List Research Group “Computer-Assisted Language Comparison” Department of Linguistic and Cultural Evolution Max-Planck Institute for the Science of Human History Jena, Germany 2017-10-24 very long title P(A|B)=P(B|A)... 1 / 29
1863) walkman Indo-European Germanic Old English English p f f f ə a æ ɑː t d d ð eː eː e ə r r r r Germanic German English iPod Comparative Linguistics 2 / 29
ə a æ ɑː t d d ð eː eː e ə r r r r Germanic German English walkman "All languages change, as long as they exist." (August Schleicher 1863) Comparative Linguistics 2 / 29
ə a æ ɑː t d d ð eː eː e ə r r r r Germanic German English iPod "All languages change, as long as they exist." (August Schleicher 1863) Comparative Linguistics 2 / 29
ə a æ ɑː t d d ð eː eː e ə r r r r Germanic German English iPod "All languages change, as long as they exist." (August Schleicher 1863) Comparative Linguistics 2 / 29
ə a æ ɑː t d d ð eː eː e ə r r r r walkman L₁ L₁ L₁ L₁ L₁ "All languages change, as long as they exist." (August Schleicher 1863) Comparative Linguistics 2 / 29
ə a æ ɑː t d d ð eː eː e ə r r r r walkman L₁ L₁ L₁ L₁ L₁ "All languages change, as long as they exist." (August Schleicher 1863) Comparative Linguistics 2 / 29
ə a æ ɑː t d d ð eː eː e ə r r r r walkman L₁ L₁ L₁ L₁ L₁ "All languages change, as long as they exist." (August Schleicher 1863) Comparative Linguistics 2 / 29
ə a æ ɑː t d d ð eː eː e ə r r r r walkman L₁ L₁ L₁ "All languages change, as long as they exist." (August Schleicher 1863) Comparative Linguistics 2 / 29
ə a æ ɑː t d d ð eː eː e ə r r r r walkman L₂ L₁ L₃ "All languages change, as long as they exist." (August Schleicher 1863) Comparative Linguistics 2 / 29
Correspondences Bola six kʰ j a u ʔ ⁵⁵ Bola Maru Freq. a(a̰) a(a̰) 3 x u u 3 x ʔ k 3 x j j 2 x k(ʰ) k(ʰ) 2 x ⁵⁵ ⁵⁵ 2 x ³¹ ³¹ 1 x Maru six kʰ j a u k ⁵⁵ Bola lip k a̰ u ʔ ⁵⁵ Maru lip k a̰ u k ⁵⁵ Bola man j a u ʔ ³¹ Maru man j a u k ³¹ 11 / 29
Maru Rangoon d Ø Ø d t t t t tʰ tʰ tʰ tʰ tθ Ø Ø tθ ts ts ts Ø tsʰ tsʰ tsʰ Ø tʃ tʃ tʃ Ø tʃʰ tʃʰ tʃʰ Ø s s s s sʰ Ø Ø sʰ ɕ Ø Ø ɕ ʃ ʃ ʃ t ts t tʰ tθ 12 / 29
Maru Rangoon d Ø Ø d t t t t tʰ tʰ tʰ tʰ tθ Ø Ø tθ ts ts ts Ø tsʰ tsʰ tsʰ Ø tʃ tʃ tʃ Ø tʃʰ tʃʰ tʃʰ Ø s s s s sʰ Ø Ø sʰ ɕ Ø Ø ɕ ʃ ʃ ʃ t ts t tʰ tθ "tooth" Bola t u i ⁵⁵ Maru ts ɔ i ³¹ Rangoon tθ w a ⁵⁵ 12 / 29
Maru Rangoon d Ø Ø d t t t t tʰ tʰ tʰ tʰ tθ Ø Ø tθ ts ts ts Ø tsʰ tsʰ tsʰ Ø tʃ tʃ tʃ Ø tʃʰ tʃʰ tʃʰ Ø s s s s sʰ Ø Ø sʰ ɕ Ø Ø ɕ ʃ ʃ ʃ t t t tʰ t 12 / 29
Maru Rangoon d Ø Ø d t t t t tʰ tʰ tʰ tʰ tθ Ø Ø tθ ts ts ts Ø tsʰ tsʰ tsʰ Ø tʃ tʃ tʃ Ø tʃʰ tʃʰ tʃʰ Ø s s s s sʰ Ø Ø sʰ ɕ Ø Ø ɕ ʃ ʃ ʃ t t t tʰ t "wing" Bola t a u ŋ ⁵⁵ Maru t a u ŋ ³¹ Rangoon t ɑ u ∼ ²² 12 / 29
Hittite Sanskrit Avestan Greek Latin Gothic Old Church Slavonic Lithuanian Old Irish Armenian Tocharian *p p p p f p p f b p p Ø h w Ø p *b b p b bβ b b p b b b p p *bʰ b p bʱ/bh bβ pʰ/ph f b b b b b b p *t t t t θ t t θ/þ d t t t tʼ j/y t tʃ/c *d d t d d ð d d t d d d t ts ʃ/ś *dʰ d t dʰ/dh h d ð tʰ/th f d b d d d d t t tʃ/c ... ... ... ... ... ... ... ... ... ... ... ... *kʷ kʷ/ku k c k c k p t kʷ/qu hʷ/hw g k tʃ/č k c kʼ tʃʼ/čʼ k ʃʲ/ś *gʷ kʷ/u g j g j g b d gʷ/gu u q g ʒ/ž z g b k k ś *gʷʰ kʷ/ku gʷ/gu gʱ/gh h g j pʰ/ph tʰ/th kʰ/kh f gʷ/gu u g b g ʒ/ž z g g g dʒ/ǰ k ʃʲ/ś Clackson (2007: 37) 14 / 29
patterns in linguistics are a way to encode mappings across several different alphabets they are usually inferred manually, by inspecting “correspondence sets” (Clackson 2007: 29f) of words (i.e., cognate sets with recurring sounds) 15 / 29
patterns in linguistics are a way to encode mappings across several different alphabets they are usually inferred manually, by inspecting “correspondence sets” (Clackson 2007: 29f) of words (i.e., cognate sets with recurring sounds) the main problem of correspondence pattern identification is the handling of missing data, since not all cognate sets will necessarily contain reflexes from each of the languages under investigation 15 / 29
Patterns the main idea for the correspondence pattern inference algorithm is to derive a graph from correspondence sets in which each individual correspondence set (a site in an aligned cognate set) is a node, and links between nodes are drawn between compatible correspondence sets 16 / 29
Patterns the main idea for the correspondence pattern inference algorithm is to derive a graph from correspondence sets in which each individual correspondence set (a site in an aligned cognate set) is a node, and links between nodes are drawn between compatible correspondence sets if two correspondence sets are compatible, this means that they have identical non-missing values for at least one language and no conflicting data for any of the languages 16 / 29
Patterns the main idea for the correspondence pattern inference algorithm is to derive a graph from correspondence sets in which each individual correspondence set (a site in an aligned cognate set) is a node, and links between nodes are drawn between compatible correspondence sets if two correspondence sets are compatible, this means that they have identical non-missing values for at least one language and no conflicting data for any of the languages if two or more correspondence sets are compatible, we can impute missing values by combining them 16 / 29
Sites Cognate Set L1 L2 L3 L4 L5 L6 L7 L8 “hand-1” p p p Ø f f Ø p “foot-1” p p p p f f p p ⊠ compatible □ incompatible Cognate Set L1 L2 L3 L4 L5 L6 L7 L8 “hand-1” p p p Ø f f Ø p “leg-1” p p f pf f f p p □ compatible ⊠ incompatible 17 / 29
Burmish languages (spoken in China and Myanmar, taken from Hill and List 2017) 240 concepts 855 partial cognate sets 728 cross-semantic partial cognate sets (covering one and more concepts) 18 / 29
Burmish languages (spoken in China and Myanmar, taken from Hill and List 2017) 240 concepts 855 partial cognate sets 728 cross-semantic partial cognate sets (covering one and more concepts) 218 valid cognate sets (residues in more than one language) 18 / 29
tʰ tʰ tʰ tʰ pʰ tʰ tʰ pʰ tʰ pʰ ŋ ŋ ŋ ŋ ŋ ŋ ŋ ŋ tsʰ tʃʰ tsʰ tʃʰ tʃʰ tsʰ tsʰ tsʰ v j f j v v n◌̥ ŋ - ŋ ŋ ŋ ŋ ʃ ʃ ʃ ʃ ʃ ʃ ʃ ʃ s s tʃ tʃ s tʃ tʃ tʃ tʃ tʃ tʃ tʃ x x tʃ x x x t t t t t t ʃ ʃ ʃ ʃ ʃ ʃ ts ts ts ts ts ts ts ts ts ts t t t t t t t t p pʰ m m p m m pʰ p m s s s s s s s s s s n l l l l l l l l l l l l s s s s s s p p p p p p p p p p pʰ p p p p p p m m m m m m m l l l l l l l l j j - j - j j - j k ɣ ɣ ɣ ɣ ʐ ɣ j j v j - w v j k k k k k k k k k kʰ kʰ kʰ kʰ kʰ kʰ kʰ k k k k k kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ x x x x n◌̥ n n n n n n n n n ŋ n n n n n n n n n k k k k k k m m m m m m m m m n◌̥ n n n - ŋ n ŋ nʲ m m m m m m m m 20 / 29
tʰ tʰ pʰ tʰ tʰ pʰ pʰ tʰ tʰ pʰ ŋ ŋ ŋ ŋ ŋ ŋ ŋ ŋ tsʰ tsʰ tʃʰ tsʰ tsʰ tʃʰ tʃʰ tsʰ j v f j v v ŋ - ŋ n◌̥ ŋ ŋ ŋ ʃ ʃ s ʃ ʃ ʃ ʃ ʃ ʃ s tʃ tʃ tʃ tʃ tʃ s tʃ tʃ tʃ tʃ x x tʃ x x x t t t t t t ʃ ʃ ʃ ʃ ʃ ʃ ts ts ts ts ts ts ts ts ts ts t t t t t t t t m m m pʰ pʰ p p p m m s s s s s s s s s s n l l l l l l l l l l l l s s s s s s p p p p p p p p p p p p p p p pʰ p m m m m m m m l l l l l l l l - - j j j j j - j k ɣ ɣ ɣ ɣ ʐ ɣ w j - v v j j j k k k k k k k k k kʰ kʰ kʰ kʰ kʰ kʰ kʰ k k k k k kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ x x x x n n◌̥ n n n n n ŋ n n n n n n n n n n n n k k k k k k m m m m m m m m - n n ŋ ŋ n n◌̥ n nʲ m m m m m m m m m 20 / 29
fully compatible clusters (i.e., only cliques in our net- work of correspondence sets) can represent true sound corre- spondence patterns (if sound change is regular). 20 / 29
as Clique Cover Problem The clique cover problem (also called clique partitioning problem, see Bhasker 1991) is the inverse of the famous graph coloring problem and has been shown to be NP-hard. 21 / 29
as Clique Cover Problem The clique cover problem (also called clique partitioning problem, see Bhasker 1991) is the inverse of the famous graph coloring problem and has been shown to be NP-hard. The goal of the problem is to split a graph into the smallest number of cliques in which each node is represented by exactly one clique. 21 / 29
as Clique Cover Problem The clique cover problem (also called clique partitioning problem, see Bhasker 1991) is the inverse of the famous graph coloring problem and has been shown to be NP-hard. The goal of the problem is to split a graph into the smallest number of cliques in which each node is represented by exactly one clique. We assume (but we cannot formally prove it) that the clique cover of our graph of compatible correspondence sets will correspond to the optimal set of sound correspondence patterns in our data. 21 / 29
as Clique Cover Problem The clique cover problem (also called clique partitioning problem, see Bhasker 1991) is the inverse of the famous graph coloring problem and has been shown to be NP-hard. The goal of the problem is to split a graph into the smallest number of cliques in which each node is represented by exactly one clique. We assume (but we cannot formally prove it) that the clique cover of our graph of compatible correspondence sets will correspond to the optimal set of sound correspondence patterns in our data. By applying an approximation algorithm to infer a near-optimal clique cover of our data of aligned cognate sets, we can infer the most frequently recurring correspondence patterns in our data. 21 / 29
Cover tʃʰ s ʃ tʃʰ x j n◌̥ n n ŋ tsʰ tsʰ tsʰ tʃʰ pʰ pʰ pʰ j w x v x v x j x n n x x x n◌̥ x n m m m n kʰ kʰ kʰ tʰ tʰ tʰ kʰ tʰ tʰ tʰ tʰ - - - pʰ pʰ p j ɣ ɣ ɣ ɣ j j j n n n n m n n n n m m m m m m m m m m l l l p l l l l l l l kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ ʃ s s s k ʃ ʃ k s s k s ʃ ʃ ʃ t t t t t t t t t t t m m m m m m m m m p p p p p p p p p p p p p p p k k s kʰ kʰ k ʃ ŋ n m l t t l - tsʰ f tʃ k n n l ʃ tsʰ l s m t p k n kʰ m j m v j s tʃ n ts m l ŋ k kʰ v ʃ ʐ ʃ n k j - tʃ pʰ s v m k ŋ ŋ - n n l n◌̥ ŋ ŋ l l l l ʃ ʃ ts ʃ k s k s s s s s ts ts ts ts ts ts j k j ɣ k ŋ ŋ ŋ ŋ ŋ ŋ ŋ s s tʃ tʃ tʃ tʃ tʃ tʃ tʃ k k k k k ts p pʰ ts nʲ ŋ n ŋ k 22 / 29
Cover tʰ tʰ tʰ pʰ tʰ tʰ pʰ pʰ tʰ tʰ pʰ ŋ ŋ ŋ ŋ ŋ ŋ ŋ ŋ tsʰ tsʰ tʃʰ tsʰ tsʰ tʃʰ tʃʰ tsʰ j v f j v v ŋ - ŋ n◌̥ ŋ ŋ ŋ ʃ ʃ s ʃ ʃ ʃ ʃ ʃ ʃ s tʃ tʃ tʃ tʃ tʃ s tʃ tʃ tʃ tʃ x x tʃ x x x t t t t t t ʃ ʃ ʃ ʃ ʃ ʃ ts ts ts ts ts ts ts ts ts ts t t t t t t t t m m m pʰ pʰ p p p m m s s s s s s s s s s n l l l l l l l l l l l l s s s s s s p p p p p p p p p p p p p p p pʰ p m m m m m m m l l l l l l l l - - j j j j j - j k ɣ ɣ ɣ ɣ ʐ ɣ w j - v v j j j k k k k k k k k k kʰ kʰ kʰ kʰ kʰ kʰ kʰ k k k k k kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ kʰ x x x x n n◌̥ n n n n n ŋ n n n n n n n n n n n n k k k k k k m m m m m m m m - n n ŋ ŋ n n◌̥ n nʲ m m m m m m m m m 22 / 29
104 initial consonant patterns (48 with more than one reflex, the rest highly irregular) many patterns correspond to well-known proto-sounds in the data 23 / 29
104 initial consonant patterns (48 with more than one reflex, the rest highly irregular) many patterns correspond to well-known proto-sounds in the data some cliques are unintuitive 23 / 29
Irregular patterns: result in part from problems in cognate coding (homology assessment) result in part from sparseness of data result in part from the exactness of the algorithm 24 / 29
Irregular patterns: result in part from problems in cognate coding (homology assessment) result in part from sparseness of data result in part from the exactness of the algorithm Unintuitive patterns: 24 / 29
Irregular patterns: result in part from problems in cognate coding (homology assessment) result in part from sparseness of data result in part from the exactness of the algorithm Unintuitive patterns: result in part from the greediness of the algorithm 24 / 29
the greediness of the algorithm by adding a secondary check of cliques (calculate consensus, re-assign all alignment sites to all compatible consensus sequences, count the instances) 25 / 29
the greediness of the algorithm by adding a secondary check of cliques (calculate consensus, re-assign all alignment sites to all compatible consensus sequences, count the instances) allow for non-perfect cliques in which compatibility is allowed to deviate to a certain degree (e.g., one irregular cell) 25 / 29
the greediness of the algorithm by adding a secondary check of cliques (calculate consensus, re-assign all alignment sites to all compatible consensus sequences, count the instances) allow for non-perfect cliques in which compatibility is allowed to deviate to a certain degree (e.g., one irregular cell) provide a more fine-grained checking of proposed cliques by counting columns suffering from missing data 25 / 29
the greediness of the algorithm by adding a secondary check of cliques (calculate consensus, re-assign all alignment sites to all compatible consensus sequences, count the instances) allow for non-perfect cliques in which compatibility is allowed to deviate to a certain degree (e.g., one irregular cell) provide a more fine-grained checking of proposed cliques by counting columns suffering from missing data allow for a direct checking and correcting of patterns by the experts 25 / 29
inference of correspondence patterns is a first attempt to account for systemic aspects of sound change in a rigorous manner in contrast to many approaches proposed so far, it does not require family trees in any form, networks are just enough, but the patterns inferred can be used to study tree-like aspects of evolution (Chacon and List 2015), 28 / 29
inference of correspondence patterns is a first attempt to account for systemic aspects of sound change in a rigorous manner in contrast to many approaches proposed so far, it does not require family trees in any form, networks are just enough, but the patterns inferred can be used to study tree-like aspects of evolution (Chacon and List 2015), the algorithm is surely a good start, but it needs to be improved in several ways 28 / 29
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