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Computer-Assisted Approaches to Linguistic
Reconstruction
A Case Study from the Burmish Languages
Johann-Mattis List¹ and Nathan W. Hill²
2017-07-21
¹ Max Planck Institute for the Science of Human History, ² SOAS, University of London
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Outline
1. Introduction
2. Phonological Reconstruction
3. Computer-Assisted Phonological Reconstruction
4. Examples
5. Outlook
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Introduction
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Etymology 2.0?
Historical linguistics after the quantitative turn:
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Etymology 2.0?
Historical linguistics after the quantitative turn:
• Quantitative methods in historical linguistics have received much
attention of late,
4
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Etymology 2.0?
Historical linguistics after the quantitative turn:
• Quantitative methods in historical linguistics have received much
attention of late,
• but only a few (if any) of the new methods have addressed
long-standing problems of classical linguistics,
4
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Etymology 2.0?
Historical linguistics after the quantitative turn:
• Quantitative methods in historical linguistics have received much
attention of late,
• but only a few (if any) of the new methods have addressed
long-standing problems of classical linguistics,
• and as a result, many classical linguists are very sceptical of the new
approaches.
4
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Etymology 3.0?
Towards a “qualitative turn” in computational historical linguistics:
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Etymology 3.0?
Towards a “qualitative turn” in computational historical linguistics:
• Instead of blaming computers for our misery (no funding, institutes
are begin shut down, etc.), we should start seeing computers as a
chance to address the important questions which we have not solved
in 200 years of research...
5
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Etymology 3.0?
Towards a “qualitative turn” in computational historical linguistics:
• Instead of blaming computers for our misery (no funding, institutes
are begin shut down, etc.), we should start seeing computers as a
chance to address the important questions which we have not solved
in 200 years of research...
• But we don’t a framework in which computers do our work for us,
instead, we need a framework, where we tell computers to some
work for us, in order to render our research more explicit, more
efficient, and more rigorous.
5
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Computer-Assisted Language Comparison
very
long
title
P(A|B)=P(B|A)...
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Computer-Assisted Language Comparison
CALC (MPI-SHH, Jena)
• computational formalization of the classical methods for historical
language comparison
• establish a close collaboration between computational and classical
historical linguistics by providing data in human- and
machine-readable form
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Computer-Assisted Language Comparison
CALC (MPI-SHH, Jena)
• computational formalization of the classical methods for historical
language comparison
• establish a close collaboration between computational and classical
historical linguistics by providing data in human- and
machine-readable form
ASIA (SOAS, London)
• reconstruction of Proto-Burmish
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The Burmish Languages
8
Hill and List (forthcoming)
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The Burmish Etymological Database (BED)
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The Burmish Etymological Database (BED)
problem current etymological accounts on Proto-Burmish have
many problems (no lexical reconstruction, insufficient
phonological reconstruction, unclear data, intransparent
methodology)
9
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The Burmish Etymological Database (BED)
problem current etymological accounts on Proto-Burmish have
many problems (no lexical reconstruction, insufficient
phonological reconstruction, unclear data, intransparent
methodology)
goal compile an etymological database of Proto-Burmish
9
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The Burmish Etymological Database (BED)
problem current etymological accounts on Proto-Burmish have
many problems (no lexical reconstruction, insufficient
phonological reconstruction, unclear data, intransparent
methodology)
goal compile an etymological database of Proto-Burmish
procedure BED as litmus test for CALC:
• make the Proto-Burmish Etymological Database
project a first test for the CALC framework
• use existing computational methods to pre-analyze
the data
• develop interfaces to allow for correction and
inspection by the experts
9
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Where are you now with BED?
⊠ Develop computer-assisted workflows to create and curate data in
human- and machine-readable form.
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Where are you now with BED?
⊠ Develop computer-assisted workflows to create and curate data in
human- and machine-readable form.
⊠ Develop computer-assisted workflow for partial cognate detection
and alignments.
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Where are you now with BED?
⊠ Develop computer-assisted workflows to create and curate data in
human- and machine-readable form.
⊠ Develop computer-assisted workflow for partial cognate detection
and alignments.
⊟ Develop methods for automatic phonological reconstruction and
workflows for the correction of the results by experts (→ THIS
TALK).
10
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Where are you now with BED?
⊠ Develop computer-assisted workflows to create and curate data in
human- and machine-readable form.
⊠ Develop computer-assisted workflow for partial cognate detection
and alignments.
⊟ Develop methods for automatic phonological reconstruction and
workflows for the correction of the results by experts (→ THIS
TALK).
□ Develop methods for lexical reconstruction (first ideas, not shown in
this talk).
10
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Where are you now with BED?
⊠ Develop computer-assisted workflows to create and curate data in
human- and machine-readable form.
⊠ Develop computer-assisted workflow for partial cognate detection
and alignments.
⊟ Develop methods for automatic phonological reconstruction and
workflows for the correction of the results by experts (→ THIS
TALK).
□ Develop methods for lexical reconstruction (first ideas, not shown in
this talk).
□ Write a data-driven etymological dictionary (table of contents is
half-written).
10
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Phonological Reconstruction
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What is phonological reconstruction?
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What is phonological reconstruction?
• Phonological reconstruction is primarily understood as the
reconstruction of the sound system of a language not reflected in
written sources.
12
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What is phonological reconstruction?
• Phonological reconstruction is primarily understood as the
reconstruction of the sound system of a language not reflected in
written sources.
• More specifically, however, we see phonological reconstruction as the
task of reconstructing major patterns of sound change which allow
us to reconstruct tentative proto-forms from cognate sets, regardless
of whether those words were really present in the Ursprache or what
those forms meant.
12
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What is phonological reconstruction?
• Phonological reconstruction is primarily understood as the
reconstruction of the sound system of a language not reflected in
written sources.
• More specifically, however, we see phonological reconstruction as the
task of reconstructing major patterns of sound change which allow
us to reconstruct tentative proto-forms from cognate sets, regardless
of whether those words were really present in the Ursprache or what
those forms meant.
• The task of lexical reconstruction follows phonological
reconstruction in projecting full lexemes to the Ursprache, thereby
also assessing their meaning, and whether it is reasonable to
reconstruct them at all.
12
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Classical Workflow (Comparative Method)
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Classical Workflow (Comparative Method)
• assemble cognate sets and sound correspondences by comparing
data on different languages
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Classical Workflow (Comparative Method)
• assemble cognate sets and sound correspondences by comparing
data on different languages
• infer sound change processes (“sound laws”) from the inferred sound
correspondence patterns
13
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Classical Workflow (Comparative Method)
• assemble cognate sets and sound correspondences by comparing
data on different languages
• infer sound change processes (“sound laws”) from the inferred sound
correspondence patterns
• explain exceptions by:
13
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Classical Workflow (Comparative Method)
• assemble cognate sets and sound correspondences by comparing
data on different languages
• infer sound change processes (“sound laws”) from the inferred sound
correspondence patterns
• explain exceptions by:
• refining inferred sound change processes (cf. “Verner’s law”)
• borrowing (“substratum influence”)
• analogy (leftovers)
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Computer-Based Automatic Approaches
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Computer-Based Automatic Approaches
Problems of computer-based approaches:
15
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Computer-Based Automatic Approaches
Problems of computer-based approaches:
(a) fail to model sound change as a systemic process (each column of an
alignment is counted independently)
15
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Computer-Based Automatic Approaches
Problems of computer-based approaches:
(a) fail to model sound change as a systemic process (each column of an
alignment is counted independently)
(b) fail to make use of linguistic knowledge on the directionality of
sound change processes and have to rely on phylogenies
15
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Computer-Based Automatic Approaches
Problems of computer-based approaches:
(a) fail to model sound change as a systemic process (each column of an
alignment is counted independently)
(b) fail to make use of linguistic knowledge on the directionality of
sound change processes and have to rely on phylogenies
(c) fail to handle unattested sounds, as only sounds which are in the
data can be reconstructed
15
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Computer-Assisted Phonological
Reconstruction
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General Workflow
Preliminary steps:
• partial cognate detection and partial phonetic alignment (List et al.
2016) with manual refinement
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General Workflow
Preliminary steps:
• partial cognate detection and partial phonetic alignment (List et al.
2016) with manual refinement
• preliminary identification of cross-semantic cognates based on partial
colexifications (Hill and List forthcoming)
17
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General Workflow
Preliminary steps:
• partial cognate detection and partial phonetic alignment (List et al.
2016) with manual refinement
• preliminary identification of cross-semantic cognates based on partial
colexifications (Hill and List forthcoming)
Phonological reconstruction:
17
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General Workflow
Preliminary steps:
• partial cognate detection and partial phonetic alignment (List et al.
2016) with manual refinement
• preliminary identification of cross-semantic cognates based on partial
colexifications (Hill and List forthcoming)
Phonological reconstruction:
• sound correspondence pattern identification (List et al. in prep.)
17
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General Workflow
Preliminary steps:
• partial cognate detection and partial phonetic alignment (List et al.
2016) with manual refinement
• preliminary identification of cross-semantic cognates based on partial
colexifications (Hill and List forthcoming)
Phonological reconstruction:
• sound correspondence pattern identification (List et al. in prep.)
• automatic reconstruction using weighted directed networks (→ this
talk)
17
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Detailed Workflow: Preliminary Steps
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
Partial cognate detection: List, Lopez, and Bapteste (2016)
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Detailed Workflow: Preliminary Steps
EDICTOR tool: List (2017)
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Detailed Workflow: Preliminary Steps
Language 'mountain' 'dog' 'thunder' 'wolf' 'bear (n.)'
Atsi pum⁵¹ kʰui²¹ mau²¹ mjiŋ⁵¹ vam⁵¹ kʰui²¹ mo⁵⁵ vam⁵¹
mountain dog sky + thunder bear + dog + m-suff. bear
Bola pam⁵⁵ kʰui³⁵ mau³¹ mjaŋ⁵⁵ mjaŋ⁵⁵ kʰui³⁵ vɛ⁵
⁵⁵
mountain dog sky + thunder thunder + dog bear
Lashi pɔm³¹ kʰui⁵⁵ mou³³ kɔm³³ wɔm³¹ kʰui⁵⁵ wɔm³¹
mountain dog sky + thunderB bear + dog bear
Maru pam³¹ lə¹
³¹ kʰa³⁵ muk⁵⁵ kum³¹ mjaŋ³¹ kʰa³⁵ vɛ⁵
³¹
mountain ? + dog sky + thunderB thunder + dog bear
Achang
pum⁵⁵ xui³¹ mau³¹ ʐau³¹ pum⁵⁵ xui³¹ ɔm⁵⁵
mountain dog sky + thunderC mountain + dog bear
Morpheme Annotation (Hill and List forthc.) 20
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Detailed Workflow: Sound Correspondence Pattern Inference
Clackson (2007: 37)
21
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Detailed Workflow: Sound Correspondence Pattern Inference
Sound Correspondence Patterns and Phonological Reconstruction:
22
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Detailed Workflow: Sound Correspondence Pattern Inference
Sound Correspondence Patterns and Phonological Reconstruction:
• the most traditional way to reconstruct in the comparative-method
framework is to infer patterns of regular sound correspondences
across a set of languages and then assign proto-forms for each
distinct pattern
22
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Detailed Workflow: Sound Correspondence Pattern Inference
Sound Correspondence Patterns and Phonological Reconstruction:
• the most traditional way to reconstruct in the comparative-method
framework is to infer patterns of regular sound correspondences
across a set of languages and then assign proto-forms for each
distinct pattern
• correspondence patterns are usually inferred manually, by inspecting
“correspondence sets” (Clackson 2007: 29f) of words (i.e., cognate
sets with recurring sounds)
22
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Detailed Workflow: Sound Correspondence Pattern Inference
Sound Correspondence Patterns and Phonological Reconstruction:
• the most traditional way to reconstruct in the comparative-method
framework is to infer patterns of regular sound correspondences
across a set of languages and then assign proto-forms for each
distinct pattern
• correspondence patterns 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
22
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Detailed Workflow: Sound Correspondence Pattern Inference
Graphs of Compatible Correspondence Sets:
23
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Detailed Workflow: Sound Correspondence Pattern Inference
Graphs of Compatible Correspondence Sets:
• 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
23
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Detailed Workflow: Sound Correspondence Pattern Inference
Graphs of Compatible Correspondence Sets:
• 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
23
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Detailed Workflow: Sound Correspondence Pattern Inference
Graphs of Compatible Correspondence Sets:
• 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
23
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Detailed Workflow: Sound Correspondence Pattern Inference
Cognate Set L1 L2 L3 L4 L5 L6 L7 L8
“hand-1” p p p f f p
“foot-1” p p p p f f p p
⊠ compatible
□ incompatible
24
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Detailed Workflow: Sound Correspondence Pattern Inference
Cognate Set L1 L2 L3 L4 L5 L6 L7 L8
“hand-1” p p p f f 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 f f p
“leg-1” p p f pf f f p p
□ compatible
⊠ incompatible
24
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Detailed Workflow: Sound Correspondence Pattern Inference
s
s
s
s
s
s
s
s
s
s
s
k
s
-
x
x
x
x
k
k
k
k
k
k
kʰ
k
ʃ
k
ʃ
ʃ
x
ɣ
ʃ
k
k
k
k
k
k
k
s
s
s
s
s
n
s
s
k
k
k
k
ʃ
ʃ
s
s
ʃ
ʃ
tʃ
tʃ
tʃ
tʃ
tʃ
tʃ
tʃ
tʃ
tʃ
tʃ
x
x
x
x
x
ʃ
ʃ
ʃ
ʃ
ʃ
ʃ
ʃ
kʰ
ʃ
ʃ
s
ʃ
ts
ts
ts
ts
ts
ts
ts
ts
ts
ts
t
t
t
t
t
t
t
t
t
t
t
t
t t
t
t
kʰ
t
kʰ
kʰ
kʰ
kʰ
kʰ
kʰ
kʰ
kʰ
kʰ
kʰ
kʰ kʰ
kʰ
kʰ
kʰ
kʰ
kʰ
kʰ
25
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Detailed Workflow: Sound Correspondence Pattern Inference
x
x
x
x
x
x
x
x
x
x
good
correspondence
set
bad
correspondence
set
25
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Detailed Workflow: Sound Correspondence Pattern Inference
Only fully compatible clusters (i.e., only cliques in our network of
correspondence sets) can represent true sound correspondence pat-
terns (if sound change is regular).
25
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Detailed Workflow: Sound Correspondence Pattern Inference
Sound Correspondence Pattern Inference as a Clique Cover Problem:
26
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Detailed Workflow: Sound Correspondence Pattern Inference
Sound Correspondence Pattern Inference as a 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.
26
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Detailed Workflow: Sound Correspondence Pattern Inference
Sound Correspondence Pattern Inference as a 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.
26
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Detailed Workflow: Sound Correspondence Pattern Inference
Sound Correspondence Pattern Inference as a 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.
26
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Detailed Workflow: Sound Correspondence Pattern Inference
Sound Correspondence Pattern Inference as a 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.
26
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Detailed Workflow: Automatic Reconstruction
We can do without trees!
27
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Detailed Workflow: Automatic Reconstruction
We can do without trees!
• Phonological reconstruction in the comparative-method framework
usually starts from correspondence patterns.
27
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Detailed Workflow: Automatic Reconstruction
We can do without trees!
• Phonological reconstruction in the comparative-method framework
usually starts from correspondence patterns.
• Apart from very few exceptions, it does not require the knowledge of
any specific phylogeny for the language family under investigation
(at least not for most consonants).
27
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Detailed Workflow: Automatic Reconstruction
We can do without trees!
• Phonological reconstruction in the comparative-method framework
usually starts from correspondence patterns.
• Apart from very few exceptions, it does not require the knowledge of
any specific phylogeny for the language family under investigation
(at least not for most consonants).
• What it requires, however, is to know the major sound change
transitions, which have strong directional preferences for consonants
(much less for vowels and tones).
27
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Detailed Workflow: Automatic Reconstruction
We can do without trees!
• Phonological reconstruction in the comparative-method framework
usually starts from correspondence patterns.
• Apart from very few exceptions, it does not require the knowledge of
any specific phylogeny for the language family under investigation
(at least not for most consonants).
• What it requires, however, is to know the major sound change
transitions, which have strong directional preferences for consonants
(much less for vowels and tones).
• We can use this knowledge as a proxy to select which of the sounds
in a given correspondence pattern is the best candidate for the
proto-sound.
27
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Detailed Workflow: Automatic Reconstruction
əː ə̆
ɿ
ə
ḭ:
ḭ
ɤ
ə̰
ĭ
aː
i
ɑ
ɛ̃
ɑ̃
ɛ
ɯ
ɛ̰̃
ɔ̃
a̰:
ɛ̰
ɔ̰̃
ã
o̰
a̰
ɑ̰
w
∼ ŋ
-
v
ĩ
e
a
ḛ
ẽ
ɔ̰
ŋ̊
ŋʲ
n◌̥ʲ
n◌̥
ɲ̊
m
n
mʲ ɲ
nʲ
m◌̥
ɕ
ʃ
ç
ɬ
ʂ r◌̥
l◌̥
ɔː
u:
ṵ
õ
ɔ
o
ʊ
ṵː
u
ũ
j
ɣ
ʐ
kʰ
x
ʑ
rj
xʐ
r
l
tɕ tʃ
t
ts
c
k
tʰ
s
tθ
p
ʔ
pʰ
f
tsʰ
tɕʰ
tʃʰ
cʰ
sʰ
28
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Detailed Workflow: Automatic Reconstruction
s
ts
tθ
ʃ tʃ
ʂ
28
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Detailed Workflow: Automatic Reconstruction
s ts
tθ
ts ʂ
s s ts
*ts
s
ts
tθ
ʂ
28
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Detailed Workflow: Automatic Reconstruction
ʃ tʃ
ʂ
s ʃ s s
ʂ ʂ ʃ
s
ts
tθ
ʂ
28
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Detailed Workflow: Automatic Reconstruction
ʃ
ʂ
s ʃ s s
ʂ ʂ ʃ
*ʃ
s
ʂ
28
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Detailed Workflow: Automatic Reconstruction
ts
tʃ
tθ
ʃ
ʂ
tʃ ts ʂ ʂ ts ts
sʃ
ʂ
28
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Detailed Workflow: Automatic Reconstruction
ts
tʃ
ʂ
tʃ ts s ʂ ts ts
*tʃ/ts
s
ʂ
28
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Detailed Workflow: Automatic Reconstruction
Automatic Reconstruction Strategy:
29
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Detailed Workflow: Automatic Reconstruction
Automatic Reconstruction Strategy:
1. extract the sub-graph from the sound-change graph for each distinct
sound in a given correspondence pattern,
29
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Detailed Workflow: Automatic Reconstruction
Automatic Reconstruction Strategy:
1. extract the sub-graph from the sound-change graph for each distinct
sound in a given correspondence pattern,
2. search for a potential source in the sub-graph, i.e., a sound that has
no ancestor,
29
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Detailed Workflow: Automatic Reconstruction
Automatic Reconstruction Strategy:
1. extract the sub-graph from the sound-change graph for each distinct
sound in a given correspondence pattern,
2. search for a potential source in the sub-graph, i.e., a sound that has
no ancestor,
3. if
• there is a source, select it as proto-form,
29
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Detailed Workflow: Automatic Reconstruction
Automatic Reconstruction Strategy:
1. extract the sub-graph from the sound-change graph for each distinct
sound in a given correspondence pattern,
2. search for a potential source in the sub-graph, i.e., a sound that has
no ancestor,
3. if
• there is a source, select it as proto-form,
• there are multiple sources, select all as proto-form,
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Detailed Workflow: Automatic Reconstruction
Automatic Reconstruction Strategy:
1. extract the sub-graph from the sound-change graph for each distinct
sound in a given correspondence pattern,
2. search for a potential source in the sub-graph, i.e., a sound that has
no ancestor,
3. if
• there is a source, select it as proto-form,
• there are multiple sources, select all as proto-form,
• the graph is disconnected or no source can be found (loops in the
graph), select the most frequently recurring form as a potential
proto-form (“majority rules”),
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Detailed Workflow: Automatic Reconstruction
Automatic Reconstruction Strategy:
1. extract the sub-graph from the sound-change graph for each distinct
sound in a given correspondence pattern,
2. search for a potential source in the sub-graph, i.e., a sound that has
no ancestor,
3. if
• there is a source, select it as proto-form,
• there are multiple sources, select all as proto-form,
• the graph is disconnected or no source can be found (loops in the
graph), select the most frequently recurring form as a potential
proto-form (“majority rules”),
4. label the “quality” of the respective proto-form, specifically marking
correspondence patterns which occur only one time in the data,
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Slide 86 text
Detailed Workflow: Automatic Reconstruction
Automatic Reconstruction Strategy:
1. extract the sub-graph from the sound-change graph for each distinct
sound in a given correspondence pattern,
2. search for a potential source in the sub-graph, i.e., a sound that has
no ancestor,
3. if
• there is a source, select it as proto-form,
• there are multiple sources, select all as proto-form,
• the graph is disconnected or no source can be found (loops in the
graph), select the most frequently recurring form as a potential
proto-form (“majority rules”),
4. label the “quality” of the respective proto-form, specifically marking
correspondence patterns which occur only one time in the data,
5. have the expert clean up the mess.
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Detailed Workflow: Automatic Reconstruction
Advantage of the Approach:
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Detailed Workflow: Automatic Reconstruction
Advantage of the Approach:
(a) ⊠ systemic aspects of sound change are integrated into the
correspondence pattern detection algorithm
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Detailed Workflow: Automatic Reconstruction
Advantage of the Approach:
(a) ⊠ systemic aspects of sound change are integrated into the
correspondence pattern detection algorithm
(b) ⊠ linguistic knowledge (even language-specific knowledge) is
exhaustively used to construct the sound-change networks
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Detailed Workflow: Automatic Reconstruction
Advantage of the Approach:
(a) ⊠ systemic aspects of sound change are integrated into the
correspondence pattern detection algorithm
(b) ⊠ linguistic knowledge (even language-specific knowledge) is
exhaustively used to construct the sound-change networks
(c) □ unattested sounds need to be manually handled by assigning them
to specific correspondence patterns
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Examples
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General Findings
Basic Statistics:
• 8 languages
• 240 concepts
• 855 partial cognate sets
• 728 cross-semantic partial cognate sets
• 218 valid cognate sets (with more than two reflexes)
• 104 initial consonant patterns (48 with more than one reflex, the
rest highly irregular)
• well-reconstructed proto-sounds:
stops and affricates *k, *kʰ, *t, *tʰ, *tʃ, *tʃʰ, *ts, *tsʰ, p, pʰ
fricatives s, ʃ, x
liquids and j r, l, j
nasals ŋ, n, m
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Specific Findings: “black” and “dark”
Language black dark
Old Burmese n a k - ∅
Rangoon n ɛ ʔ ⁴ ∅
Achang l ɔ k ⁵⁵ ∅
Xiandao n ɔ ʔ ⁵⁵ ∅
Atsi n o ʔ ²¹ n o ʔ ²¹
Bola n a ʔ ³¹ n a ʔ ³¹
Lashi n ɔː ʔ ³¹ ∅
Maru n ɔ ʔ ³¹ n ɔ ʔ ³¹
Proto-Burmish n *a k ³¹ *n *a|*ṵ ʔ|*k ³¹
[i] The discrepancy in the reconstructions for these two forms which were
regularly recognized as cognate in all languages is due to the insufficient
reconstruction by the proto-type which takes each correspondence set in-
dependently, rather than summarizing all possible reflexes for accepted
cross-semantic cognate sets.
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Specific Findings: “middle” and “outside”
Language middle outside (“out-middle”)
Old Burmese ∅ ∅
Rangoon ∅ ∅
Achang k u - ŋ ⁵⁵ ∅
Xiandao k o - ŋ ⁵⁵ ∅
Atsi k u - ŋ ²¹ ∅
Bola k a u ŋ ³¹ k a u ŋ ³¹
Lashi k u - ŋ ³¹ ∅
Maru k a u ŋ ³⁵ k a u ŋ ³⁵
Proto-Burmish *k u - *ŋ ⁵⁵ ∅
[i] The algorithm refuses to reconstruct the morpheme “middle” in the
word “outside”, as it only occurs two times. If the proto-type, again, only
reconstructed one time per cross-semantic cognate set, the results would
be the same.
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Specific Findings: “tree” and “wood”
Language tree wood
Old Burmese s a ts - s a ts -
Rangoon tθ i ʔ ⁴ tθ i ʔ ⁴
Achang s a ŋ ³¹ ʂ ə k ⁵⁵
Xiandao ʂ ɯ k ⁵⁵ ∅
Atsi s i k ⁵⁵ s i k ⁵⁵
Bola s a k ⁵⁵ s a k ⁵⁵
Lashi s ə̰ k ⁵⁵ s ə̰ k ⁵⁵
Maru s a̰ k ⁵⁵ s a̰ k ⁵⁵
Proto-Burmish s a k ⁵⁵ *s ə̰ *k ⁵⁵
[i] Apart from the vowel, which is marked as irregular in the reconstruc-
tion for “wood”, this reconstruction is regular and also easy to compare
with other Sino-Tibetan reflexes. The reconstruction for “tree”, on the
other hand, is irregular, due to the wrong cognate assignment for Achang.
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Outlook
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We are only beginning to explore the potential of sound correspon-
dence pattern analysis as a backbone for automatic linguistic recon-
struction. Even now, it is straightforward to manually annotate all
different sound correspondence patterns which we could infer from
the data.
To test the full potential of the approach, we will have to drastically
increase the number of lexical items in our data, but even in this
state, the approach is promising, and can serve as a starting point
for a classical phonological reconstruction analysis.
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Danke für Ihre Aufmerksamkeit!
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