Advances in Automated Language Classification ASJP Consortium Dik Bakker

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Advances inAutomatedLanguageClassificationASJ
P Consortium(Dik Bakker)
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Overview
Project (MAY 2007 - ) ASJP (Automated
Similarity Judgment Program)
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Overview
Project ASJP (Automated Similarity Judgment
Program)
NUMBERS
LANGUAGE
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Overview
Project ASJP (Automated Similarity Judgment
Program)
Data sources
TOOLS
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Overview
Project ASJP (Automated Similarity Judgment
Program)
Data bases
Data sources
Results
TOOLS
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Overview
Project ASJP (Automated Similarity Judgment
Program)
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Overview
Project ASJP are Sören Wichmann (BRD
Netherlands) Viveka Velupillai (BRD) André
Müller (BRD) Robert Mailhammer (BRD) Hagen
Jung (BRD) Eric Holman (US) Anthony Grant
(UK) Dmitry Egorov (Russia) Pamela Brown
(US) Cecil Brown (US) Dik Bakker (UK
Netherlands)
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Overview
Project ASJP (Automated Similarity Judgment
Program)
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Overview
Project ASJP (Automated Similarity Judgment
Program) Overall goal Automatic reconstruction
of language relationships
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Overview
Project ASJP (Automated Similarity Judgment
Program) Overall goal Automatic reconstruction
of language relationships Basis Distance
matrices between individual languages on
the basis of linguistic features
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Overview
Project ASJP (Automated Similarity Judgment
Program) Overall goal Automatic reconstruction
of language relationships Basis Distance
matrices between individual languages on
the basis of linguistic features Method
Lexicostatistics mass comparison of basic
lexical items,
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Overview
Project ASJP (Automated Similarity Judgment
Program) Overall goal Automatic reconstruction
of language relationships Basis Distance
matrices between individual languages on
the basis of linguistic features Method
Lexicostatistics mass comparison of basic
lexical items, extended by all relevant
data available
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Swadesh (2440)
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Swadesh (2440)
ASJP software
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Swadesh (2440)
ASJP software
distance matrices
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Swadesh (2440)
ASJP1
ASJP2
distance matrices
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Swadesh (2440)
ASJP1
ASJP2
distance matrices
TREE SFTW
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Swadesh (2440)
ETHN WALS EXPRT
ASJP1
ASJP2
calibration
distance matrices
TREE SFTW
STAT SFTW
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Swadesh (2440)
ETHN WALS EXPRT
ASJP1
ASJP2
calibration
distance matrices
TREE SFTW
STAT SFTW
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Swadesh (2440)
ETHN WALS EXPRT
GEO GRAPH
ASJP1
ASJP2
distance matrices
TREE SFTW
STAT SFTW
MAP SFTW
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Swadesh (2440)
ETHN WALS EXPRT
GEO GRAPH
HIST FACTS
ASJP1
ASJP2
distance matrices
TREE SFTW
STAT SFTW
MAP SFTW
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Swadesh (2440)
ETHN WALS EXPRT
GEO GRAPH
HIST FACTS
PHON INVENT
ASJP1
ASJP2
distance matrices
TREE SFTW
STAT SFTW
MAP SFTW
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Swadesh (2440)
ETHN WALS EXPRT
GEO GRAPH
HIST FACTS
PHON INVENT
ASJP1
ASJP2
Jeff Mielke 500
distance matrices
TREE SFTW
STAT SFTW
MAP SFTW
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Swadesh (2440)
ETHN WALS EXPRT
GEO GRAPH
HIST FACTS
PHON INVENT
LOANS
ASJP1
ASJP2
distance matrices
TREE SFTW
STAT SFTW
MAP SFTW
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Overview
OVERALL GOAL Reconstruction of Language
Relationships
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Overview
OVERALL GOAL Reconstruction of Language
Relationships Derived goals
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Overview
OVERALL GOAL Reconstruction of Language
Relationships Derived goals - Critical
assessment and refinement of existing
classifications
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Overview
OVERALL GOAL Reconstruction of Language
Relationships Derived goals - Critical
assessment and refinement of existing
classifications - Classify newly described and
unclassified languages
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Overview
OVERALL GOAL Reconstruction of Language
Relationships Derived goals - Critical
assessment and refinement of existing
classifications - Classify newly described and
unclassified languages - Search for
(ir)regularities in phylogenies
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Overview
OVERALL GOAL Reconstruction of Language
Relationships Derived goals - Critical
assessment and refinement of existing
classifications - Classify newly described and
unclassified languages - Search for
(ir)regularities in phylogenies - Test hypotheses
(e.g. Atkinson et al 2008 elbow phenomenon)
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Overview
OVERALL GOAL Reconstruction of Language
Relationships Derived goals - Critical
assessment and refinement of existing
classifications - Classify newly described and
unclassified languages - Search for
(ir)regularities in phylogenies - Test hypotheses
(e.g. Atkinson et al 2008 elbow phenomenon) -
Experimentally find an optimal dating method
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Overview
OVERALL GOAL Reconstruction of Language
Relationships Derived goals - Critical
assessment and refinement of existing
classifications - Classify newly described and
unclassified languages - Search for
(ir)regularities in phylogenies - Test hypotheses
(e.g. Atkinson et al 2008 elbow phenomenon) -
Experimentally find an optimal dating method -
Automatically detect borrowings
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Overview
OVERALL GOAL Reconstruction of Language
Relationships Derived goals - Critical
assessment and refinement of existing
classifications - Classify newly described and
unclassified languages - Search for
(ir)regularities in phylogenies - Test hypotheses
(e.g. Atkinson et al 2008 elbow phenomenon) -
Experimentally find the best/optimal dating
method - Automatically detect borrowings
Today ...
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Overview
1. The list of basic lexical items

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Overview
1. The list of basic lexical items
2. Comparing words languages
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Overview
1. The list of basic lexical items 2. Comparing
words languages 3. Some results genetic
proximity
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Overview
1. The list of basic lexical items 2. Comparing
words languages 3. Some results genetic
proximity 4. On Inheritance vs
Borrowing
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Overview
1. The list of basic lexical items 2. Comparing
words languages 3. Some results genetic
proximity 4. On Inheritance vs Borrowing 5.
Immanent extensions
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1. The list of basic lexical items
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Lexical items
Word list Swadesh 100 basic meanings

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Lexical items
Word list Swadesh 100 basic meanings - Word
coined in most languages

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Lexical items
Word list Swadesh 100 basic meanings - Word
coined in most languages - Collected in field
work lexicon / grammar

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Lexical items
Word list Swadesh 100 basic meanings - Word
coined in most languages - Collected in field
work lexicon / grammar - Inherited rather than
borrowed

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Lexical items
Word list Swadesh 100 basic meanings - Word
coined in most languages - Collected in field
work lexicon / grammar - Inherited rather than
borrowed - Culturally independent

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Lexical items
Word list Swadesh 100 basic meanings - Word
coined in most languages - Collected in field
work lexicon / grammar - Inherited rather than
borrowed - Culturally independent - Stable over
time

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Lexical items
Word list Swadesh 100 basic meanings - Word
coined in most languages - Collected in field
work lexicon / grammar - Inherited rather than
borrowed - Culturally independent - Stable over
time - Few synonyms

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Lexical items
Word list Swadesh 100 basic meanings - Word
coined in most languages - Collected in field
work lexicon / grammar - Inherited rather than
borrowed - Culturally independent - Stable over
time - Few synonyms
?

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Lexical items
Word list Swadesh 100 basic meanings - Word
coined in most languages - Collected in field
work lexicon / grammar - Inherited rather than
borrowed - Culturally independent - Stable over
time - Few synonyms
LWT
?

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(No Transcript)
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Otomi from Spanish
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Lexical items further reduction
Early analyses have shown - Most stable 40/100
item subset gives same results

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Lexical items further reduction

• Early analyses have shown
• - Most stable 40/100 item subset gives same
  results
• ? Less work

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Lexical items further reduction

• Early analyses have shown
• - Most stable 40/100 item subset gives same
  results
• ? Less work
• ? Less missing data

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Lexical items further reduction

• Early analyses have shown
• - Most stable 40/100 item subset gives same
  results
• ? Less work
• ? Less missing data
• Faster processing combinatorial explosion
• 40 100 109 lt 1010 COMPARISONS

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Lexical items further reduction

• Early analyses have shown
• - Most stable 40/100 item subset gives same
  results
• ? Less work
• ? Less missing data
• Faster processing combinatorial explosion
• 40 100 109 lt 1010 COMPARISONS

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Lexical items further reduction
Most stable SSM (R U) / (1 U)

see references

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Lexical items further reduction
Most stable SSM (R U) / (1 U) R
mean proportion same form for SMi / genus

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Lexical items further reduction
Most stable SSM (R U) / (1 U) R
mean proportion same form for SMi / genus U
mean proportion same form for different SMx /
genus

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Lexical items further reduction
Most stable SSM (R U) / (1 U) R
mean proportion same form for SMi / genus U
mean proportion same form for different SMx /
genus
N.B. Ssm high correlation between families

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Ethnologue (Goodmann-Kruskal)
WALS (Pearson)
lt Stability gt --
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(No Transcript)
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40 Most Stable
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40 Most Stable
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Lexical items transcription
First phase of project (2007) Problems with
full IPA representation of words

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Lexical items transcription
First phase of project (2007) Problems with
full IPA representation of words - data entry
via keyboard

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Lexical items transcription
First phase of project (2007) Problems with
full IPA representation of words - data entry
via keyboard - simple programming language
(Fortran Pascal)

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Lexical items transcription
First phase of project (2007) Problems with
full IPA representation of words - data entry
via keyboard - simple programming language
(Fortran Pascal) ? Recoding to simplified
ASJPcode (only Ascii)

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Lexical items transcription
ASJPcode

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Lexical items transcription
ASJPcode 7 Vowels

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Lexical items transcription
ASJPcode 7 Vowels 34 Consonants

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Lexical items transcription
ASJPcode 7 Vowels 34 Consonants
Closest sound

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Lexical items transcription
ASJPcode 7 Vowels 34 Consonants Operators
for Nasalization Labialization Palatalizati
on Aspiration Glottalization

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Abaza (Caucasian) Meaning PERSON LEAF SKI
N HORN NOSE TOOTH
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Abaza (Caucasian) Meaning IPA PERSON ????'??
???s LEAF b??? SKIN ??az? HORN ?'???
?a NOSE p?n?'a TOOTH p??
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Abaza (Caucasian) Meaning IPA ASJPcode PERSON
????'?????s Xw3Cw"yXw3s LEAF b??? bxy3 S
KIN ??az? Cwazy HORN ?'????a Cw"3Xwa NO
SE p?n?'a p3nc"a TOOTH p?? p3c
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Lexical items
Collected to date - Close to 2500 languages
(incl. dialects and proto)

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Lexical items

• Collected to date
• - Close to 2500 languages (incl. dialects and
  proto)
• - Mean number of items/language 35.8 (/40)

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Lexical items
Areal distribution (not a sample!) Americas 27
Eurasia 23 Australia/PNG 18 Austronesia 15
Africa 14 Creoles 2 Artificial 1

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Languages currently sampled
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2. Comparing words and languages
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Comparing words
Two strategies

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Comparing words
Two strategies 1. ASJP rules

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Comparing words
1. ASJP context rules

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Comparing words
ASJP context rules a. between 2 words

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Comparing words
ASJP context rules
SMi WORDlg1
WORDlg2

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Comparing words
ASJP context rules (C/Vgeneral c/vspecific
X)
SMi WORDlg1
WORDlg2 R1 (V)cVcX XcVcX R2
Xc(V)c(V)cX Xc(V)c(V)cX R12 AVcvX
VcvX Ahwy R13 (V)ccVX
(V)ccVX R22 cv (CV)cv

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Comparing words
ASJP context rules (C/Vgeneral c/vspecific
X)
SMi WORDlg1
WORDlg2 R1 (V)cVcX XcVcX R2
Xc(V)c(V)cX Xc(V)c(V)cX R12 AVcvX
VcvX Ahwy R13 (V)ccVX
(V)ccVX R22 cv
(CV)cv pattern Wlg1 UNIFIES pattern Wlg2

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Comparing words
ASJP context rules (C/Vgeneral c/vspecific
X)
SMi WORDlg1
WORDlg2 R1 (V)cVcX XcVcX R2
Xc(V)c(V)cX Xc(V)c(V)cX R12 AVcvX
VcvX Ahwy R13 (V)ccVX
(V)ccVX R22 cv (CV)cv

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Comparing words
ASJP context rules (C/Vgeneral c/vspecific
X)
SMi WORDlg1
WORDlg2 R1 (V)cVcX XcVcX R2
Xc(V)c(V)cX Xc(V)c(V)cX R12 AVcvX
VcvX Ahwy R13 (V)ccVX
(V)ccVX R22 cv (CV)cv

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Comparing words
ASJP context rules (C/Vgeneral c/vspecific
X)
R1 (V)cVcX XcVcX R2
Xc(V)c(V)cX Xc(V)c(V)cX R12 AVcvX
VcvX Ahwy R13 (V)ccVX
(V)ccVX R22 cv
(CV)cv yapi opi

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Comparing words
ASJP context rules a. between 2 words value 0
or 1

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Comparing words

• ASJP context rules
• a. between 2 words
• value 0 or 1
• b. between 2 languages RELATEDNESS
• (n of matching words / total pairs) 100

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Comparing words

• ASJP context rules
• a. between 2 words
• value 0 or 1
• b. between 2 languages DISTANCE
• LSP100 ((matching words / total pairs) 100 )

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Comparing words
2. Levenshtein Distance

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Comparing words
Levenshtein Distance a. between 2
words number of transformations to get from the
shorter form to the longer one (changes,
additions) min 0 / max length longest word

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Comparing words
Levenshtein Distance a. between 2
words number of transformations to get from the
shorter form to the longer one (changes,
additions) b. between 2 languages mean LD for
total number of pairs

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Comparing words
Two problems with simple LD

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Comparing words

• Two problems
• Value depends on length of longest word

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Comparing words

• Two problems
• Value depends on length of longest word
• ? Normalize LDN ( LD / Lmax )

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Comparing words

• Two problems
• Value depends on length of longest word
• ? Normalize LDN ( LD / Lmax )
• 2. Differences between lgs in phonological overlap

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Comparing words

• Two problems
• Value depends on length of longest word
• ? Normalize LDN ( LD / Lmax )
• 2. Differences between lgs in phonological
  overlap
• Eliminate background noise
• LDND ( LDN / LDNdifferent pairs )

102
Comparing words
Levenshtein Distance a. between 2 words LDND
0 - 100 ()

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Comparing words
Levenshtein Distance a. between 2 words LDND
0 - 100 () b. between 2
languages Mean of all LDNDs of words in common

104
Comparing languages
AGUACATEC (agu) ltgt MOCHO (mhc) MAYAN (45) gt
MAYAN GeoD97 GenD1.86 ONE xunhun -
LDND 37.4 TWO kobkabe7 R1 LDND
67.3 BONE baqbaq R3 LDND 0.0
EAR SCinCikin - LDND 67.3
WATER a7ha7 R10 LDND 37.4

105
Comparing languages
AGUACATEC (agu) ltgt MOCHO (mhc) MAYAN (45) gt
MAYAN GeoD97 GenD1.86 ONE xunhun -
LDND 37.4 TWO kobkabe7 R1 LDND
67.3 BONE baqbaq R3 LDND 0.0
EAR SCinCikin - LDND 67.3
WATER a7ha7 R10 LDND 37.4 T O T A
L LSP 58.14

106
Comparing languages
AGUACATEC (agu) ltgt MOCHO (mhc) MAYAN (45) gt
MAYAN GeoD97 GenD1.86 ONE xunhun -
LDND 37.4 TWO kobkabe7 R1 LDND
67.3 BONE baqbaq R3 LDND 0.0
EAR SCinCikin - LDND 67.3
WATER a7ha7 R10 LDND 37.4 T O T A
L LSP 58.14 LDND 51.68 (n35)

107
Comparing languages
AGUACATEC (agu) ltgt MOCHO (mhc) MAYAN (45) gt
MAYAN GeoD97 GenD1.86 ONE xunhun -
LDND 37.4 TWO kobkabe7 R1 LDND
67.3 BONE baqbaq R3 LDND 0.0
EAR SCinCikin - LDND 67.3
WATER a7ha7 R10 LDND 37.4 HIGH
CORRELATION LSP 58.14 LDND 51.68 (n35)

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Comparing languages
HIGH CORRELATION LSP LDND

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Comparing languages
HIGH CORRELATION LSP LDND MAYA
(n34) 0.93 INDO-EUROPEAN (n129) 0.97 AME
RINDIAN (n511) 0.59

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Comparing languages
BEST PERFORMERS Within families 1.
EYE 0.496 2. LOUSE 0.480 3. DIE 0.469
4. BREAST 0.415 5. STONE 0.364

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Comparing languages
BEST PERFORMERS Within families 1.
EYE 0.496 2. LOUSE 0.480 3. DIE 0.469
4. BREAST 0.415 5. STONE 0.364 Across
families 1. I 0.072 2. DIE 0.065 3.
WE 0.061 4. YOU 0.057 5. BREAST 0.057

112
Comparing languages
BEST PERFORMERS Within families 1.
EYE 0.496 2. LOUSE 0.480 3. DIE 0.469
4. BREAST 0.415 5. STONE 0.364 Across
families 1. I 0.072 2. DIE 0.065 3.
WE 0.061 4. YOU 0.057 5. BREAST 0.057

113
Comparing languages
BEST PERFORMERS Within families 1.
EYE 0.496 2. LOUSE 0.480 3. DIE 0.469
4. BREAST 0.415 5. STONE 0.364 Across
families 1. I 0.072 2. DIE 0.065 3.
WE 0.061 4. YOU 0.057 5. BREAST 0.057
- Shortness - Sound Symbolism?

114
Comparing languages
WORST PERFORMERS Within families 36.
HORN 0.107 37. SEE 0.099 38.
KNEE 0.095 39. NIGHT
0.079 40. MOUNTAIN 0.075

115
Comparing languages
WORST PERFORMERS Within families 36.
HORN 0.107 37. SEE 0.099 38.
KNEE 0.095 39. NIGHT
0.079 40. MOUNTAIN 0.075 Across
families 36. NIGHT 0.028 37. HEAR
0.027 38. HORN
0.027 39. STAR 0.024 40. KNEE
0.023

116
Comparing languages
WORST PERFORMERS Within families 36.
HORN 0.107 37. SEE 0.099 38.
KNEE 0.095 39. NIGHT
0.079 40. MOUNTAIN 0.075 Across
families 36. NIGHT 0.028 37. HEAR
0.027 38. HORN
0.027 39. STAR 0.024 40. KNEE
0.023

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for 2440 lgs 3,000,000 ( 362 3.109
)
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3. Genetic proximity
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Swadesh (2440)
AJP2
distance matrices
Splits Tree
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Swadesh (2440)
AJP2
distance matrices
Splits Tree
MEGA4
121
Swadesh (2440)
AJP2
distance matrices
Neighbour Joining
Splits Tree
MEGA4
122
ASJP
LSP
123
Correlation ETHN .325
LSP
124
Correlation ETHN .325 (n 69)
(n 34)
LSP
125
Correlation ETHN .325
More structure than ETHN
LSP
126
Correlation ETHN .325
Separation
LSP
127
Levenshtein
LDND
128
Levenshtein
Correlation ETHN .195
LDND
129
Levenshtein
Correlation ETHN .195 (LSP .325)
LDND
130
ASJP
LDND
131
cholan
ASJP
LDND
132
cholan
tzeltalan
ASJP
LDND
133
cholan
tzeltalan
ASJP
LDND
134
yucatecan
ASJP
LDND
135
ASJP
LDND
136
ASJP
LDND
137

all significant gt 0.01
138

all significant gt 0.01
139
Improving the fit
Enrich lexical with typological data

140
Swadesh (2440)
WALS (2580)

ASJP
distance matrices
TREE SFTW
141
SWALSH (2440)
ASJP
distance matrices
TREE SFTW
142
Improving the fit
Enrich lexical with typological data

143
Improving the fit

• Enrich lexical with typological data
• NOT 11 with ASJP languages

144
SWALSH (550)
ASJP
distance matrices
TREE SFTW
145
Improving the fit

• Enrich lexical with typological data
• NOT 11 with ASJP languages
• WALS variables very unevenly spread

146
Improving the fit

• Enrich lexical with typological data
• NOT 11 with ASJP languages
• WALS variables very unevenly spread
• Maximum subset 85 most stable

147
Most stable WALS variables
148
Improving the fit

• Enrich lexical with typological data
• Maximum subset 85 most stable

149
Improving the fit

• Enrich lexical with typological data
• Maximum subset 85 most stable
• Correlation with Swadesh 0.063 (gt 0.001)

?

150
Improving the fit

• Enrich lexical with typological data
• Maximum subset 85 most stable
• Correlation with Swadesh 0.063 (gt 0.001)
• Mantel Test 10.000 simulations

151
Improving the fit

• Enrich lexical with typological data
• Maximum subset 85 most stable
• Correlation with Swadesh 0.063 (gt 0.001)
• Mantel Test 10.000 simulations
• best 0.050 lt gt - 0.043 (mean 0.009)

152
Improving the fit

• Enrich lexical with typological data
• Database 40 most stable Swadesh
• 85 most stable WALS features

153
Improving the fit

• Enrich lexical with typological data
• Database 40 most stable Swadesh
• 85 most stable WALS features
• - Optimal weight of both?

154
Improving the fit
155
Improving the fit
156
Improving the fit
157
4. On Inheritance vs Borrowing
158
Inherited or borrowed?
AVAR (AVA) / AGUL (AGL)

159
Inherited or borrowed?
AVAR (AVA) / AGUL (AGL) I dunzun
LDND36.6 YOU munwun
LDND36.6 HORN tLark"arC
LDND66.0 FIRE c"ac"a LDND
0.0 FULL c"uraac"uf LDND66.0 NEW
c"iyac"EyEr LDND55.0

160
Inherited or borrowed?
AVAR (AVA) / AGUL (AGL) I dunzun
LDND36.6 YOU munwun
LDND36.6 HORN tLark"arC
LDND66.0 FIRE c"ac"a LDND
0.0 FULL c"uraac"uf LDND66.0 NEW
c"iyac"EyEr LDND55.0 ? 6 items lt
70.0

161
Inherited or borrowed?
AVAR (AVA) / AGUL (AGL) I dunzun
LDND36.6 YOU munwun
LDND36.6 HORN tLark"arC
LDND66.0 FIRE c"ac"a LDND
0.0 FULL c"uraac"uf LDND66.0 NEW
c"iyac"EyEr LDND55.0 ? 6 items lt
70.0 ? Genetically related !!

162
Inherited or borrowed?
SPANISH (SPA) / CHAMORRO (CHA)

163
Inherited or borrowed?
SPANISH (SPA) / CHAMORRO (CHA) ONE
unounu LDND36.9 TWO
dosdos LDND 0.0 PERSON
personapetsona LDND15.8 STAR
estreyaestrecas LDND27.6 NIGHT
noCenoces LDND44.2 NEW
nuevonueba LDND44.2

164
Inherited or borrowed?
SPANISH (SPA) / CHAMORRO (CHA) ONE
unounu LDND36.9 TWO
dosdos LDND 0.0 PERSON
personapetsona LDND15.8 STAR
estreyaestrecas LDND27.6 NIGHT
noCenoces LDND44.2 NEW
nuevonueba LDND44.2 ? 6 items lt 70.0

165
Inherited or borrowed?
SPANISH (SPA) / CHAMORRO (CHA) ONE
unounu LDND36.9 TWO
dosdos LDND 0.0 PERSON
personapetsona LDND15.8 STAR
estreyaestrecas LDND27.6 NIGHT
noCenoces LDND44.2 NEW
nuevonueba LDND44.2 NOT Related
Chance?

166
Inherited or borrowed?
SPANISH (SPA) / CHAMORRO (CHA) ONE
unounu LDND36.9 TWO
dosdos LDND 0.0 PERSON
personapetsona LDND15.8 STAR
estreyaestrecas LDND27.6 NIGHT
noCenoces LDND44.2 NEW
nuevonueba LDND44.2 NOT Related
Chance? Or Borrowing?

167
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE
(12) / CHAMORRO (CHA) AUSTRONESIAN
(310) gt CHAMORROSTAR estreyaestrecas
LDND27.6

168
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA f/g 0.17/0.82 ( lt
0.70)

169
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA ltgt CHA f/g 0.17/0.82
0.00/0.00

170
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82
gt 0.00/0.00

171
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA ltgt CHA wwF

172
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA wwF 83 ( mean LDND estreya
in IE)

173
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA wwF 83-99 ( mean estreya in
AU)

174
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA ltgt CHA wwF 83-99 ltgt 102 ( mn
estrecas / AU)

175
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA ltgt CHA wwF 83-99 ltgt 102-85 (
estrecas / IE)

176
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA ltgt CHA wwF 83-99 ltgt 102-85

177
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA gt CHA wwF 83-99 gt 102-85

178
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA gt CHA wwF 83-99 gt 102-85
SPA ltgt CHA phwF

179
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA gt CHA wwF 83-99 gt 102-85
SPA phwF 100.00 (phon estreya in IE / AU)

180
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA gt CHA wwF 83-99 gt 102-85
SPAltgt CHA phwF 100.00 ltgt 0.52

(phon estrecas in AU/ IE )

181
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA gt CHA wwF 83-99 gt 102-85
SPA gt CHA phwF 100.00 gt 0.52

182
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE (12)
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROSTAR estreyaestrecas
LDND27.6 SPA gt CHA f/g 0.17/0.82 gt
0.00/0.00 SPA gt CHA wwF 83-99 gt 102-85
SPA gt CHA phwF 100.00 gt 0.52 SYN CHA
puti7on (f 1.00)

183
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROONE unounu
LDND36.9 SPA gt CHA f/g 0.24/0.82 gt
0.03/0.00 wwF 97-106 gt 110-97 phwF 12.00
gt 0.44

184
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROTWO dosdos LDND
0.0 SPA gt CHA f/g 0.62/1.00 gt
0.12/0.00 wwF 78-99 gt 102-78
phwF100.00 gt 0.22


185
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORRONIGHT noCenoces
LDND44.2 SPA gt CHA f/g 0.23/0.55 gt
0.04/0.00 wwF 89-100 gt 105-92 phwF 100.00
gt 0.10


186
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORRONEW nuevonueba
LDND44.2 SPA gt CHA f/g 0.50/0.64 gt
0.04/0.00 wwF 68-104 gt 105-80 phwF 4.27 gt
0.03

187
Inherited or borrowed?
SPANISH (SPA) INDO-EUROPEAN (128) gt ROMANCE
/ CHAMORRO (CHA) AUSTRONESIAN (310) gt
CHAMORROPERSON personapetsona
LDND15.8 SPA gt CHA f/g 0.20/0.64 gt
0.01/0.00 wwF 89-98 gt 98-90 phwF 32.40
gt 0.13 SYN CHA taotao (f 1.00)

188
Inherited or borrowed?
Further output filters

189
Inherited or borrowed?
Further output filters 1. Minimum N potential
borrowings

190
Inherited or borrowed?
Further output filters 1. Minimum N potential
borrowings 2. All in the same direction

191
Inherited or borrowed?
Further output filters 1. Minimum N potential
borrowings 2. All in the same direction 3.
Geographic information

192
Inherited or borrowed?
SPANISH (spa) INDO-EUROPEAN (128) gt ROMANCE
(12) EURASIA SPAIN VS. CHAMORRO (cha)
AUSTRONESIAN (678) gt CHAMORRO OCEANIA
GUAM GEODIST13244 GENDIST3.00

193
Spaniards in Pacific since 16th century
HIST FACTS
Swadesh (2440)
ETHN WALS EXPRT
GEO GRAPH
ASJP1
ASJP2
distance matrices
TREE SFTW
STAT SFTW
MAP SFTW
194
Inherited or borrowed?
Further output filters 1. Minimum N potential
borrowings 2. All in the same direction 3.
Geographic information 4. Role of form and
meaning (?)

195
Inherited or borrowed?
Further output filters 1. Minimum N potential
borrowings 2. All in the same direction 3.
Geographic information 4. Role of form and
meaning (?)
LWT

196
Borrowed!
BOR spa TO cha 6 (15.0) LDND 76.63
(shared40 crit70.00 - U) DATABASE unu(spa)
dos(spa) petsona(spa) estrecas(spa) n
oces(spa) nueba(spa)

197
5. Immanent extensions
198
(No Transcript)
199
GARBAGE IN ? GARBAGE OUT
200
Lexical items transcription
Second year of project (2008-9) Replace ASJP
code by full IPA representations

201
Lexical items transcription
Second year of project (2008-9) Replace ASJP
code by full IPA representations
Juliette Jeff

202
Lexical items transcription
Second year of project (2008-9) Problems with
full IPA representation solved

203
Lexical items transcription
Second year of project (2008-9) Problems with
full IPA representation solved 1.
scan/download/ full IPA representations

204
Lexical items transcription
Second year of project (2008-9) Problems with
full IPA representation solved 1.
scan/download/ full IPA representations 2.
automatic conversion IPA to integer (Python)

205
Lexical items transcription
Second year of project (2008-9) Problems with
full IPA representation solved 1.
scan/download/ full IPA representations 2.
automatic conversion IPA to integer (Python) 3.
(semi-)automatic recoding to ASJPcode
transduction on the basis of a formal grammar

206
Lexical items transcription
Abaza (Caucasian) Meaning PERSON

207
Lexical items transcription
Abaza (Caucasian) Meaning PERSON IPA ????'?
????s

208
Lexical items transcription
Abaza (Caucasian) Meaning PERSON IPA ????'?
????s Decimal 661,695,616,679,700,690,695,661,6
95,616,115

209
Lexical items transcription
Abaza (Caucasian) Meaning PERSON IPA ????'?
????s Decimal 661,695,616,679,700,690,695,661,6
95,616,115 ASJPcode 88,119,126,51,67,34,121,11
9,126,88,119,126,51 115 ( Xw3Cw"yXw3s)

210
Lexical items transcription
Second year of project (2008-9) 1. automatic
conversion IPA to integer (Python) 2.
(semi-)automatic recoding to ASJPcode
transduction on the basis of a formal grammar
Why not run on full IPA??

211
Lexical items transcription
Second year of project (2008) 1. automatic
conversion IPA to integer (Python) 2.
(semi-)automatic recoding to ASJPcode
transduction on the basis of a formal grammar
Caucasian correlations IPA ASJP gt 0.9

212
Lexical items transcription
Second year of project (2008) 1. automatic
conversion IPA to integer (Python) 2.
(semi-)automatic recoding to ASJPcode
transduction on the basis of a formal grammar
- correlations IPA ASJP gt 0.9 - but ASJP
better fit with classifications ?
IPA too specific

213
Lexical items transcription
IPA ????'?????s Decimal 661,695,616,679,700,6
90,695,661,695,616,115 ASJPcode (
any unicode subset )
a lt- 661, 895, 416,
formal grammar

214
Lexical items transcription
IPA ????'?????s Decimal 661,695,616,679,700,6
90,695,661,695,616,115 ASJPcode (
any unicode subset )
a lt- 661, 895, 416, C -V lt- C V / -
C V lt- C -V, PL / - C V
formal grammar

215
Lexical items transcription
IPA ????'?????s Decimal 661,695,616,679,700,6
90,695,661,695,616,115 ASJPcode (
any unicode subset )
optimal level of abstraction for
historical phonological reconstruction?
a lt- 661, 895, 416, C -V lt- C V / -
C V lt- C -V, PL / - C V

216
Swadesh
Phon Invent
ETHN WALS EXP
GEO GRAPH
HIST FACTS
ASJP1
ASJP2
distance matrices
Borrowing!
TREE SFTW
STAT SFTW
MAP SFTW
217
Lexical items transcription

all significant gt 0.01
218
Lexical items transcription

all significant gt 0.01
219

• Holman, Eric et al. (2008).
• Advances in automated language classification.
• In Arppe, Antti, Kaius Sinnemäki and Urpu
• Nikanne (eds.), Quantitative Investigations in
  Theoretical
• Linguistics, 40-43. Helsinki University of
  Helsinki.
• - Holman et al. (forthc. 2008)
• Explorations in automated language
  classification.
• Folia Linguistica
• - Brown et al. (forthc. 2008)
• Automated Classification of the Worlds
  languages
• A description of the method and prelimary
  results
• Sprachtypologie und Universalienforschung
• - Bakker et al. (2009?)
• Using WALS for the ASJP project

220
email.eva.mpg.de./wichmann/ASJPHomePage
221
?