Analogies, Transfer, and Adaptation Interdisciplinary Research on Evolutionary Dynamics Linguistics and Biology Johann-Mattis List Department of Linguistic and Cultural Evolution Max Planck Institute for the Science of Human History Jena 2018/03/14 1 / 52

Languages 语言 language язык språk Languages 2 / 52

Languages What is a Language? What is a Language? Norwegian, Swedish, and Danish are different languages . . Běijīng-Chinese, Shànghǎi-Chinese und Hakka-Chinese are dialects of the same language 3 / 52

Languages What is a Language? What is a Language? Běijīng Chinese 1 iou²¹ i⁵⁵ xuei³⁵ pei²¹fəŋ⁵⁵ kən⁵⁵ tʰai⁵¹iaŋ¹¹ t͡ʂəŋ⁵⁵ ʦai⁵³ naɚ⁵¹ t͡ʂəŋ⁵⁵luən⁵¹ Hakka Chinese 1 iu³³ it⁵⁵ pai³³a¹¹ pet³³fuŋ³³ tʰuŋ¹¹ ɲit¹¹tʰeu¹¹ hɔk³³ e⁵³ au⁵⁵ Shànghǎi Chinese 1 ɦi²² tʰɑ̃⁵⁵ ʦɿ²¹ poʔ³foŋ⁴⁴ taʔ⁵ tʰa³³ɦiã⁴⁴ ʦəŋ³³ hɔ⁴⁴ ləʔ¹lə²³ʦa⁵³ Běijīng Chinese 2 ʂei³⁵ də⁵⁵ pən³⁵ liŋ²¹ ta⁵¹ Hakka Chinese 2 man³³ ɲin¹¹ kʷɔ⁵⁵ vɔi⁵³ Shànghǎi 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 4 / 52

Languages What is a Language? What is a Language? From the perspective of the lexicon and the sound system, the Chinese dialects are at least as diverse as the Scandinavian languages 4 / 52

Languages Language as a Diasystem Language as a Diasystem Languages are complex aggregates of different linguistic systems which “coexist and mutually influence each other” (Coseriu 1973: 40, my translation). . . 5 / 52

Languages Language as a Diasystem Language as a Diasystem Standard Language Diatopic Varieties Diastratic Varieties Diaphasic Varieties 5 / 52

Languages Language Variation Language Variation: Dimensions LANGUAGE 6 / 52

Languages Language Variation Language Variation: Dimensions LANGUAGE diatopic place 6 / 52

Languages Language Variation Language Variation: Dimensions LANGUAGE diastratic diatopic social layer place 6 / 52

Languages Language Variation Language Variation: Dimensions LANGUAGE diastratic diatopic diaphasic social layer place situation 6 / 52

Languages Language Variation Language Variation: Dimensions LANGUAGE diastratic diatopic diaphasic diam esic social layer place situation m edium 6 / 52

Languages Language Variation Language Variation: Dimensions LANGUAGE diachronic diastratic diatopic diaphasic diam esic time social layer place situation m edium 6 / 52

Languages Language Variation Language Variation: Dimensions LANGUAGE diachronic diastratic diatopic diaphasic diam esic 6 / 52

Languages Language Variation Language Variation: Complexity of Borrowing 7 / 52

Languages Language Variation Language Variation: Complexity of Borrowing expected Mandarin [ma₅₅po₂₁lou] 7 / 52

Languages Language Variation Language Variation: Complexity of Borrowing expected Mandarin [ma₅₅po₂₁lou] attested Mandarin [wan₅₁paw₂₁lu₅₁] 7 / 52

Languages Language Variation Language Variation: Complexity of Borrowing expected Mandarin [ma₅₅po₂₁lou] attested Mandarin [wan₅₁paw₂₁lu₅₁] explanation Cantonese [maːn₂₂pow₃₅low₃₂] 7 / 52

Languages Language Variation Language Variation: Complexity of Borrowing English Cantonese Mandarin maːlboʁo maːn22 pow35 low32 wan51 paw21 lu51 Proper Name “Road of 1000 Tre- asures” “Road of 1000 Tre- asures” 万宝路 8 / 52

Language History Language History 9 / 52

Language History Dendrophilia Modeling Language History: Dendrophilia August Schleicher (1821-1868) 10 / 52

Language History Dendrophilia Modeling Language History: Dendrophilia August Schleicher (1821-1868) “These assumptions, which follow logically from the results of our re- search, can be best illustrated by the image of a branching tree.” (Schle- icher 1853: 787) 10 / 52

Language History Dendrophilia Modeling Language History: Dendrophilia Schleicher (1853) 11 / 52

Language History Dendrophobia Modeling Language History: Dendrophobia Johannes Schmidt (1843-1901) 12 / 52

Language History Dendrophobia Modeling Language History: Dendrophobia Johannes Schmidt (1843-1901) “You can turn it as you want, but as long as you stick to the idea that the his- torically attested languages have been developing by multiple furcations of an ancestral language, that is, as long as you assume that there is a Stammbaum [family tree] of the Indo-European lan- guages, you will never be able to explain all facts which have been assembled in a scientifically satisfying way.” (Schmidt 1872: 17, my translation) 12 / 52

Language History Dendrophobia Modeling Language History: Dendrophobia Johannes Schmidt (1843-1901) “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) 13 / 52

Language History Dendrophobia Modeling Language History: Dendrophobia Schmidt (1875) 14 / 52

Language History Dendrophobia Modeling Language History: Dendrophobia Meillet (1908) Hirt (1905) Bloomfield (1933) Bonfante (1931) 14 / 52

Language History Phylogenetic Networks Modeling Language History: Phylogenetic Networks Trees are bad, because... 15 / 52

Language History Phylogenetic Networks Modeling Language History: Phylogenetic Networks Trees are bad, because... they are difficult to reconstruct............ 15 / 52

Language History Phylogenetic Networks Modeling Language History: Phylogenetic Networks Trees are bad, because... they are difficult to reconstruct............ languages do not always split............ .......... ............ ............ 15 / 52

Language History Phylogenetic Networks Modeling Language History: Phylogenetic Networks Trees are bad, because... they are difficult to reconstruct............ languages do not always split............ .......... ............ ............ they are boring, since they only model the vertical aspects of language history ............ 15 / 52

Language History Phylogenetic Networks Modeling Language History: Phylogenetic Networks Trees are bad, because... they are difficult to reconstruct............ languages do not always split............ .......... ............ ............ they are boring, since they only model the vertical aspects of language history ............ Waves are bad, because nobody knows how to reconstruct them 15 / 52

Language History Phylogenetic Networks Modeling Language History: Phylogenetic Networks Trees are bad, because... they are difficult to reconstruct............ languages do not always split............ .......... ............ ............ they are boring, since they only model the vertical aspects of language history ............ Waves are bad, because nobody knows how to reconstruct them languages still diverge, even if not necessarily in split processes 15 / 52

Language History Phylogenetic Networks Modeling Language History: Phylogenetic Networks Trees are bad, because... they are difficult to reconstruct............ languages do not always split............ .......... ............ ............ they are boring, since they only model the vertical aspects of language history ............ Waves are bad, because nobody knows how to reconstruct them languages still diverge, even if not necessarily in split processes they are boring, since they only model the horizontal aspects of language history 15 / 52

Language History Phylogenetic Networks Modeling Language History: Phylogenetic Networks Hugo Schuchardt (1842-1927) 16 / 52

Language History Phylogenetic Networks Modeling Language History: Phylogenetic Networks Hugo Schuchardt (1842-1927) “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) 16 / 52

Language History Phylogenetic Networks Phylogenetic Networks 17 / 52

Language History Phylogenetic Networks Phylogenetic Networks 17 / 52

Biological Approaches in Historical Linguistics Biological Approaches in Historical Linguistics 18 / 52

Biological Approaches in Historical Linguistics Keys to the Past Keys to the Past The Geological Evidences of The Antiquity of Man with Remarks on Theories of The Origin of Species by Variation By Sir Charles Lyell London John Murray, Albemarle Street 1863 19 / 52

Biological Approaches in Historical Linguistics Keys to the Past Keys to the Past If we new not- hing of the existence of Latin, - if all historical documents previous to the fin- teenth century had been lost, - if tra- dition even was si- lent as to the former existance of a Ro- man empire, a me- re comparison of the Italian, Spanish, Portuguese, French, Wallachian, and Rhaetian dialects would enable us to say that at some time there must ha- ve been a language, from which these six modern dialects derive their origin in common. 19 / 52

Biological Approaches in Historical Linguistics Keys to the Past Keys to the Past: Uniformitarianism (C. Lyell) 20 / 52

Biological Approaches in Historical Linguistics Keys to the Past Keys to the Past: Uniformitarianism (C. Lyell) Uniformity of Change: Laws of change are uniform. They have applied in the past as they apply now and will apply in the future, no matter at which place. 20 / 52

Biological Approaches in Historical Linguistics Keys to the Past Keys to the Past: Uniformitarianism (C. Lyell) Uniformity of Change: Laws of change are uniform. They have applied in the past as they apply now and will apply in the future, no matter at which place. Graduality of Change: Change proceeds gradually, not abrupt. 20 / 52

Biological Approaches in Historical Linguistics Keys to the Past Keys to the Past: Uniformitarianism (C. Lyell) Uniformity of Change: Laws of change are uniform. They have applied in the past as they apply now and will apply in the future, no matter at which place. Graduality of Change: Change proceeds gradually, not abrupt. Abductive Reasoning: We can infer past events and processes by investigating patterns observed in the present, which becomes the “key to the interpretation of some mystery in the archives of remote ages” (Lyell 1830: 165) 20 / 52

Biological Approaches in Historical Linguistics Keys to the Past Keys to the Past: Uniformitarianism (A. Schleicher) Language Change is a gradual process (Schleicher 1848: 25). is a law-like process (Schleicher 1848: 25). is a natural process which occurs in all languages (Schleicher 1848: 25). universal process which occurs in all times (Schleicher 1863[1873]: 10f). allows us to infer past processes and extinct languages by investigating the languages of the present (see Schleicher 1848: 25). 21 / 52

Biological Approaches in Historical Linguistics Keys to the Past Keys to the Past: Summary It was not the direct exchange of ideas that lead to the devel- opment of similar approaches in biology and linguistics, but the astonishing fact that scholars in both fields would at about the same time detect striking parallels between both disci- plines, both regarding their theoretical foundations and the processes they were investigating. 22 / 52

Biological Approaches in Historical Linguistics Keys to the Past Keys to the Past: Summary It was not the direct exchange of ideas that lead to the devel- opment of similar approaches in biology and linguistics, but the astonishing fact that scholars in both fields would at about the same time detect striking parallels between both disci- plines, both regarding their theoretical foundations and the processes they were investigating. And linguists were the first to draw trees! 22 / 52

Biological Approaches in Historical Linguistics Keys to the Past Keys to the Past: Summary 1700 1800 1750 1850 List et al. (2016, Biology Direct) Stiernhielm's Lingua Nova 1671 Gallet's Arbre ca. 1800 Darwin's Origins 1859 De Buffon's Table 1755 Schleicher's Stammbaum 1853 Darwin's Tree Sketch 1837 Lamarck's Tableaux 1809 Čelakovský's Rodový Kmen 1853 Rühling's Tabula 1774 Hicke's Affinitas 1689 Schottels's Tabelle 1663 23 / 52

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Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative Turn 2002 2004 2006 2008 2010 2012 2014 25 / 52

Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative Turn 2002 2004 2006 2008 2010 2012 2014 25 / 52

Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative Turn 2002 2004 2006 2008 2010 2012 2014 25 / 52

Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative Turn 2002 2004 2006 2008 2010 2012 2014 25 / 52

Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative Turn: Words as Genes Basic Concept German ID English ID Italian ID French ID HAND Hand 1 hand 1 mano 2 main 2 BLOOD Blut 3 blood 3 sangue 4 sang 4 HEAD Kopf 5 head 6 testa 7 tête 7 TOOTH Zahn 8 tooth 8 dente 8 dent 8 TO SLEEP schlafen 9 sleep 9 dormir 10 dormir 10 TO SAY sagen 11 say 11 dire 12 dire 12 ... ... ... ... ... ... ... ... ... 26 / 52

Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative Turn: Words as Genes Basic Concept German ID English ID Italian ID French ID HAND Hand 1 hand 1 mano 2 main 2 BLOOD Blut 3 blood 3 sangue 4 sang 4 HEAD Kopf 5 head 6 testa 7 tête 7 TOOTH Zahn 8 tooth 8 dente 8 dent 8 TO SLEEP schlafen 9 sleep 9 dormir 10 dormir 10 TO SAY sagen 11 say 11 dire 12 dire 12 ... ... ... ... ... ... ... ... ... 26 / 52

Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative Turn: Words as Genes ID Proto-Form Basic Concept German English Italian French 1 PGM *xanda- HAND 1 1 0 0 2 LAT mānus HAND 0 0 1 1 3 PGM *blođa- BLOOD 1 1 0 0 4 LAT sanguis BLOOD 0 0 1 1 5 PGM *kuppa- HEAD 1 0 0 0 6 PGM *xawbda- HEAD 0 1 0 0 7 LAT tēsta HEAD 0 0 1 1 8 PIE *h3 dont- TOOTH 1 1 1 1 9 PGM *slēpan- TO SLEEP 1 1 0 0 10 LAT dormīre TO SLEEP 0 0 1 1 11 PGM *sagjan- TO SAY 1 1 0 0 12 LAT dīcere TO SAY 0 0 1 1 ... ... ... ... ... ... ... 26 / 52

Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative Turn: Words as Genes English 111 German 101 French 000 Italian 001 101 001 001 + B − C + A Char. English German French Italian A 1 1 0 0 B 1 0 0 0 C 1 1 0 1 26 / 52

Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative Turn: Sounds as Nuclein Bases Concept German English Italian French “HAND” G E I F Hand 0 1 2 3 hand 1 0 2 3 mano 2 2 0 2 main 3 3 2 0 “BLOOD” G E I F Blut 0 4 5 4 blood 4 0 6 5 sangue 5 6 0 2 sang 4 5 2 0 Edit Distances between Orthographic Entries 27 / 52

Biological Approaches in Historical Linguistics The Quantitative Turn The Quantitative Turn: Sounds as Nuclein Bases German English Italian French German 0 30 60 55 English 30 0 60 50 Italian 60 60 0 20 French 55 50 20 0 27 / 52

Biological Approaches in Historical Linguistics Analogies and Parallels Analogies and Parallels Parallels between Species and Languages (Pagel 2009) aspect species languages unit of replication gene word replication asexual und sexual reproduction learning speciation cladogenesis language split forces of change natural selection and genetic drift social selection and trends differentiation tree-like tree-like 28 / 52

Biological Approaches in Historical Linguistics Analogies and Parallels Analogies and Parallels 29 / 52

Biological Approaches in Historical Linguistics Analogies and Parallels Analogies and Parallels 29 / 52

Biological Approaches in Historical Linguistics Analogies and Parallels Analogies and Parallels Differences between Species and Languages (Geisler & List 2013) Aspect Species Languages domain Popper’s World I Popper’s World III relation between form and function mechanical arbitrary origin monogenesis unclear sequence similarity universal (indepen- dent of species) language-specific differentiation tree-like network-like 30 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Alphabets 31 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Alphabets • universal • language-specific 31 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Alphabets • universal • language-specific • limited • widely varying 31 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Alphabets • universal • language-specific • limited • widely varying • constant • mutable 31 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes GENES <=> WORDS HOMOLOGS <=> COGNATES 32 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa 33 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa l > j 33 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j 33 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j l > l 33 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j l > l l > j / p _ 33 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j l > l l > j / p _ Not sounds change, sound systems change (Bloomfield 1933)! 33 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j l > l l > j / p _ Not sounds change, sound systems change (Bloomfield 1933)! Sound change depends on the context in which the sounds occur! 33 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Meaning Latin Italian ‘FEATHER’ pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j l > l l > j / p _ Not sounds change, sound systems change (Bloomfield 1933)! Sound change depends on the context in which the sounds occur! Sound change largely follows irreversible patterns! 33 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change 34 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Was ist das für ein Buchstabe? Das ist ein P. 34 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Was ist das für ein Buchstabe? Das ist ein P. Ich püsse euch alle, ganz besonders Averell, meinen Pleinen. Das reicht! 34 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Was ist das für ein Buchstabe? Das ist ein P. Ich püsse euch alle, ganz besonders Averell, meinen Pleinen. Das reicht! Aber wohin gehen wir, wenn man uns wieder einfängt? Plappe Pleiner! 34 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Was ist das für ein Buchstabe? Das ist ein P. Ich püsse euch alle, ganz besonders Averell, meinen Pleinen. Das reicht! Aber wohin gehen wir, wenn man uns wieder einfängt? Plappe Pleiner! Liebe Kinder, heute habe ich Lucky Luke getroffen. Ich küsse euch, ganz besonders Averell, meinen Kleinen! Eure Ma Dalton 34 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Was ist das für ein Buchstabe? Das ist ein P. Ich püsse euch alle, ganz besonders Averell, meinen Pleinen. Das reicht! Aber wohin gehen wir, wenn man uns wieder einfängt? Plappe Pleiner! Liebe Pinder, heute habe ich Lucpy Lupe getroffen. Ich püsse euch, ganz besonders Averell, meinen Pleinen! Eure Ma Dalton 34 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Nase Nass Muse muss singen 35 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Nase Nass Muse muss singen m → b n → d ng → g 35 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Nase Nass Muse muss singen m → b n → d ng → g ss → f s → w 35 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Nase Nass Muse muss singen Dase Dass Buse buss sigen 35 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change Nase Nass Muse muss singen Dase Dass Buse buss sigen Nawe Naf Muwe muf wingen 35 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Sound Change *Nase *Nass *Muse *muss *singen Dase Dass Buse buss sigen Nawe Naf Muwe muf wingen 35 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Homology The term homology was coined by Richard Owen (1804–1892), who distinguished ‘homologues’, as ‘the same organ in different animals under every variety of form and function’ (Owen 1843: 379), from from ‘analogues’ as an ‘organ in one animal which has the same function as another part or organ in a different animal’ (ibid.: 374). 36 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Homology The term homology was coined by Richard Owen (1804–1892), who distinguished ‘homologues’, as ‘the same organ in different animals under every variety of form and function’ (Owen 1843: 379), from from ‘analogues’ as an ‘organ in one animal which has the same function as another part or organ in a different animal’ (ibid.: 374). Nowadays, it commonly denotes a ‘relationship of common descent between any entities, without further specification of the evolutionary scenario’ (Koonin 2005: 311). 36 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Homology The term homology was coined by Richard Owen (1804–1892), who distinguished ‘homologues’, as ‘the same organ in different animals under every variety of form and function’ (Owen 1843: 379), from from ‘analogues’ as an ‘organ in one animal which has the same function as another part or organ in a different animal’ (ibid.: 374). Nowadays, it commonly denotes a ‘relationship of common descent between any entities, without further specification of the evolutionary scenario’ (Koonin 2005: 311). With respect to specific scenarios of common descent, molecular biologists characterize relationships between homologous genes further by distinguishing between orthology, paralogy, and xenology. 36 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Homology B A C D duplication speciation lateral transfer D D orthologs paralogs xenologs B C D B A A B A B List (2016) 37 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Homology Historical Relations Terminology Biology Linguistics common descent direct homology orthology cognacy.... ? oblique cognacy indirect paralogy involving lateral transfer xenology ? Linguistics indirect cognate relation (oblique cognacy) cognate relation (cognacy) ? ? ? 37 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Homology Historical Relations Terminology Biology Linguistics common descent direct homology orthology cognacy.... ? oblique cognacy indirect paralogy involving lateral transfer xenology ? Linguistics direct cognate relation etymological relation indirect cognate relation (oblique cognacy) indirect etymological relation cognate relation (cognacy) List (2014) 37 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Homology Relation Biol. Term continuity traditional notion of cognacy - + +/- +/- cognacy à la Swadesh - + +/- + direct cognate relation orthology + + + oblique cognate relation paralogy (?) + - + etymological relation homology +/- +/- +/- oblique etymological relation xenology - +/- +/- ... ... ... ... ... Stratic Morpho- logical Seman- tic List (2016) 37 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Homology Italian dare French donner Indo-European *deh₃- *deh₃-no- Latin dare dōnum dōnāre Italian sole French soleil Swedish sol German Sonne Germanic *sōwel- *sunnō- Latin sol soliculus Indo-European *sóh₂-wl̩ - *sh₂én- A B List (2016) 37 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Semantic Change hand arm foot day m eat animal day sand moon leg T₁ 38 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Semantic Change hand arm foot day m eat animal day sand moon leg T₁ hand arm foot day m eat animal day sand moon leg T₂ 38 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Semantic Change hand arm foot day m eat animal day sand moon leg T₂ ? ? ? 38 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Semantic Change hand arm foot day m eat animal day sand moon leg T₂ hand arm foot day m eat animal sun sand moon leg 38 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Semantic Change Semantic change plays a crucial role in language change. Al- though most linguists assume that it proceeds according to certain general patterns, we currently lack the empirical basis to pursue the question in depth. Normally, semantic change proceeds by cumulation and reduction. 39 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Semantic Change German “head” Kopf . k ɔ p͡f Pre-German “head” *kop – k ɔ p “vessel” Proto- Germanic *kuppa- k u pː a “vessel” POLYSEMY PHASE FORM MEANING MONOSEMY PHASE MONOSEMY PHASE CUMULATION REDUCTION 39 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Semantic Change “cup” CONTEST TROPHY [kʌp] CUP English polysemy structure for cup 39 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Semantic Change “head, cup” CUP HEAD [kɔp] TOP Dutch polysemy structure for kop 39 / 52

Biological Approaches in Historical Linguistics Differences Differences in the Processes: Semantic Change “head” HEAD TOP [kɔp͡f] CHIEF German polysemy structure for Kopf 39 / 52

New Approaches in Historical Linguistics Shifting the Paradigm 40 / 52

New Approaches in Historical Linguistics Rethinking Parallels Rethinking Parallels Our crucial approach to interdisciplinary research is to adapt suitable methods from other disciplines to our needs instead of blindly taking them unmodified without testing whether they are suitable to be used in historical linguistics after all. 41 / 52

New Approaches in Historical Linguistics Automatic Word Comparison Automatic Word Comparison: Basic Workflow WORDLIST DATA 42 / 52

New Approaches in Historical Linguistics Automatic Word Comparison Automatic Word Comparison: Basic Workflow WORDLIST DATA PAIRWISE DISTANCES BETWEEN WORDS PAIRWISE COMPARISON 42 / 52

New Approaches in Historical Linguistics Automatic Word Comparison Automatic Word Comparison: Basic Workflow WORDLIST DATA PAIRWISE DISTANCES BETWEEN WORDS COGNATE SETS COGNATE CLUSTERING PAIRWISE COMPARISON 42 / 52

New Approaches in Historical Linguistics Automatic Word Comparison Automatic Word Comparison: Basic Workflow Analysis ID Taxa Word Gloss GlossID IPA ... ... ... ... ... ... 21 German Frau woman 20 frau 22 Dutch vrouw woman 20 vrɑu 23 English woman woman 20 wʊmən 24 Danish kvinde woman 20 kvenə 25 Swedish kvinna woman 20 kviːna 26 Norwegian kvine woman 20 kʋinə ... ... ... ... ... ... 42 / 52

New Approaches in Historical Linguistics Automatic Word Comparison Automatic Word Comparison: Basic Workflow Swedish English Danish Norwegian Dutch German kvinna woman kvinde kvine vrouw Frau Swedish kvina 0.00 0.69 0.07 0.12 0.71 0.78 English wumin 0.69 0.00 0.66 0.57 0.68 0.87 Danish kveni 0.07 0.66 0.00 0.08 0.67 0.71 Norwegian kwini 0.12 0.57 0.08 0.00 0.75 0.74 Dutch frou 0.71 0.68 0.67 0.75 0.00 0.17 German frau 0.78 0.87 0.71 0.74 0.17 0.00 Analysis ID Taxa Word Gloss GlossID IPA ... ... ... ... ... ... 21 German Frau woman 20 frau 22 Dutch vrouw woman 20 vrɑu 23 English woman woman 20 wʊmən 24 Danish kvinde woman 20 kvenə 25 Swedish kvinna woman 20 kviːna 26 Norwegian kvine woman 20 kʋinə ... ... ... ... ... ... 42 / 52

New Approaches in Historical Linguistics Automatic Word Comparison Automatic Word Comparison: Basic Workflow Swedish English Danish Norwegian Dutch German kvinna woman kvinde kvine vrouw Frau Swedish kvina 0.00 0.69 0.07 0.12 0.71 0.78 English wumin 0.69 0.00 0.66 0.57 0.68 0.87 Danish kveni 0.07 0.66 0.00 0.08 0.67 0.71 Norwegian kwini 0.12 0.57 0.08 0.00 0.75 0.74 Dutch frou 0.71 0.68 0.67 0.75 0.00 0.17 German frau 0.78 0.87 0.71 0.74 0.17 0.00 German Frau frau Dutch vrouw vrou English woman wumin Danish kvinde kveni Swedish kvinna kvina Norwegian kvine kwini 42 / 52

New Approaches in Historical Linguistics Automatic Word Comparison Automatic Word Comparison: Basic Workflow Swedish English Danish Norwegian Dutch German kvinna woman kvinde kvine vrouw Frau Swedish kvina 0.00 0.69 0.07 0.12 0.71 0.78 English wumin 0.69 0.00 0.66 0.57 0.68 0.87 Danish kveni 0.07 0.66 0.00 0.08 0.67 0.71 Norwegian kwini 0.12 0.57 0.08 0.00 0.75 0.74 Dutch frou 0.71 0.68 0.67 0.75 0.00 0.17 German frau 0.78 0.87 0.71 0.74 0.17 0.00 German Frau frau Dutch vrouw vrou English woman wumin Danish kvinde kveni Swedish kvinna kvina Norwegian kvine kwini 42 / 52

New Approaches in Historical Linguistics Automatic Word Comparison Automatic Word Comparison: Basic Workflow German Frau frau Dutch vrouw vrou English woman wumin Danish kvinde kveni Swedish kvinna kvina Norwegian kvine kwini Analysis ID Taxa Word Gloss GlossID IPA CogID ... ... ... ... ... ... ... 21 German Frau woman 20 frau 1 22 Dutch vrouw woman 20 vrɑu 1 23 English woman woman 20 wʊmən 2 24 Danish kvinde woman 20 kvenə 3 25 Swedish kvinna woman 20 kviːna 3 26 Norwegian kvine woman 20 kʋinə 3 ... ... ... ... ... ... ... 42 / 52

New Approaches in Historical Linguistics Automatic Word Comparison Automatic Word Comparison: Problems since linguistic alphabets change, linguistic alignments need to infer both the mappings between the different alphabets and the alignment itself! the only workaround for this is to preparse the data, using an initial guess for alignments to infer mappings between the different alphabets for each language pair, and compare these against a random distribution drawn from permutation tests this workflow requires more time than a simple alignment of sequences, but luckily, our sequences are small! 43 / 52

New Approaches in Historical Linguistics Automatic Word Comparison Automatic Word Comparison: Workflow INPUT TOKENIZATION PREPROCESSING LOG-ODDS D ISTANCE COGNATE OUTPUT CORRESPONDENCE DETECTION USING PHONETIC ALIGNMENT LOOP DISTRIBUTION LexStat Algorithm (List 2014) EXPECTED ATTESTED DISTRIBUTION CALCULATION CLUSTERING 44 / 52

New Approaches in Historical Linguistics Automatic Word Comparison Automatic Word Comparison: Evaluation Edit-Dist. SCA Infomap Bahnaric Chinese Huon Romance Tujia Uralic Turchin LexStat TOTAL true positive true negative false negative false positive Accuracy of automatic word comparisons (List et al. 2017) 45 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Background 46 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Background One beer please! A beer for me! Beer? Please? You have beer? I'm thirsty, but I do not drink water, can you help me? I want the same as everybody else here. 46 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Background Tokens Units Relations Levels sounds phonemes phonotactics phonemics words morphemes morpho-tactics morphemics sentences constructions grammatical syntax 47 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Background English steam train German Dampfzug (steam + train) Chinese huǒ chē (fire + vehicle) Russian parovoz (steam + driver) French locomotive à vapeur (locomotive + with + steam) 47 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Background We can think of many different ways of how to express a cer- tain meaning, but although the potential is virtually unlimited, the roads of denotation, that is, the mechanisms by which words are formed from morphemes, follow certain recurring patterns across all languages. Comparing these patterns can give us important insights into human cognition. 47 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Background On the other hand, the fact that words are often formed from smaller parts, be it by compounding existing words, or using specific morphemes to derive new words, makes it very diffi- cult to identify homologous words automatically! What are the mechanisms by which the roads of denotation are created across the worlds languages? How can we distinguish direct homologues (orthologues) from indirect ones (partial homologues, etc.) in phylogenetic models or homologue detection? 47 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Investigation Automatic Detection of Partial Cognates: Problem languages in which words are frequently created by compounding the identification of homologous words is extremely difficult current phylogenetic models cannot handle partial homology, and as a result, very important signal is lost current methods for automatic homologue detection in linguistics also cannot handle partial homologues and show a very low accuracy in languages where compounding is frequent (especially in the languages of South-East Asia) 48 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Investigation German m oː n t - English m uː n - - Danish m ɔː n - ə Swedish m oː n - e 48 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Investigation German m oː n t - English m uː n - - Danish m ɔː n - ə Swedish m oː n - e Fúzhōu ŋ u o ʔ ⁵ - - - - - - - - - - Měixiàn ŋ i a t ⁵ - - - - - k u o ŋ ⁴⁴ Guǎngzhōu j - y t ² l - œ ŋ ²² - - - - - Běijīng - y ɛ - ⁵¹ l i ɑ ŋ - - - - - - 48 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Investigation German m oː n t - English m uː n - - Danish m ɔː n - ə Swedish m oː n - e Fúzhōu ŋ u o ʔ ⁵ - - - - - - - - - - Měixiàn ŋ i a t ⁵ - - - - - k u o ŋ ⁴⁴ Guǎngzhōu j - y t ² l - œ ŋ ²² - - - - - Běijīng - y ɛ - ⁵¹ l i ɑ ŋ - - - - - - "MOON" "MOON" "SHINE" "LIGHT" 48 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Investigation Automatic Detection of Partial Cognates: The Solution use sequence similarity networks to determine the similarity between the parts of the words in the data apply filters to reduce the edges in the similarity networks use a community detection algorithm to further partition the data into clusters 48 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Investigation Fúzhōu ŋuoʔ⁵ Měixiàn ŋiat⁵ 0.44 kuoŋ⁴⁴ 0.78 0.78 Wēnzhōu y²¹ ȵ 0.30 0.35 0.67 ku ³ ɔ ⁵ 0.80 0.85 0.27 0.67 vai¹³ 0.85 0.85 0.82 0.73 0.73 Běijīng y ¹ ɛ⁵ 0.77 0.84 0.73 0.56 0.56 0.66 li ŋ¹ ɑ 0.78 0.78 0.44 0.67 0.82 0.82 0.80 ŋiat⁵ kuoŋ⁴⁴ ŋuoʔ⁵ ȵy²¹ yɛ⁵¹ kuɔ³⁵ liɑŋ¹ vai¹³ ŋiat⁵ vai¹³ kuoŋ⁴⁴ ŋuoʔ⁵ liɑŋ¹ yɛ⁵¹ ȵy²¹ kuɔ³⁵ ȵy²¹ kuɔ³⁵ ŋiat⁵ yɛ⁵¹ liɑŋ¹ ŋuoʔ⁵ kuoŋ⁴⁴ vai¹³ B C D A 48 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Investigation Fúzhōu ŋuoʔ⁵ Měixiàn ŋiat⁵ 0.44 kuoŋ⁴⁴ 0.78 0.78 Wēnzhōu y²¹ ȵ 0.30 0.35 0.67 ku ³ ɔ ⁵ 0.80 0.85 0.27 0.67 vai¹³ 0.85 0.85 0.82 0.73 0.73 Běijīng y ¹ ɛ⁵ 0.77 0.84 0.73 0.56 0.56 0.66 li ŋ¹ ɑ 0.78 0.78 0.44 0.67 0.82 0.82 0.80 ŋiat⁵ kuoŋ⁴⁴ ŋuoʔ⁵ ȵy²¹ yɛ⁵¹ kuɔ³⁵ liɑŋ¹ vai¹³ ŋiat⁵ vai¹³ kuoŋ⁴⁴ ŋuoʔ⁵ liɑŋ¹ yɛ⁵¹ ȵy²¹ kuɔ³⁵ ȵy²¹ kuɔ³⁵ ŋiat⁵ yɛ⁵¹ liɑŋ¹ ŋuoʔ⁵ kuoŋ⁴⁴ vai¹³ B C D A 48 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Investigation Fúzhōu ŋuoʔ⁵ Měixiàn ŋiat⁵ 0.44 kuoŋ⁴⁴ 0.78 0.78 Wēnzhōu y²¹ ȵ 0.30 0.35 0.67 ku ³ ɔ ⁵ 0.80 0.85 0.27 0.67 vai¹³ 0.85 0.85 0.82 0.73 0.73 Běijīng y ¹ ɛ⁵ 0.77 0.84 0.73 0.56 0.56 0.66 li ŋ¹ ɑ 0.78 0.78 0.44 0.67 0.82 0.82 0.80 ŋiat⁵ kuoŋ⁴⁴ ŋuoʔ⁵ ȵy²¹ yɛ⁵¹ kuɔ³⁵ liɑŋ¹ vai¹³ ŋiat⁵ vai¹³ kuoŋ⁴⁴ ŋuoʔ⁵ liɑŋ¹ yɛ⁵¹ ȵy²¹ kuɔ³⁵ ȵy²¹ kuɔ³⁵ ŋiat⁵ yɛ⁵¹ liɑŋ¹ ŋuoʔ⁵ kuoŋ⁴⁴ vai¹³ B C D A 48 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Investigation Fúzhōu ŋuoʔ⁵ Měixiàn ŋiat⁵ 0.44 kuoŋ⁴⁴ 0.78 0.78 Wēnzhōu y²¹ ȵ 0.30 0.35 0.67 ku ³ ɔ ⁵ 0.80 0.85 0.27 0.67 vai¹³ 0.85 0.85 0.82 0.73 0.73 Běijīng y ¹ ɛ⁵ 0.77 0.84 0.73 0.56 0.56 0.66 li ŋ¹ ɑ 0.78 0.78 0.44 0.67 0.82 0.82 0.80 ŋiat⁵ kuoŋ⁴⁴ ŋuoʔ⁵ ȵy²¹ yɛ⁵¹ kuɔ³⁵ liɑŋ¹ vai¹³ ŋiat⁵ vai¹³ kuoŋ⁴⁴ ŋuoʔ⁵ liɑŋ¹ yɛ⁵¹ ȵy²¹ kuɔ³⁵ ȵy²¹ kuɔ³⁵ ŋiat⁵ yɛ⁵¹ liɑŋ¹ ŋuoʔ⁵ kuoŋ⁴⁴ vai¹³ B C D A 48 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Investigation Fúzhōu ŋuoʔ⁵ Měixiàn ŋiat⁵ 0.44 kuoŋ⁴⁴ 0.78 0.78 Wēnzhōu y²¹ ȵ 0.30 0.35 0.67 ku ³ ɔ ⁵ 0.80 0.85 0.27 0.67 vai¹³ 0.85 0.85 0.82 0.73 0.73 Běijīng y ¹ ɛ⁵ 0.77 0.84 0.73 0.56 0.56 0.66 li ŋ¹ ɑ 0.78 0.78 0.44 0.67 0.82 0.82 0.80 ŋiat⁵ kuoŋ⁴⁴ ŋuoʔ⁵ ȵy²¹ yɛ⁵¹ kuɔ³⁵ liɑŋ¹ vai¹³ ŋiat⁵ vai¹³ kuoŋ⁴⁴ ŋuoʔ⁵ liɑŋ¹ yɛ⁵¹ ȵy²¹ kuɔ³⁵ ȵy²¹ kuɔ³⁵ ŋiat⁵ yɛ⁵¹ liɑŋ¹ ŋuoʔ⁵ kuoŋ⁴⁴ vai¹³ B C D A 48 / 52

New Approaches in Historical Linguistics Partial Cognacy Partial Cognacy: Investigation Automatic Detection of Partial Cognates: Solution with help of sequence similarity networks, we (List, Lopez, and Bapteste 2016) have created the first algorithm to detect partial cognates (homologues) in linguistic data our method outperforms traditional methods largely, reaching a plus of more than 5% in accuracy on our test sets the algorithms is also very fast and can be easily applied to considerably large datasets 48 / 52

New Approaches in Historical Linguistics Semantic Change Semantic Change: Investigation Key Concept Russian German ... 1.1 world mir, svet Welt ... 1.21 earth, land zemlja Erde, Land ... 1.212 ground, soil počva Erde, Boden ... 1.420 tree derevo Baum ... 1.430 wood derevo Wald ... ... ... ... ... ... 49 / 52

New Approaches in Historical Linguistics Semantic Change Semantic Change: Investigation CLICS: Crosslinguistic Colexifications - 221 Languages - 64 language families - 1280 concepts - 301,498 words - 45,667 polysemies (colexifications) - 16,239 different links between concepts - http://clics.lingpy.org 49 / 52

New Approaches in Historical Linguistics Semantic Change Semantic Change: Investigation 684 678 871 1043 6 30 129 196 1243 128 869 853 650 344 1103 150 185 627 232 709 1035 1206 177 97 311 496 606 137 207 444 840 1077 325 222 1063 1138 1204 1258 559 723 495 766 914 38 1101 652 865 891 872 633 291 980 700 144 410 430 1025 406 464 787 622 131 242 918 275 1159 99 1174 671 1038 786 705 641 760 1259 356 391 197 10 214 299 63 191 619 644 792 1205 897 67 1231 213 226 747 681 399 841 439 773 123 800 16 1067 1227 696 417 550 68 76 108 360 1244 339 500 81 867 79 1097 98 96 833 771 715 455 380 1268 1186 1046 39 252 1228 66 23 1112 133 676 336 739 1150 1071 986 485 112 372 1109 830 721 1053 1057 601 573 556 527 1248 614 488 908 499 1002 309 442 814 1193 569 458 258 563 653 682 774 70 1151 948 801 1082 243 47 71 83 153 1265 934 85 1215 1199 523 581 422 21 358 1261 111 354 219 759 15 890 261 1222 141 158 74 806 1031 845 770 850 903 1224 419 754 433 798 188 1256 613 528 208 539 323 981 132 1055 1001 790 804 844 1118 907 640 446 815 923 498 201 1184 578 566 427 532 452 151 750 598 1094 345 735 777 978 599 492 390 286 1107 742 1015 1202 1210 1257 1275 859 988 69 752 596 290 126 110 950 922 1047 741 253 347 385 620 966 221 431 3 224 1194 999 953 1029 852 301 389 318 530 1048 1032 175 701 544 1119 241 94 745 835 1270 62 107 159 20 767 512 331 248 549 1013 946 974 1022 1100 477 302 233 1168 1003 1211 570 307 40 945 1269 784 546 437 901 350 238 305 1191 482 1012 977 906 783 524 117 457 603 836 1181 880 229 124 216 1113 1074 72 586 647 447 2 113 1179 7 1006 665 397 502 610 1274 707 327 659 667 824 917 985 1089 346 1229 101 542 1042 727 782 733 967 462 592 468 1106 440 478 308 577 698 776 75 1155 51 145 517 359 938 1157 1160 1183 947 1102 1135 1252 343 608 537 103 634 251 383 506 25 829 396 686 679 574 516 42 250 379 809 602 660 780 765 697 856 899 594 1008 393 179 114 1140 11 100 1209 618 600 192 1277 896 1142 1278 762 421 713 182 521 861 672 297 1116 1190 1192 140 1212 46 493 1187 157 1225 212 403 519 616 173 413 912 1110 84 756 793 636 118 889 692 998 366 711 1045 61 240 1263 199 648 832 289 522 368 1091 931 982 949 400 119 388 811 53 59 1069 708 952 545 763 1238 184 825 377 1242 1233 262 635 269 1062 1061 1073 933 17 1247 352 64 384 50 632 736 1246 822 781 758 1 939 595 778 105 860 1049 1066 1072 995 503 370 919 1149 1127 1128 972 1126 245 921 973 675 587 1235 960 928 926 1143 548 1250 86 1021 32 1068 719 965 259 1070 863 638 303 324 873 249 892 976 1007 722 36 459 293 165 209 557 1245 788 862 651 900 31 483 236 935 1052 115 294 680 831 44 453 206 971 1273 170 753 256 1148 200 450 382 1240 561 615 317 572 725 870 438 139 1011 646 1117 392 45 276 264 704 1080 174 1050 808 1197 508 576 225 562 471 1217 333 1014 593 92 1034 611 1171 312 802 1253 29 902 244 582 466 668 878 341 432 1163 625 904 164 467 1195 1232 796 828 281 629 349 1166 411 369 387 1208 394 415 1000 58 1098 148 287 1223 818 263 220 838 876 313 260 65 1165 5 355 106 1172 490 718 171 1139 163 785 881 887 1169 319 585 553 894 306 314 1041 1009 799 674 848 1201 1004 689 1085 1218 1145 1170 228 911 279 73 104 690 1254 402 340 169 693 868 893 1018 78 1092 194 555 198 834 1249 997 932 237 1176 666 956 624 1262 541 520 795 866 702 4 734 1095 1180 728 964 1079 271 842 1241 1056 154 751 353 905 1136 504 909 910 1133 362 583 670 1124 381 1216 215 178 571 470 142 376 1154 172 296 533 364 963 152 797 1213 803 1051 738 426 1036 1153 637 823 915 428 1075 560 547 1137 35 882 89 511 1122 805 494 1130 1188 1086 1236 669 588 930 703 942 18 655 335 155 710 1156 1028 465 147 183 414 1221 273 166 1054 278 55 460 812 1090 810 180 768 143 156 404 367 1182 231 288 136 456 82 529 970 1016 729 395 187 604 408 330 1064 34 1267 847 726 543 677 642 940 645 958 683 695 864 1058 605 1084 451 443 699 1167 959 925 1198 227 886 628 1178 337 991 813 657 1185 1039 769 1081 484 712 1189 944 1207 322 33 685 424 80 270 937 1177 283 1237 816 130 161 189 77 300 1026 463 1104 326 589 60 983 474 1093 744 748 554 292 41 267 984 373 1214 957 1024 969 507 37 874 1030 630 579 962 535 706 688 122 497 1060 1083 1027 102 510 405 1134 658 617 936 929 363 1175 361 536 534 1219 181 386 884 418 558 8 479 979 551 505 316 298 26 315 761 202 1144 176 473 348 134 639 663 717 885 924 149 49 1078 1040 57 167 764 1173 673 280 1152 277 1272 1065 272 827 531 607 1123 257 996 436 9 826 234 1096 875 525 304 1108 475 1132 714 846 540 716 1005 1105 357 1162 694 920 743 28 994 1200 168 1266 420 515 568 755 895 218 916 730 807 210 375 854 1010 879 1125 268 1129 1114 1255 1158 1279 487 486 398 597 661 135 565 621 193 321 1230 513 654 265 612 737 855 211 1196 246 1264 584 338 749 1271 434 121 423 509 839 1147 656 230 239 489 14 469 22 1044 351 448 282 329 961 254 989 371 284 223 843 821 24 1023 643 819 285 514 746 757 791 138 186 849 93 951 127 877 1088 518 1164 1260 501 54 190 95 43 205 1276 116 146 662 217 461 883 204 1033 310 472 12 412 332 817 649 794 1037 943 927 481 968 425 109 195 857 1121 564 687 664 724 87 1120 88 449 429 255 987 992 1111 591 575 491 720 851 328 941 990 1019 993 1087 955 580 1226 975 1099 732 235 779 365 1234 441 609 247 334 91 1251 1131 913 691 52 274 1017 435 90 407 480 1239 13 623 0 266 626 295 954 1059 552 898 858 772 526 1115 48 1161 125 590 454 1020 1141 203 740 1146 342 820 1220 56 320 416 27 401 476 19 120 1203 445 789 775 888 567 378 1076 160 162 409 731 631 374 538 837 49 / 52

New Approaches in Historical Linguistics Semantic Change Semantic Change: Investigation Concept "money" is part of a cluster with the central concept "fishscale" with a total of 10 nodes. Hover over forms for each link. Click on the forms to check their sources. Click HERE to export the current network. ty: Line weights: Coloring: Family silver leather fishscale bark coin fur snail skin, hide money shell 49 links for "silver" and "money": Language Family Form 1. Ignaciano Arawakan ne 2. Aymara, Central Aymaran ḳulʸḳi 3. Tsafiki Barbacoan kaˈla 4. Seselwa Creole French Creole larzan 5. Miao, White Hmong-Mien nyiaj 6. Breton Indo-European arhant 7. French Indo-European argent 8. Gaelic, Irish Indo-European airgead 9. Welsh Indo-European arian 10. Cofán Isolate koriΦĩʔdi 49 / 52

New Approaches in Historical Linguistics Semantic Change Semantic Change: Investigation Concept "wheel" is part of a cluster with the central concept "leg" with a total of 11 nodes. Hover over the e each link. Click on the forms to check their sources. Click HERE to export the current network. ity: Line weights: Coloring: Geolocation sphere, ball round footprint foot calf of leg circle thigh wheel leg hip buttocks 6 links for "foot" and "wheel": Language Family Form 1. Cofán Isolate c̷ɨʔtʰe 2. Puinave Isolate sim 3. Yaminahua Panoan taɨ 4. Wayampi Tupi pɨ 5. Pumé Unclassified taɔ 6. Ninam Yanomam mãhuk 49 / 52

Outlook Outlook Outlook 50 / 52

Outlook Outlook interdisciplinary work can be useful and rewarding but we need to be careful to not overstrain our analogies we can try and get inspiration from solutions proposed in other disciplines 51 / 52

Outlook Outlook interdisciplinary work can be useful and rewarding but we need to be careful to not overstrain our analogies we can try and get inspiration from solutions proposed in other disciplines but we should never forget who we are: LINGUISTS AND PROUD! 51 / 52

Danke fürs Zuhören! 52 / 52