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The LingPy library for quantitative historical
linguistics
Background, theory, and application
Johann-Mattis List
Forschungszentrum Deutscher Sprachatlas
Philipps-Universität Marburg
15.02.2014
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Background
LingPy
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Background
What is LingPy?
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Background
What is LingPy?
Python library for automatic tasks in historical linguistics
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Background
What is LingPy?
Python library for automatic tasks in historical linguistics
project homepage at http://lingpy.org
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Background
What is LingPy?
Python library for automatic tasks in historical linguistics
project homepage at http://lingpy.org
code base for developers at
https://github.com/lingpy/lingpy
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Background
What is LingPy?
Python library for automatic tasks in historical linguistics
project homepage at http://lingpy.org
code base for developers at
https://github.com/lingpy/lingpy
supports Python2 and Python3
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Background
What is LingPy?
Python library for automatic tasks in historical linguistics
project homepage at http://lingpy.org
code base for developers at
https://github.com/lingpy/lingpy
supports Python2 and Python3
works on Mac, Linux, and (basically also) Windows
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Background
What is LingPy?
Python library for automatic tasks in historical linguistics
project homepage at http://lingpy.org
code base for developers at
https://github.com/lingpy/lingpy
supports Python2 and Python3
works on Mac, Linux, and (basically also) Windows
current release: 2.2
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Background
What is LingPy?
Python library for automatic tasks in historical linguistics
project homepage at http://lingpy.org
code base for developers at
https://github.com/lingpy/lingpy
supports Python2 and Python3
works on Mac, Linux, and (basically also) Windows
current release: 2.2
offers methods for sequence modeling, phonetic alignment,
cognate and borrowing detection, and tools for data manipulation
and visualization
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Background
What can be done with LingPy?
tokenize phonetic sequences
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Background
What can be done with LingPy?
align phonetic sequences
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Background
What can be done with LingPy?
search for cognates
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Background
What can be done with LingPy?
search for borrowings
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Formats
Formats
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Formats
Formats: Basics
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ID CONCEPT COUNTERPART IPA DOCULECT COGID
1 hand Hand hant German 1
2 hand hand hænd English 1
3 hand рука ruka Russian 2
4 hand рука ruka Ukrainian 2
5 leg Bein bain German 3
6 leg leg lɛg English 4
7 leg нога noga Russian 5
8 leg нога noha Ukrainian 5
9 Woldemort Waldemar valdemar German 6
10 Woldemort Woldemort wɔldemɔrt English 6
11 Woldemort Владимир vladimir Russian 6
12 Woldemort Володимир volodimir Ukrainian 6
13 Harry Harald haralt German 7
14 Harry Harry hæri English 7
15 Harry Гарри gari Russian 7
16 Harry Гаррi hari Ukrainian 7
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Formats
Formats: Basics
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CONCEPT GERMAN ENGLISH RUSSIAN UKRAINIAN
hand Hand hand рука рука
leg Bein leg нога нога
Woldemort Waldemar Woldemort Владимир Володимир
Harry Harald Harry Гарри Гаррi
+ Orthography +
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Formats
Formats: Basics
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CONCEPT GERMAN ENGLISH RUSSIAN UKRAINIAN
hand hant hænd ruka ruka
leg bain lɛg noga noha
Woldemort valdəmar wɔldəmɔrt vladimir volodimir
Harry haralt hæri gari hari
+ Entries in IPA +
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Formats
Formats: Basics
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CONCEPT GERMAN ENGLISH RUSSIAN UKRAINIAN
hand 1 1 2 2
leg 3 4 5 5
Woldemort 6 6 6 6
Harry 7 7 7 7
+ Cognate-IDs +
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Formats
Formats: Key-Value Extension
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# Wordlist
# META
@author: Potter, Harry
@date: 2013-04-02
@tree: ((German,English),(Russian,Ukrainian));
@note: Use the data with care, it might have been charmed...
# DATA
ID CONCEPT COUNTERPART IPA DOCULECT COGID
1 hand Hand hant German 1
2 hand hand hænd English 1
3 hand рука ruka Russian 2
4 hand рука ruka Ukrainian 2
5 leg Bein bain German 3
... ... ... ... ... ...
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Formats
Formats: Further Extensions
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# Wordlist
# META
@author:Potter, Harry
@date:2012-11-07
# JSON
{
"taxa": [
"English",
"German",
"Russian",
"Ukrainian"
]
}
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Formats
Formats: Further Extensions
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# DISTANCES
4
English 0.000000 0.333333 0.666667 0.666667
German 0.333333 0.000000 0.666667 0.666667
Russian 0.666667 0.666667 0.000000 0.000000
Ukrainian 0.666667 0.666667 0.000000 0.000000
# DATA
ID CONCEPT COUNTERPART IPA DOCULECT COGID
#
1 hand Hand hant German 1
2 hand hand hænd English 1
... ... ... ... ... ...
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Representation
Representation
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Representation Sound Classes
Sound Classes: General Idea
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Sound Classes
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Representation Sound Classes
Sound Classes: General Idea
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Sound Classes
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Sounds which often occur in
correspondence relations in
genetically related languages can
be clustered into classes (types). It
is assumed “that phonetic
correspondences inside a‘type’
are more regular than those
between different‘types’”
(Dolgopolsky 1986: 35).
k g p b
ʧ ʤ f v
t d ʃ ʒ
θ ð s z
1
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Representation Sound Classes
Sound Classes: General Idea
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Sound Classes
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Sounds which often occur in
correspondence relations in
genetically related languages can
be clustered into classes (types). It
is assumed “that phonetic
correspondences inside a‘type’
are more regular than those
between different‘types’”
(Dolgopolsky 1986: 35).
k g p b
ʧ ʤ f v
t d ʃ ʒ
θ ð s z
1
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Representation Sound Classes
Sound Classes: General Idea
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Sound Classes
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Sounds which often occur in
correspondence relations in
genetically related languages can
be clustered into classes (types). It
is assumed “that phonetic
correspondences inside a‘type’
are more regular than those
between different‘types’”
(Dolgopolsky 1986: 35).
k g p b
ʧ ʤ f v
t d ʃ ʒ
θ ð s z
1
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Representation Sound Classes
Sound Classes: General Idea
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Sound Classes
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Sounds which often occur in
correspondence relations in
genetically related languages can
be clustered into classes (types). It
is assumed “that phonetic
correspondences inside a‘type’
are more regular than those
between different‘types’”
(Dolgopolsky 1986: 35).
K
T
P
S
1
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Representation Sound Classes
Sound Classes: Scoring Functions
LingPy offers default scoring functions for three standard
sound-class models (ASJP, SCA, DOLGO).
The standard models vary regarding the roughness by which the
continuum of sounds is split into discrete classes.
The scoring functions are based on empirical data on sound
correspondence frequencies (ASJP model, Brown et al. 2013), and
on general theoretical models of the directionality and probability of
sound change processes (SCA, DOLGO, see List 2012b for
details).
Scoring functions can be easily expanded by the user.
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Representation Prosodic Strings
Prosodic Strings
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Representation Prosodic Strings
Prosodic Strings
Sound change occurs more frequently in prosodically weak
positions (Geisler 1992).
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Representation Prosodic Strings
Prosodic Strings
Sound change occurs more frequently in prosodically weak
positions (Geisler 1992).
Given a sonority profile, one can distinguish positions that differ
regarding their prosodic context.
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Representation Prosodic Strings
Prosodic Strings
Sound change occurs more frequently in prosodically weak
positions (Geisler 1992).
Given a sonority profile, one can distinguish positions that differ
regarding their prosodic context.
Prosodic strings indicate different prosodic contexts for each
segment.
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Representation Prosodic Strings
Prosodic Strings
Sound change occurs more frequently in prosodically weak
positions (Geisler 1992).
Given a sonority profile, one can distinguish positions that differ
regarding their prosodic context.
Prosodic strings indicate different prosodic contexts for each
segment.
Substitution scores and gap penalties can be modified depending
on the underlying prosodic string.
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Representation Prosodic Strings
Prosodic Strings
Sound change occurs more frequently in prosodically weak
positions (Geisler 1992).
Given a sonority profile, one can distinguish positions that differ
regarding their prosodic context.
Prosodic strings indicate different prosodic contexts for each
segment.
Substitution scores and gap penalties can be modified depending
on the underlying prosodic string.
Prosodic strings are an alternative to n-gram approaches: they also
handle context, but their advantage is that they are more abstract
and less data-dependent than n-grams.
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Representation Prosodic Strings
Prosodic Strings
j a b ə l k a
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Representation Prosodic Strings
Prosodic Strings
j a b ə l k a
sonority
increases
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Representation Prosodic Strings
Prosodic Strings
j a b ə l k a
↑ △ ↑ △ ↓ ↑ △
↑ ascending
△ maximum
↓ descending
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Representation Prosodic Strings
Prosodic Strings
j a b ə l k a
↑ △ ↑ △ ↓ ↑ △
o strong
weak
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Representation Prosodic Strings
Prosodic Strings
phonetic sequence j a b ə l k a
SCA model J A P E L K A
ASJP model y a b I l k a
DOLGO model J V P V R K V
sonority profile 6 7 1 7 5 1 7
prosodic string # v C v c C >
Relative Weight 2.0 1.5 1.5 1.3 1.1 1.5 0.7
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Analysis
*
*
*
*
*
*
*
*
* *
*
*
*
v o l - d e m o r t
v - l a d i m i r -
v a l - d e m a r -
Analysis
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Analysis Phonetic Alignment
Sound-Class-Based Phonetic Alignment (SCA)
List, JM (2012). “SCA. Phonetic alignment based on sound classes”. In: New directions
in logic, lan- guage, and computation. Ed. by M Slavkovik and D Lassiter. Berlin and
Heidelberg: Springer, 32–51.
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Analysis Phonetic Alignment
Sound-Class-Based Phonetic Alignment (SCA)
List, JM (2012). “SCA. Phonetic alignment based on sound classes”. In: New directions
in logic, lan- guage, and computation. Ed. by M Slavkovik and D Lassiter. Berlin and
Heidelberg: Springer, 32–51.
method for pairwise and multiple phonetic alignment
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Analysis Phonetic Alignment
Sound-Class-Based Phonetic Alignment (SCA)
List, JM (2012). “SCA. Phonetic alignment based on sound classes”. In: New directions
in logic, lan- guage, and computation. Ed. by M Slavkovik and D Lassiter. Berlin and
Heidelberg: Springer, 32–51.
method for pairwise and multiple phonetic alignment
internal sequence representation as sound classes and prosodic
strings
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Analysis Phonetic Alignment
Sound-Class-Based Phonetic Alignment (SCA)
List, JM (2012). “SCA. Phonetic alignment based on sound classes”. In: New directions
in logic, lan- guage, and computation. Ed. by M Slavkovik and D Lassiter. Berlin and
Heidelberg: Springer, 32–51.
method for pairwise and multiple phonetic alignment
internal sequence representation as sound classes and prosodic
strings
supports global, local, semi-global, and diagonal alignment
analyses
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Analysis Phonetic Alignment
Sound-Class-Based Phonetic Alignment (SCA)
List, JM (2012). “SCA. Phonetic alignment based on sound classes”. In: New directions
in logic, lan- guage, and computation. Ed. by M Slavkovik and D Lassiter. Berlin and
Heidelberg: Springer, 32–51.
method for pairwise and multiple phonetic alignment
internal sequence representation as sound classes and prosodic
strings
supports global, local, semi-global, and diagonal alignment
analyses
handles secondary sequence structures (morpheme, syllable
boundaries)
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Analysis Phonetic Alignment
Sound-Class-Based Phonetic Alignment (SCA)
List, JM (2012). “SCA. Phonetic alignment based on sound classes”. In: New directions
in logic, lan- guage, and computation. Ed. by M Slavkovik and D Lassiter. Berlin and
Heidelberg: Springer, 32–51.
method for pairwise and multiple phonetic alignment
internal sequence representation as sound classes and prosodic
strings
supports global, local, semi-global, and diagonal alignment
analyses
handles secondary sequence structures (morpheme, syllable
boundaries)
can identify swapped sites in multiple phonetic alignments
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Analysis Phonetic Alignment
Sound-Class-Based phonetic Alignment (SCA)
INPUT
jabl̩ko
jabəlka
jabləkə
japkɔ
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Analysis Phonetic Alignment
Sound-Class-Based phonetic Alignment (SCA)
CONVERSION (1)
jabl̩ko → JAPLKU
jabəlka → JAPELKA
jabləkə → JAPLEKE
japkɔ → JAPKU
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Analysis Phonetic Alignment
Sound-Class-Based phonetic Alignment (SCA)
CONVERSION (2)
jabl̩ko → #VCVC>
jabəlka → #VCVcC>
jabləkə → #VCCVC>
japkɔ → #VcC>
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Analysis Phonetic Alignment
Sound-Class-Based phonetic Alignment (SCA)
ALIGNMENT
J A P - L - K U
J A P E L - K A
J A P - L E K E
J A P - - - K U
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Analysis Phonetic Alignment
Sound-Class-Based phonetic Alignment (SCA)
OUTPUT
j a b - l̩ - k o
j a b ə l - k a
j a b - l ə k ə
j a p - - - k ɔ
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Analysis Cognate Detection
LexStat
List, JM (2012): “LexStat. Automatic detection of cognates in multilingual word-
lists”. In: Proceedings of the EACL 2012 Joint Workshop of Visualization of
Linguistic Patterns and Uncovering Language History from Multilingual Resour-
ces.“LINGVIS & UNCLH 2012” (Avignon, 04/23–04/24/2012).
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Analysis Cognate Detection
LexStat
List, JM (2012): “LexStat. Automatic detection of cognates in multilingual word-
lists”. In: Proceedings of the EACL 2012 Joint Workshop of Visualization of
Linguistic Patterns and Uncovering Language History from Multilingual Resour-
ces.“LINGVIS & UNCLH 2012” (Avignon, 04/23–04/24/2012).
multilingual and language-specific method for cognate detection
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Analysis Cognate Detection
LexStat
List, JM (2012): “LexStat. Automatic detection of cognates in multilingual word-
lists”. In: Proceedings of the EACL 2012 Joint Workshop of Visualization of
Linguistic Patterns and Uncovering Language History from Multilingual Resour-
ces.“LINGVIS & UNCLH 2012” (Avignon, 04/23–04/24/2012).
multilingual and language-specific method for cognate detection
alignment-based detection of regular sound correspondences
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Analysis Cognate Detection
LexStat
List, JM (2012): “LexStat. Automatic detection of cognates in multilingual word-
lists”. In: Proceedings of the EACL 2012 Joint Workshop of Visualization of
Linguistic Patterns and Uncovering Language History from Multilingual Resour-
ces.“LINGVIS & UNCLH 2012” (Avignon, 04/23–04/24/2012).
multilingual and language-specific method for cognate detection
alignment-based detection of regular sound correspondences
re-alignment of the data with help of correspondence-based
scoring functions
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Analysis Cognate Detection
LexStat
List, JM (2012): “LexStat. Automatic detection of cognates in multilingual word-
lists”. In: Proceedings of the EACL 2012 Joint Workshop of Visualization of
Linguistic Patterns and Uncovering Language History from Multilingual Resour-
ces.“LINGVIS & UNCLH 2012” (Avignon, 04/23–04/24/2012).
multilingual and language-specific method for cognate detection
alignment-based detection of regular sound correspondences
re-alignment of the data with help of correspondence-based
scoring functions
flat cluster analysis for the detection of cognate sets
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Analysis Cognate Detection
LexStat
ID Taxa Word Gloss GlossID IPA .....
... ... ... ... ... ... ...
21 German Frau woman 20 frau ...
22 Dutch vrouw woman 20 vrɑu ...
23 English woman woman 20 wʊmən ...
24 Danish kvinde woman 20 kvenə ...
25 Swedish kvinna woman 20 kviːna ...
26 Norwegian kvine woman 20 kʋinə ...
... ... ... ... ... ... ...
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Analysis Cognate Detection
LexStat
ID Taxa Word Gloss GlossID IPA CogID
... ... ... ... ... ... ...
21 German Frau woman 20 frau 1
22 Dutch vrouw woman 20 vrɑu 1
23 English woman woman 20 wʊmən 2
24 Danish kvinde woman 20 kvenə 3
25 Swedish kvinna woman 20 kviːna 3
26 Norwegian kvine woman 20 kʋinə 3
... ... ... ... ... ... ...
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Analysis Cognate Detection
LexStat
ID Taxa Word Gloss GlossID IPA CogID
... ... ... ... ... ... ...
21 German Frau woman 20 frau 1
22 Dutch vrouw woman 20 vrɑu 1
23 English woman woman 20 wʊmən 2
24 Danish kvinde woman 20 kvenə 3
25 Swedish kvinna woman 20 kviːna 3
26 Norwegian kvine woman 20 kʋinə 3
... ... ... ... ... ... ...
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Analysis Borrowing Detection
Phylogeny-Based Borrowing Detection (PhyBo)
List, JM, S Nelson-Sathi, H Geisler, und W Martin (2014). “Networks of lexical
borrowing and lateral gene transfer in language and genome evolution”. BioEs-
says 36.2, 141–150.
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Analysis Borrowing Detection
Phylogeny-Based Borrowing Detection (PhyBo)
List, JM, S Nelson-Sathi, H Geisler, und W Martin (2014). “Networks of lexical
borrowing and lateral gene transfer in language and genome evolution”. BioEs-
says 36.2, 141–150.
phylogeny-based method for borrowing detection
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Analysis Borrowing Detection
Phylogeny-Based Borrowing Detection (PhyBo)
List, JM, S Nelson-Sathi, H Geisler, und W Martin (2014). “Networks of lexical
borrowing and lateral gene transfer in language and genome evolution”. BioEs-
says 36.2, 141–150.
phylogeny-based method for borrowing detection
uses parsimony analyses to detect cognate sets which cannot be
explained with help of a given reference tree
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Analysis Borrowing Detection
Phylogeny-Based Borrowing Detection (PhyBo)
List, JM, S Nelson-Sathi, H Geisler, und W Martin (2014). “Networks of lexical
borrowing and lateral gene transfer in language and genome evolution”. BioEs-
says 36.2, 141–150.
phylogeny-based method for borrowing detection
uses parsimony analyses to detect cognate sets which cannot be
explained with help of a given reference tree
selection of the best weighting model based on similar vocabulary
size distribution
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Analysis Borrowing Detection
Phylogeny-Based Borrowing Detection (PhyBo)
List, JM, S Nelson-Sathi, H Geisler, und W Martin (2014). “Networks of lexical
borrowing and lateral gene transfer in language and genome evolution”. BioEs-
says 36.2, 141–150.
phylogeny-based method for borrowing detection
uses parsimony analyses to detect cognate sets which cannot be
explained with help of a given reference tree
selection of the best weighting model based on similar vocabulary
size distribution
reconstructs a minimal lateral network of the data in which the
minimal amount of lateral connections inferred by the best model is
displayed
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Analysis Borrowing Detection
Phylogeny-Based Borrowing Detection (PhyBo)
.
.
---Lánzhōu
.
Fùzhōu --
.
Xiāngtàn --
.
M
ěixiàn
--
.
H
ongkong
--
.
---Wǔhàn
.
---Běijīng
.
---Kùnmíng
.
Hángzhōu
--
.
Xiàmén --
.
---Chéngdū
.
Sùzhōu
--
.
Shànghǎi --
.
Táiběi --
.
---Zhèngzhōu
.
Shèxiàn --
.
---Nánjīng
.
---Guìyáng
.
W
énzhōu
--
.
N
ánníng
--
.
Tūnxī --
.
---Tiānjìn
.
Shāntóu --
.
---Xīníng
.
---Q
īngdǎo
.
---Ürüm
qi
.
---Píngyáo
.
Nánchàng --
.
---Tàiyuán
.
Chángshā --
.
Hǎikǒu --
.
---Héfèi
.
Jiàn'ǒu --
.
---Yīnchuàn
.
---Hohhot
.
Táoyuán --
.
---Xī'ān
.
G
uǎngzhōu
--
.
---Harbin
.
---Jìnán
.
0
.
0
.
0
.
Inferred Links
Reference tree of the Chinese dialects
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Analysis Borrowing Detection
Phylogeny-Based Borrowing Detection (PhyBo)
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.
---Lánzhōu
.
Fùzhōu --
.
Xiāngtàn --
.
M
ěixiàn
--
.
H
ongkong
--
.
---Wǔhàn
.
---Běijīng
.
---Kùnmíng
.
Hángzhōu
--
.
Xiàmén --
.
---Chéngdū
.
Sùzhōu
--
.
Shànghǎi --
.
Táiběi --
.
---Zhèngzhōu
.
Shèxiàn --
.
---Nánjīng
.
---Guìyáng
.
W
énzhōu
--
.
N
ánníng
--
.
Tūnxī --
.
---Tiānjìn
.
Shāntóu --
.
---Xīníng
.
---Q
īngdǎo
.
---Ürüm
qi
.
---Píngyáo
.
Nánchàng --
.
---Tàiyuán
.
Chángshā --
.
Hǎikǒu --
.
---Héfèi
.
Jiàn'ǒu --
.
---Yīnchuàn
.
---Hohhot
.
Táoyuán --
.
---Xī'ān
.
G
uǎngzhōu
--
.
---Harbin
.
---Jìnán
.
0
.
0
.
0
.
Inferred Links
MLN analysis, no borrowing allowed
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Analysis Borrowing Detection
Phylogeny-Based Borrowing Detection (PhyBo)
.
.
---Lánzhōu
.
Fùzhōu --
.
Xiāngtàn --
.
M
ěixiàn
--
.
H
ongkong
--
.
---Wǔhàn
.
---Běijīng
.
---Kùnmíng
.
Hángzhōu
--
.
Xiàmén --
.
---Chéngdū
.
Sùzhōu
--
.
Shànghǎi --
.
Táiběi --
.
---Zhèngzhōu
.
Shèxiàn --
.
---Nánjīng
.
---Guìyáng
.
W
énzhōu
--
.
N
ánníng
--
.
Tūnxī --
.
---Tiānjìn
.
Shāntóu --
.
---Xīníng
.
---Q
īngdǎo
.
---Ürüm
qi
.
---Píngyáo
.
Nánchàng --
.
---Tàiyuán
.
Chángshā --
.
Hǎikǒu --
.
---Héfèi
.
Jiàn'ǒu --
.
---Yīnchuàn
.
---Hohhot
.
Táoyuán --
.
---Xī'ān
.
G
uǎngzhōu
--
.
---Harbin
.
---Jìnán
.
1
.
4
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8
.
Inferred Links
MLN analysis, best fit of borrowing and inheritance
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Slide 69
Slide 69 text
Examples
Examples
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Slide 70
Slide 70 text
Examples
Examples in form of an IPython Notebook along with a HowTo-script
will be uploaded to http://lingulist.de/talks.php.
26 / 30
Slide 71
Slide 71 text
Outlook
Outlook
27 / 30
Slide 72
Slide 72 text
Outlook
We need to improve both the methods we use and the way we present
them to the linguistic world. The following are just a few pending
problems:
28 / 30
Slide 73
Slide 73 text
Outlook
We need to improve both the methods we use and the way we present
them to the linguistic world. The following are just a few pending
problems:
make it easier for non-programmers to access LingPy (a GUI, or
some simple terminal-based framework, a full tutorial)
28 / 30
Slide 74
Slide 74 text
Outlook
We need to improve both the methods we use and the way we present
them to the linguistic world. The following are just a few pending
problems:
make it easier for non-programmers to access LingPy (a GUI, or
some simple terminal-based framework, a full tutorial)
make the results of LingPy analyses more transparent (plots,
findings, predictions)
28 / 30
Slide 75
Slide 75 text
Outlook
We need to improve both the methods we use and the way we present
them to the linguistic world. The following are just a few pending
problems:
make it easier for non-programmers to access LingPy (a GUI, or
some simple terminal-based framework, a full tutorial)
make the results of LingPy analyses more transparent (plots,
findings, predictions)
conduct rigorous testing of LingPy analyses (benchmarking, test
parameter settings)
28 / 30
Slide 76
Slide 76 text
Outlook
We need to improve both the methods we use and the way we present
them to the linguistic world. The following are just a few pending
problems:
make it easier for non-programmers to access LingPy (a GUI, or
some simple terminal-based framework, a full tutorial)
make the results of LingPy analyses more transparent (plots,
findings, predictions)
conduct rigorous testing of LingPy analyses (benchmarking, test
parameter settings)
develop the methods further and include further methods
(borrowing detection, automatic linguistic reconstruction,
morpheme detection)
28 / 30
Slide 77
Slide 77 text
That’s all for now...
29 / 30
Slide 78
Slide 78 text
Thank You!
30 / 30