List DFG research fellow Centre des recherches linguistiques sur l’Asie Orientale Team Adaptation, Integration, Reticulation, Evolution EHESS and UPMC, Paris 2016/02/02 1 / 50
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) 3 / 50
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) 5 / 50
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) 6 / 50
Trees are bad, because... they are difficult to reconstruct............ languages do not always split............ .......... ............ ............ 8 / 50
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 ............ 8 / 50
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 8 / 50
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 8 / 50
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 8 / 50
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) 9 / 50
Indo-European soːwel- sunːoː- SUN Germanic soːl- SUN soːlikul- SMALL SUN Romance solej SUN French sol SUN Spanish zɔnə SUN German suːl SUN Swedish SEM ANTIC SHIFT M O RPH O LO G ICAL CH AN G E M O R PH O LO G ICA L CH A N G E MORPHOLOGICAL CHANGE MORPHOLOGICAL CHANGE 11 / 50
pluːma pjuma ‘FLAT’ plaːnus pjano ‘SQUARE’ plateːa pjaʦːa Meaning Latin Italian ‘TONGUE’ liŋgua liŋgwa ‘MOON’ lu:na luna ‘SLOW’ lentus lento l > j 13 / 50
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 13 / 50
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 _ 13 / 50
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)! 13 / 50
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! 13 / 50
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! 13 / 50
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 15 / 50
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. 15 / 50
to the Past: Uniformitarianism (Charles 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. 16 / 50
to the Past: Uniformitarianism (Charles 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. 16 / 50
to the Past: Uniformitarianism (Charles 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) 16 / 50
to the Past: Uniformitarianism (August 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). 17 / 50
to the Past: Summary It was not the direct exchange of ideas that lead to the de- velopment 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 disciplines, both regarding their theoretical founda- tions and the processes they were investigating. 18 / 50
to the Past: Summary It was not the direct exchange of ideas that lead to the de- velopment 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 disciplines, both regarding their theoretical founda- tions and the processes they were investigating. And linguists were the first to draw trees! 18 / 50
Turn “Indo-European and computational cladistics” (Ringe, Warnow and Taylor 2002) “Language-tree divergence times support the Anatolian theory of Indo-European origin” (Gray und Atkinson 2003) “Language classification by numbers” (McMahon und McMahon 2005) “Curious Parallels and Curious Connections: Phylogenetic Thinking in Biology and Historical Linguistics” (Atkinson und Gray 2005) “Automated classification of the world’s languages” (Brown et al. 2008) “Indo-European languages tree by Levenshtein distance” (Serva and Petroni 2008) “Networks uncover hidden lexical borrowing in Indo-European language evolution” (Nelson-Sathi et al. 2011) 19 / 50
Turn “Indo-European and computational cladistics” (Ringe, Warnow and Taylor 2002) “Language-tree divergence times support the Anatolian theory of Indo-European origin” (Gray und Atkinson 2003) “Language classification by numbers” (McMahon und McMahon 2005) “Curious Parallels and Curious Connections: Phylogenetic Thinking in Biology and Historical Linguistics” (Atkinson und Gray 2005) “Automated classification of the world’s languages” (Brown et al. 2008) “Indo-European languages tree by Levenshtein distance” (Serva and Petroni 2008) “Networks uncover hidden lexical borrowing in Indo-European language evolution” (Nelson-Sathi et al. 2011) 19 / 50
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 ... ... ... ... ... ... ... ... ... 20 / 50
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 ... ... ... ... ... ... ... ... ... 20 / 50
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 20 / 50
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 21 / 50
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 22 / 50
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 24 / 50
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). 27 / 50
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). 27 / 50
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. 27 / 50
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 (under review) 28 / 50
Languages Sound Change in the Tukano Languages Tucanoan Quechuan Huitotoan Unclassified / Other Arawakan Cariban Boran Nadahup Mai Colombia Colombia Brazil Brazil Peru Peru Sek Sio Mac Kue Tam Kub Wan Des Pir Kar Mak Tan Kur Pis Yah Yup Bas Bar Tat Yur Sir Tuy Tuk Chacon and List (in press) 31 / 50
Languages Sound Change in the Tukano Languages *P CT Sio Sek Kor Mai Kue Tan Kub Des Sir Yup Bas Mak Tuk Bar Tat Kar Pis Tuy Yur Pir Wan *p #_ h h h h h p/ɸ p p p p h h p p p p p p p p ph *p V_V h h h h h p/ɸ p p p p h h p p p p p p p p p *pʔ #_ pʔ pʔ p ʔb p/b b b b b b b b b b b b b b b b b *pʔ V_V h h h h h vʔp b b b b b b vʔp p p p p p p vʔp vʔp *pʔ ~V_V h h h h h vʔb b b b b b b vʔb b b b b b b vʔb vʔb *pʔ #_V*p h h h h h p p b b b b b b b b b b b b b b *w w w w b w w w w w w w w w w w w w w w w w *m m m m m m b b b b b b b b b b b b b b b b *t #_ t t th t t t t t t t t t t t t t t t t t th *t V_V t t th t t t d d d d t t t t t t t t t t t *t ~V_V t t th t n d d d d d t t t t t t t t t t t *tt V_V t t th t t t t/t∫ t t t t t t t t t t t t t t *tʔ #_ ɖ d r ʔd r/l r d d d d r r d d r r d d d d d *tʔ V_V t t th t t/l t d d d d d d vʔt t t t t t t vʔt vʔt *tʔ ~V_V t t t t n vʔr d d d d d d vʔd d d d d d d vʔd vʔd *s #_ s s s s s h h s s s t∫ t∫ s h h h h s s s s *s V_V s s s s/t∫ s h h s s s t∫ t∫ s h h s t∫ s s s s *ts #_ s s s s/t∫ t/s h h j j ts/s j j j j j j j j j j j *ts V_V s s s s t h h s s ts t∫ t∫ s h h s t∫ s s s s *t∫ #_ t∫ s s s t h h s s t∫ t∫ t∫ s h h h h s s s s *tsʔ #_ sʔ sʔ j d j j h j j j j j j j j j j j j j j *tsʔ #_ i sʔ sʔ j j r/l r h d d d r r d d r r d d d d d *j #_ j j j j j j j j j j j j j j j j j j j j j *j V_V j j j j j j j j j j j/t∫ j/t∫ s h h s t∫ s s s s *n n d n n n d d d d d d d d d d d d d d d d *k #_ k k kh k k k k k k k k k k k k k k k k k kh *k V_V k k kh k k k k g g g k k k k k k k k k k k *k i,e__ k k kh k k k k k k k k k k k k k k k k k t∫ *k ~V_V k k kh k k k Ø g g g k k k k k k k k k k k *kk k k kh k k k k k k k k k k k k k k k k k k *kʔ #_ kʔ kʔ k g / Ø k/g Ø k g g g g g Ø Ø Ø Ø k k k k kh *kʔ V_V k k kh k ND k Ø g g g g g vʔk k k k k k k vʔk vʔk *h Ø Ø Ø Ø h Ø Ø h h h h h h Ø Ø Ø Ø Ø Ø h h *ʔ ʔ ʔ ʔ Ø Ø/h ʔ Ø Ø/ʔ Ø/ʔ Ø Ø Ø ʔ Ø Ø Ø Ø Ø Ø Ø ʔ Chacon and List (in press) 32 / 50
Languages Sound Change in the Tukano Languages *P CT Sio Sek Kor Mai Kue Tan Kub Des Sir Yup Bas Mak Tuk Bar Tat Kar Pis Tuy Yur Pir Wan *p #_ h h h h h p/ɸ p p p p h h p p p p p p p p ph *p V_V h h h h h p/ɸ p p p p h h p p p p p p p p p *pʔ #_ pʔ pʔ p ʔb p/b b b b b b b b b b b b b b b b b *pʔ V_V h h h h h vʔp b b b b b b vʔp p p p p p p vʔp vʔp *pʔ ~V_V h h h h h vʔb b b b b b b vʔb b b b b b b vʔb vʔb *pʔ #_V*p h h h h h p p b b b b b b b b b b b b b b *w w w w b w w w w w w w w w w w w w w w w w *m m m m m m b b b b b b b b b b b b b b b b *t #_ t t th t t t t t t t t t t t t t t t t t th *t V_V t t th t t t d d d d t t t t t t t t t t t *t ~V_V t t th t n d d d d d t t t t t t t t t t t *tt V_V t t th t t t t/t∫ t t t t t t t t t t t t t t *tʔ #_ ɖ d r ʔd r/l r d d d d r r d d r r d d d d d *tʔ V_V t t th t t/l t d d d d d d vʔt t t t t t t vʔt vʔt *tʔ ~V_V t t t t n vʔr d d d d d d vʔd d d d d d d vʔd vʔd *s #_ s s s s s h h s s s t∫ t∫ s h h h h s s s s *s V_V s s s s/t∫ s h h s s s t∫ t∫ s h h s t∫ s s s s *ts #_ s s s s/t∫ t/s h h j j ts/s j j j j j j j j j j j *ts V_V s s s s t h h s s ts t∫ t∫ s h h s t∫ s s s s *t∫ #_ t∫ s s s t h h s s t∫ t∫ t∫ s h h h h s s s s *tsʔ #_ sʔ sʔ j d j j h j j j j j j j j j j j j j j *tsʔ #_ i sʔ sʔ j j r/l r h d d d r r d d r r d d d d d *j #_ j j j j j j j j j j j j j j j j j j j j j *j V_V j j j j j j j j j j j/t∫ j/t∫ s h h s t∫ s s s s *n n d n n n d d d d d d d d d d d d d d d d *k #_ k k kh k k k k k k k k k k k k k k k k k kh *k V_V k k kh k k k k g g g k k k k k k k k k k k *k i,e__ k k kh k k k k k k k k k k k k k k k k k t∫ *k ~V_V k k kh k k k Ø g g g k k k k k k k k k k k *kk k k kh k k k k k k k k k k k k k k k k k k *kʔ #_ kʔ kʔ k g / Ø k/g Ø k g g g g g Ø Ø Ø Ø k k k k kh *kʔ V_V k k kh k ND k Ø g g g g g vʔk k k k k k k vʔk vʔk *h Ø Ø Ø Ø h Ø Ø h h h h h h Ø Ø Ø Ø Ø Ø h h *ʔ ʔ ʔ ʔ Ø Ø/h ʔ Ø Ø/ʔ Ø/ʔ Ø Ø Ø ʔ Ø Ø Ø Ø Ø Ø Ø ʔ h s d d d k k k k k k k k k k k k Chacon and List (in press) 32 / 50
Languages Sound Change in the Tukano Languages *P CT Sio Sek Kor Mai Kue Tan Kub Des Sir Yup Bas Mak Tuk Bar Tat Kar Pis Tuy Yur Pir Wan *p #_ h h h h h p/ɸ p p p p h h p p p p p p p p ph *p V_V h h h h h p/ɸ p p p p h h p p p p p p p p p *pʔ #_ pʔ pʔ p ʔb p/b b b b b b b b b b b b b b b b b *pʔ V_V h h h h h vʔp b b b b b b vʔp p p p p p p vʔp vʔp *pʔ ~V_V h h h h h vʔb b b b b b b vʔb b b b b b b vʔb vʔb *pʔ #_V*p h h h h h p p b b b b b b b b b b b b b b *w w w w b w w w w w w w w w w w w w w w w w *m m m m m m b b b b b b b b b b b b b b b b *t #_ t t th t t t t t t t t t t t t t t t t t th *t V_V t t th t t t d d d d t t t t t t t t t t t *t ~V_V t t th t n d d d d d t t t t t t t t t t t *tt V_V t t th t t t t/t∫ t t t t t t t t t t t t t t *tʔ #_ ɖ d r ʔd r/l r d d d d r r d d r r d d d d d *tʔ V_V t t th t t/l t d d d d d d vʔt t t t t t t vʔt vʔt *tʔ ~V_V t t t t n vʔr d d d d d d vʔd d d d d d d vʔd vʔd *s #_ s s s s s h h s s s t∫ t∫ s h h h h s s s s *s V_V s s s s/t∫ s h h s s s t∫ t∫ s h h s t∫ s s s s *ts #_ s s s s/t∫ t/s h h j j ts/s j j j j j j j j j j j *ts V_V s s s s t h h s s ts t∫ t∫ s h h s t∫ s s s s *t∫ #_ t∫ s s s t h h s s t∫ t∫ t∫ s h h h h s s s s *tsʔ #_ sʔ sʔ j d j j h j j j j j j j j j j j j j j *tsʔ #_ i sʔ sʔ j j r/l r h d d d r r d d r r d d d d d *j #_ j j j j j j j j j j j j j j j j j j j j j *j V_V j j j j j j j j j j j/t∫ j/t∫ s h h s t∫ s s s s *n n d n n n d d d d d d d d d d d d d d d d *k #_ k k kh k k k k k k k k k k k k k k k k k kh *k V_V k k kh k k k k g g g k k k k k k k k k k k *k i,e__ k k kh k k k k k k k k k k k k k k k k k t∫ *k ~V_V k k kh k k k Ø g g g k k k k k k k k k k k *kk k k kh k k k k k k k k k k k k k k k k k k *kʔ #_ kʔ kʔ k g / Ø k/g Ø k g g g g g Ø Ø Ø Ø k k k k kh *kʔ V_V k k kh k ND k Ø g g g g g vʔk k k k k k k vʔk vʔk *h Ø Ø Ø Ø h Ø Ø h h h h h h Ø Ø Ø Ø Ø Ø h h *ʔ ʔ ʔ ʔ Ø Ø/h ʔ Ø Ø/ʔ Ø/ʔ Ø Ø Ø ʔ Ø Ø Ø Ø Ø Ø Ø ʔ *P CT Sio *p #_ h *p V_V h *pʔ #_ pʔ *pʔ V_V h *pʔ ~V_V h Chacon and List (in press) 32 / 50
Languages Modeling Sound Change Chacon and List (in press) The tabular data represents us the sound system of a set of languages in a very abstract form. 33 / 50
Languages Modeling Sound Change Chacon and List (in press) The tabular data represents us the sound system of a set of languages in a very abstract form. It further presents statements on the cognacy of sounds. We can think of sound change as of the transition between different states in phonetic space. 33 / 50
Languages Modeling Sound Change Chacon and List (in press) The tabular data represents us the sound system of a set of languages in a very abstract form. It further presents statements on the cognacy of sounds. We can think of sound change as of the transition between different states in phonetic space. Transitions have directional preferences, they are restricted by the context in which sounds occur in the lexicon of a language, and they are restricted by the system of the language as a whole. 33 / 50
Languages Modeling Sound Change Chacon and List (in press) The tabular data represents us the sound system of a set of languages in a very abstract form. It further presents statements on the cognacy of sounds. We can think of sound change as of the transition between different states in phonetic space. Transitions have directional preferences, they are restricted by the context in which sounds occur in the lexicon of a language, and they are restricted by the system of the language as a whole. We can model this process by using traditional parsimony analyses for phylogenetic reconstruction which we modify by allowing for multiple character states and asymetric scoring matrices that handle transitions. 33 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A A B C D Character state Language A B C D Transitions A B C D A C D B A C D B A C B D A D B C A B C D Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A A B C D Character state Language A B C D Transitions A B C D A C D B A C D B A C B D A D B C A B C D Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A A B C D Character state Language A B C D Transitions A B C D A C D B A C D B A C B D A D B C A B C D Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A A B C D Character state Language A B C D Transitions A B C D A C D B A C D B A C B D A D B C A B C D Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A A B C D Character state Language A B C D Transitions A B C D A C D B A C D B A C B D A D B C A B C D Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A A B C D Character state Language A B D C A D C B B C D A A B C D Transitions A B C D A C D B A C D B A C B D A B D C A D C B A D B C B C D A A B C D Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A A B C D Character state Language A B D C A D C B B C D A A B C D Transitions A B C D A C D B A C D B A C B D A B D C A D C B A D B C B C D A A B C D Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A B C D Character state Language A B C D Transitions B A B C D A B C D A C D B A B D C Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A B C D Character state Language A B C D Transitions B A B C D A B C D A C D B A B D C Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A B C D Character state Language A B C D Transitions C A B C D A B C D A B D C Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A B C D Character state Language A B C D Transitions C A B C D A B C D A B D C Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A B C D Character state Language A B C D Transitions Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States A B C D Character state Language A B C D Transitions A B C D D Chacon and List (in press) 35 / 50
Languages Directed, Weighted, Multi-State Parsimony with Latent States C A B C D A B C D A B D C A A B D C A D C B B C D A A B C D A C D B A C D B A C B D A B D C A D C B A D B C B C D A A B C D B A B C D A B C D A C D B A B D C A B C D D Chacon and List (in press) 35 / 50
Languages Workflow A Matrix of Reflexes tʃ h s x LANGUAGE 1 LANGUAGE 2 LANGUAGE 3 LANGUAGE 4 LANGUAGE 5 tʃ LANGUAGE 6 LANGUAGE 7 LANGUAGE 8 tʃ x s Chacon and List (in press) 36 / 50
Languages Workflow k tʃ h s x B Reflexes (including proto-form) A Matrix of Reflexes tʃ h s x LANGUAGE 1 LANGUAGE 2 LANGUAGE 3 LANGUAGE 4 LANGUAGE 5 tʃ LANGUAGE 6 LANGUAGE 7 LANGUAGE 8 tʃ x s Chacon and List (in press) 36 / 50
Languages Workflow k tʃ h s x k tʃ s → tʃ ʃ ʃ → → k x h h → → s B Reflexes (including proto-form) C Transitions (provided by expert) x → A Matrix of Reflexes tʃ h s x LANGUAGE 1 LANGUAGE 2 LANGUAGE 3 LANGUAGE 4 LANGUAGE 5 tʃ LANGUAGE 6 LANGUAGE 7 LANGUAGE 8 tʃ x s Chacon and List (in press) 36 / 50
Languages Workflow k tʃ h s x k tʃ s → tʃ ʃ ʃ → → k x h h → → s k s tʃ ʃ x h B Reflexes (including proto-form) C Transitions (provided by expert) D Conversion to directed network x → A Matrix of Reflexes tʃ h s x LANGUAGE 1 LANGUAGE 2 LANGUAGE 3 LANGUAGE 4 LANGUAGE 5 tʃ LANGUAGE 6 LANGUAGE 7 LANGUAGE 8 tʃ x s Chacon and List (in press) 36 / 50
Languages Workflow k tʃ h s x k tʃ s → tʃ ʃ ʃ → → k x h h → → s k s tʃ ʃ x h B Reflexes (including proto-form) C Transitions (provided by expert) D Conversion to directed network k x tʃ ʃ s h k 0 1 1 2 3 2 x 50 0 50 50 50 1 tʃ 50 50 0 1 2 3 ʃ 50 50 50 0 1 2 s 50 50 50 50 0 1 h 50 50 50 50 50 0 E Creation of Transition Matrix x → A Matrix of Reflexes tʃ h s x LANGUAGE 1 LANGUAGE 2 LANGUAGE 3 LANGUAGE 4 LANGUAGE 5 tʃ LANGUAGE 6 LANGUAGE 7 LANGUAGE 8 tʃ x s Chacon and List (in press) 36 / 50
Languages Implementation Chacon and List (in press) Software for the computation is implemented in Python. Tree search is carried out with help of a straightforward genetic algorithm. Only heuristics can be used since tree space is to large for more than 10 languages. Application produces interactive applications with help of which the data can be browsed in all its details (see https://digling.github.io/tukano-paper/). 37 / 50
Languages Results Model Parsimony Score Most Parsimonious Trees Homoplasy Reconstruction Success FITCH 107 716 0.67 35% SANKOFF 148 1019 0.82 33% WDT 182 18 1.9 90% Chacon and List (in press) 38 / 50
Languages Results Des Sir Yup Pir Tuk Wan Yur Tuy Pis Kar Tat Bar Tan Kub Mak Bas Kue Mai Kor Sek Sio Western Tukano East-Eastern Tukano South-Eastern Tukano West-Eastern Tukano 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 unweighted, no directional preferences FITCH Chacon and List (in press) 38 / 50
Languages Results Tan Tat Kar Bar Pis Tuy Yur Pir Tuk Wan Kub Sir Des Yup Mak Bas Kue Mai Kor Sek Sio Western Tukano East-Eastern Tukano South-Eastern Tukano West-Eastern Tukano 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.58 1.0 1.0 1.0 1.0 0.5 1.0 0.99 1.0 1.0 weighted, no directional preferences SANKOFF Chacon and List (in press) 38 / 50
Languages Results Tan Kub Pis Tuy Yur Kar Tat Bar Pir Tuk Wan Yup Sir Des Mak Bas Sek Sio Mai Kor Kue Western Tukano East-Eastern Tukano South-Eastern Tukano West-Eastern Tukano 1.0 0.67 0.67 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 weighted, directional preferences DiWeST Chacon and List (in press) 38 / 50
Languages Examples vʔp h pʔ b p → → → → Sio Sek Kor Mai Kue Tan Kub Des Sir Yup Bas Mak Wan Pir Kar Pis Tuy Yur Tuk Tat Bar Chacon and List (in press) 39 / 50
Languages Examples vʔp h pʔ b p → → → → Sio Sek Kor Mai Kue Tan Kub Des Sir Yup Bas Mak Wan Pir Kar Pis Tuy Yur Tuk Tat Bar Chacon and List (in press) 39 / 50
Languages Examples vʔp h pʔ b p → → → → Sio Sek Kor Mai Kue Tan Kub Des Sir Yup Bas Mak Wan Pir Kar Pis Tuy Yur Tuk Tat Bar Chacon and List (in press) 39 / 50
Languages Examples vʔp h pʔ b p → → → → Sio Sek Kor Mai Kue Tan Kub Des Sir Yup Bas Mak Wan Pir Kar Pis Tuy Yur Tuk Tat Bar Chacon and List (in press) 39 / 50
Languages Examples vʔp h pʔ b p → → → → Sio Sek Kor Mai Kue Tan Kub Des Sir Yup Bas Mak Wan Pir Kar Pis Tuy Yur Tuk Tat Bar Chacon and List (in press) 39 / 50
Languages Examples vʔp h pʔ b p → → → → Sio Sek Kor Mai Kue Tan Kub Des Sir Yup Bas Mak Wan Pir Kar Pis Tuy Yur Tuk Tat Bar Chacon and List (in press) 39 / 50
Languages Examples vʔp h pʔ b p → → → → Sio Sek Kor Mai Kue Tan Kub Des Sir Yup Bas Mak Wan Pir Kar Pis Tuy Yur Tuk Tat Bar Chacon and List (in press) 39 / 50
Languages Examples vʔp h pʔ b p → → → → Sio Sek Kor Mai Kue Tan Kub Des Sir Yup Bas Mak Wan Pir Kar Pis Tuy Yur Tuk Tat Bar Chacon and List (in press) 39 / 50
Dialects Lexical Change in the Chinese Dialects 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 - - - - - - List (under review) 40 / 50
Dialects Lexical Change in the Chinese Dialects 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 - - - - - - 1 2 3 4 number of morphemes per word 0.0 0.2 0.4 0.6 0.8 1.0 relative frequency all words nouns Compounds in the basic vocabulary (Swadesh1952) across 23 Chinese dialects (data by Hamed and Wang 2006) 30% 50% List (under review) 40 / 50
Dialects Lexical Change in the Chinese Dialects 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" List (under review) 40 / 50
Dialects Lexical Change in the Chinese Dialects Fúzhōu Měixiàn Guǎngzhōu Běijīng INNO VATIO N INNO VATIO N INNO VATIO N BO RRO W ING LO SS INNO VATIO N INNO VATIO N List (under review) 40 / 50
Dialects The Crux with Partial Cognacy List (under review) We cannot model partial cognacy sufficiently when restricting our analyses to binary gain-loss models, as they are common in Bayesian phylogenetic analyses. Partial cognacy is too frequent to be ignored, not only in Sino-Tibetan languages, but also in many other language families (Austro-Asiatic, Hmong-Mien, Tai-Kadai). If we define binary cognacy on the basis of common morphemes, the majority of the items in our datasets will become cognate and we will loose a great deal of the phylogenetic signal. If we define binary cognacy on the basis of identical morphemes in all words, the majority of the items in our datasets will become non-cognate, and we will again loose a great deal of phylogenetic signal. 41 / 50
Dialects The Crux with Partial Cognacy When dealing with language families in which compounding and morphological derivation is so frequent that it covers more than 30 percent of the basic vocabulary of the languages, we need to incorporate partial cognacy our phylogenetic models. List (under review) 42 / 50
Dialects Directed, Weighted, Multi-State Parsimony and Lexical Change A AC ABD AB A LANGUAGE 1 LANGUAGE 2 LANGUAGE 3 LANGUAGE 4 List (under review) 43 / 50
Dialects Directed, Weighted, Multi-State Parsimony and Lexical Change AC ABD AB A LANGUAGE 1 LANGUAGE 2 LANGUAGE 3 LANGUAGE 4 List (under review) 43 / 50
Dialects Directed, Weighted, Multi-State Parsimony and Lexical Change AC ABD AB A LANGUAGE 1 LANGUAGE 2 LANGUAGE 3 LANGUAGE 4 List (under review) 43 / 50
Dialects Testing the Method on Chinese Dialect Data List (under review) 22 Chinese dialect varieties with Chinese character readings (basis vocabulary, data by Hamed and Wang 2006), 57 nouns in which Ancient Chinese forms expressing the concepts are known to us and are are also still preserved in at least one Chinese dialect, three reference phylogenies (Arbre by Sagart 2011, Southern Chinese by Norman 2003, Shùxíngtú by Yóu 1992), four models of lexical change (binary, unweighted, weighted, weighted and directed). 45 / 50
in historical linguistics, we need to increase the complexity of our models. In order to profit from existing biological approaches, we need to foster a close collaboration between computational linguistics, computational biologists, but also and especially between classical biologists and linguistics. Without the appropriate datasets for testing, and software that is free for all scientists to use, all efforts will be in vain. Computational linguists should make sure that they standardize their data and make all of the code they use for their analyses freely available along with their publications. 49 / 50
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