing Taibei
Hefei Haikou
Wuhan Fuzhou
Guiyang Jian'ou
Chengdu Changsha
Kunming Xiangtan Taiyuan Huhehaote Pingyao
Whole Dataset (Cutoff 5) 27 / 30 Modelling Chinese Dialect History Results Results: General Results
Shanghai Wenzhou Suzhou Tianjin Hangzhou
Jinan Shexian
Qingdao Tunxi
Beijing Nanchang
Harbin Taoyuan
Zhengzhou Meixian
Xi'an Xianggang
Xining Guangzhou
Lanzhou Nanning
Yinchuan Shantou
Wulumuqi Xiamen
Nanjing Taibei
Hefei Haikou
Wuhan Fuzhou
Guiyang Jian'ou
Chengdu Changsha
Kunming Xiangtan Taiyuan Huhehaote Pingyao
Whole Dataset (Cutoff 10) 27 / 30 Modelling Chinese Dialect History Results Results: Chengdu
Harbin
Wulumuqi
Huhehaote
Beijing
Tianjin Yinchuan Taiyuan
Pingyao
Xining Jinan
Lanzhou Qingdao
Zhengzhou
Xi'an
Nanjing Hefei Shanghai
Suzhou Chengdu Wuhan Hangzhou Shexian
Changsha Tunxi Nanchang
Wenzhou Jian'ou
Xiangtan Guiyang Shantou Fuzhou
Kunming Taibei Xiamen Meixian
Guangzhou Taoyuan Nanning
Xianggang
Haikou
Contemporary Links Mapped to Coordinates 28 / 30 Modelling Chinese Dialect History Results Results: Chengdu
Harbin
Wulumuqi
Huhehaote
Beijing
Tianjin Yinchuan Taiyuan
Pingyao
Xining Jinan
Lanzhou Qingdao
Zhengzhou
Xi'an
Nanjing Hefei Shanghai
Suzhou Chengdu Wuhan Hangzhou Shexian
Changsha Tunxi Nanchang
Wenzhou Jian'ou
Xiangtan Guiyang Shantou Fuzhou
Kunming Taibei Xiamen Meixian
Guangzhou Taoyuan Nanning
Xianggang
Haikou
Contemporary Links of Chengdu 28 / 30 Modelling Chinese Dialect History Results Results: Chengdu
Shanghai Wenzhou Suzhou Tianjin Hangzhou
Jinan Shexian
Qingdao Tunxi
Beijing Nanchang
Harbin Taoyuan
Zhengzhou Meixian
Xi'an Xianggang
Xining Guangzhou
Lanzhou Nanning
Yinchuan Shantou
Wulumuqi Xiamen
Nanjing Taibei
Hefei Haikou
Wuhan Fuzhou
Guiyang Jian'ou
Chengdu Changsha
Kunming Xiangtan Taiyuan Huhehaote Pingyao
Links of Chengdu 28 / 30 Modelling Chinese Dialect History Results Results: Nanchang
Shanghai Wenzhou Suzhou Tianjin Hangzhou
Jinan Shexian
Qingdao Tunxi
Beijing Nanchang
Harbin Taoyuan
Zhengzhou Meixian
Xi'an Xianggang
Xining Guangzhou
Lanzhou Nanning
Yinchuan Shantou
Wulumuqi Xiamen
Nanjing Taibei
Hefei Haikou
Wuhan Fuzhou
Guiyang Jian'ou
Chengdu Changsha
Kunming Xiangtan Taiyuan Huhehaote Pingyao
Links of Nanchang 29 / 30 Modelling Chinese Dialect History Results Results: Nanchang
Harbin
Wulumuqi
Huhehaote
Beijing
Tianjin Yinchuan Taiyuan
Pingyao
Xining Jinan
Lanzhou Qingdao
Zhengzhou
Xi'an
Nanjing Hefei Shanghai
Suzhou Chengdu Wuhan Hangzhou Shexian
Changsha Tunxi Nanchang
Wenzhou Jian'ou
Xiangtan Guiyang Shantou Fuzhou
Kunming Taibei Xiamen Meixian
Guangzhou Taoyuan Nanning
Xianggang
Haikou
Contemporary Links of Nanchang 29 / 30 Modelling Chinese Dialect History Results Results: Nanchang
Nanjing Hefei Shanghai
Suzhou Wuhan Hangzhou Shexian
Changsha Tunxi Nanchang
Wenzhou Jian'ou
Xiangtan
Links between Nanchang and its Neighbors 29 / 30 Concluding Remarks
Phylogenetic networks look nice. Phylogenetic networks are – if properly reconstructed – a valid alternative to both the tree and the wave model. We need to test the method by Dagan and Martin (2008) on more data and in more detail in order to be able to give an account on its full potential and its limits.
30 / 30 Concluding Remarks
谢谢大家！
30 / 30 Concluding Remarks
Thank you!
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