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Annotation and Analysis of Cross-Linguistic Lexical
Data in Historical Linguistics
Toward the Establishment of Standards and Best Practices
Johann-Mattis List
Research Group “Computer-Assisted Language Comparison”
Department of Linguistic and Cultural Evolution
Max-Planck Institute for the Science of Human History
Jena, Germany
2017/07/07
very
long
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P(A|B)=P(B|A)...
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Background
Background
data
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Background Data
Data in Linguistics
Linguistics is a data-driven discipline:
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Background Data
Data in Linguistics
Linguistics is a data-driven discipline:
grammars
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Background Data
Data in Linguistics
Linguistics is a data-driven discipline:
grammars
dictionaries
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Background Data
Data in Linguistics
Linguistics is a data-driven discipline:
grammars
dictionaries
texts
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Background Data
Data in Linguistics
Linguistics is a data-driven discipline:
grammars
dictionaries
texts
etc.
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Background Data
Data in Comparative Linguistics
Comparative linguistics is also a data-driven discipline:
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Background Data
Data in Comparative Linguistics
Comparative linguistics is also a data-driven discipline:
historical grammars
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Background Data
Data in Comparative Linguistics
Comparative linguistics is also a data-driven discipline:
historical grammars
etymological dictionaries
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Background Data
Data in Comparative Linguistics
Comparative linguistics is also a data-driven discipline:
historical grammars
etymological dictionaries
annotated texts
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Background Data
Data in Comparative Linguistics
Comparative linguistics is also a data-driven discipline:
historical grammars
etymological dictionaries
annotated texts
etc.
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Background Annotation
Annotation in Linguistics
Annotation of data is crucial in linguistics:
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Background Annotation
Annotation in Linguistics
Annotation of data is crucial in linguistics:
inter-linear glossed text
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Background Annotation
Annotation in Linguistics
Annotation of data is crucial in linguistics:
inter-linear glossed text
“glossing” a words meaning in the literature
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Background Annotation
Annotation in Linguistics
Annotation of data is crucial in linguistics:
inter-linear glossed text
“glossing” a words meaning in the literature
etc.
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Background Annotation
Annotation in Comparative Linguistics
Annotation of data is also crucial in comparative linguistics:
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Background Annotation
Annotation in Comparative Linguistics
Annotation of data is also crucial in comparative linguistics:
indicating that words are cognate
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Background Annotation
Annotation in Comparative Linguistics
Annotation of data is also crucial in comparative linguistics:
indicating that words are cognate
using phonetic transcriptions to indicate how words sound
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Background Annotation
Annotation in Comparative Linguistics
Annotation of data is also crucial in comparative linguistics:
indicating that words are cognate
using phonetic transcriptions to indicate how words sound
etc.
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Background Analysis
Analysis in Linguistics
Analysis of data is crucial in linguistics:
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Background Analysis
Analysis in Linguistics
Analysis of data is crucial in linguistics:
syntactic analysis in any syntactic framework (ideally with less than
three examples and many asterisks)
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Background Analysis
Analysis in Linguistics
Analysis of data is crucial in linguistics:
syntactic analysis in any syntactic framework (ideally with less than
three examples and many asterisks)
etc.
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Background Analysis
Analysis in Comparative Linguistics
Analysis of data is also crucial in comparative linguistics:
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Background Analysis
Analysis in Comparative Linguistics
Analysis of data is also crucial in comparative linguistics:
alignment analysis (show where words are cognate, i.e., in which
segments they coincide)
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Background Analysis
Analysis in Comparative Linguistics
Analysis of data is also crucial in comparative linguistics:
alignment analysis (show where words are cognate, i.e., in which
segments they coincide)
correspondence pattern analysis (show the most frequently
corresponding sounds for a number of related languages)
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Background Analysis
Analysis in Comparative Linguistics
Analysis of data is also crucial in comparative linguistics:
alignment analysis (show where words are cognate, i.e., in which
segments they coincide)
correspondence pattern analysis (show the most frequently
corresponding sounds for a number of related languages)
etymological analysis (show how the original meaning of a word
evolved, etc.)
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Background Analysis
Analysis in Comparative Linguistics
Analysis of data is also crucial in comparative linguistics:
alignment analysis (show where words are cognate, i.e., in which
segments they coincide)
correspondence pattern analysis (show the most frequently
corresponding sounds for a number of related languages)
etymological analysis (show how the original meaning of a word
evolved, etc.)
etc.
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Problems
Problems
!
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Problems Availability
Availability
Linguists all over the world publish papers without sharing their data and
analyses. SO SAD for science! #MakeLinguisticsGreatAgain #FakePubs
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Problems Availability
Availability
Sharing is not yet considered as economic in linguistic research:
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Problems Availability
Availability
Sharing is not yet considered as economic in linguistic research:
journals still accept too many papers which do not share full material
and code they used for their analyses
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Problems Availability
Availability
Sharing is not yet considered as economic in linguistic research:
journals still accept too many papers which do not share full material
and code they used for their analyses
if authors share, they make it often very difficult to use the data for
testing by converting data which was in tabular form originally into
PDF, which may at times even not even be searchable, etc.
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Problems Availability
Availability
Sharing is not yet considered as economic in linguistic research:
journals still accept too many papers which do not share full material
and code they used for their analyses
if authors share, they make it often very difficult to use the data for
testing by converting data which was in tabular form originally into
PDF, which may at times even not even be searchable, etc.
even if authors share, they may often forget to share their explicit
annotations and analyses, thus leaving the readers alone with the
raw data
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Problems Transparency
Transparency
Taken from a blog by Bengtson (2017) at http://euskararenjatorria.net/?p=26071
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Problems Transparency
Transparency
Annotations and analyses often lack transparency and cannot be
understood in the raw form in which they are provided:
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Problems Transparency
Transparency
Annotations and analyses often lack transparency and cannot be
understood in the raw form in which they are provided:
words are declared as cognate, but nobody really knows WHERE
the words should be cognate
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Problems Transparency
Transparency
Annotations and analyses often lack transparency and cannot be
understood in the raw form in which they are provided:
words are declared as cognate, but nobody really knows WHERE
the words should be cognate
correspondences are labeled as “regular” without giving sufficient
proof for this neither in the texts nor in the data
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Problems Transparency
Transparency
Annotations and analyses often lack transparency and cannot be
understood in the raw form in which they are provided:
words are declared as cognate, but nobody really knows WHERE
the words should be cognate
correspondences are labeled as “regular” without giving sufficient
proof for this neither in the texts nor in the data
sources are omitted and readers are left alone with a bunch of word
lists of which nobody knows where they were exactly taken from
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Problems Comparability
Comparability
東童孺務口覯後松公蜂注足濡送重局功粟婁句主數束芻用豐葚譖洧侑訧鮪囿又尤友有右萏三男南琛諶煁甚枕風深任弓牛龜舊裘雄丘郿桋騤厎佽湄袺蓍鴟侐悸穗惟秭遟湝只祗茨履葵匕死視唯季矢偕祁比旨脂指耆毗階維妣屎師資咨兕
Karlgren (1954)
Li (1971)
Wáng (1980)
Zhèngzhāng (2003)
Pān (2000)
Starostin (1989)
Baxter and Sagart (2014)
Schuessler (2007)
? a e i o u æ ɑ ɔ ə ɯ ʊ
Different reconstructions for Old Chinese, taken from List et al. (2017)
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Problems Comparability
Comparability
It is extremely hard to compare different opinions in comparative
linguistics:
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Problems Comparability
Comparability
It is extremely hard to compare different opinions in comparative
linguistics:
data sources are insufficiently reported
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Problems Comparability
Comparability
It is extremely hard to compare different opinions in comparative
linguistics:
data sources are insufficiently reported
computational studies are based on different subsets of the data
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Problems Comparability
Comparability
It is extremely hard to compare different opinions in comparative
linguistics:
data sources are insufficiently reported
computational studies are based on different subsets of the data
analyses exclude or focus on specific parts of a given dataset
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Problems Summary
Summary
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Problems Summary
Summary
Although linguistics and especially comparative linguistics is
an extremely data-driven discipline, we often still behave as
if it was all about prose and discourse. In times when digital
applications make the lives of scientists a lot easier, linguists
need to find ways to transfer their analyses into the new age.
This can only be done if we drastically increase the availabil-
ity, transparency, and comparability of data in linguistics.
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Cross-Linguistic Data Formats
Cross-Linguistic Data Formats
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Cross-Linguistic Data Formats General Ideas
General Ideas
The Cross-Linguistic Data Formats initiative (Forkel et al. 2016,
http://cldf.clld.org) aims at increasing the comparability of
cross-linguistic data and analyses. It comes along with:
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Cross-Linguistic Data Formats General Ideas
General Ideas
The Cross-Linguistic Data Formats initiative (Forkel et al. 2016,
http://cldf.clld.org) aims at increasing the comparability of
cross-linguistic data and analyses. It comes along with:
standardization efforts (linguistic meta-data-bases like Glottolog
and Concepticon),
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Cross-Linguistic Data Formats General Ideas
General Ideas
The Cross-Linguistic Data Formats initiative (Forkel et al. 2016,
http://cldf.clld.org) aims at increasing the comparability of
cross-linguistic data and analyses. It comes along with:
standardization efforts (linguistic meta-data-bases like Glottolog
and Concepticon),
software APIs which help to test whether data conforms to
standards, and
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Cross-Linguistic Data Formats General Ideas
General Ideas
The Cross-Linguistic Data Formats initiative (Forkel et al. 2016,
http://cldf.clld.org) aims at increasing the comparability of
cross-linguistic data and analyses. It comes along with:
standardization efforts (linguistic meta-data-bases like Glottolog
and Concepticon),
software APIs which help to test whether data conforms to
standards, and
working examples for best practice.
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Cross-Linguistic Data Formats General Ideas
General Ideas
As of now, a couple of software tools (LingPy, Beastling, EDICTOR)
support CLDF to some degree. In the future, we hope that the number of
users will increase, and that the community helps to develop the formats
further.
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Cross-Linguistic Data Formats Technical Aspects
Technical Aspects
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Cross-Linguistic Data Formats Technical Aspects
Technical Aspects
See http://github.com/glottobank/cldf for details,
discussions, and working examples.
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Cross-Linguistic Data Formats Technical Aspects
Technical Aspects
See http://github.com/glottobank/cldf for details,
discussions, and working examples.
Format for machine-readable specification is CSV with metadata in
JSON, following the W3C’s Model for Tabular Data and Metadata
on the Web
(http://www.w3.org/TR/tabular-data-model/).
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Cross-Linguistic Data Formats Technical Aspects
Technical Aspects
See http://github.com/glottobank/cldf for details,
discussions, and working examples.
Format for machine-readable specification is CSV with metadata in
JSON, following the W3C’s Model for Tabular Data and Metadata
on the Web
(http://www.w3.org/TR/tabular-data-model/).
CLDF ontology builds and expands upon the General Ontology for
Linguistic Description (GOLD).
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Cross-Linguistic Data Formats Technical Aspects
Technical Aspects
See http://github.com/glottobank/cldf for details,
discussions, and working examples.
Format for machine-readable specification is CSV with metadata in
JSON, following the W3C’s Model for Tabular Data and Metadata
on the Web
(http://www.w3.org/TR/tabular-data-model/).
CLDF ontology builds and expands upon the General Ontology for
Linguistic Description (GOLD).
A pycldf API in Python is currently in preparation and will help users
to evaluate whether their data conforms to CLDF.
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Cross-Linguistic Data Formats Technical Aspects
The Zen of CLDF (following Robert Forkel)
A lot of the guidelines put forward for Python code in The Zen of Python
(https://www.python.org/dev/peps/pep-0020/) can also be
used to characterize CLDF. In particular
Explicit is better than implicit.
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Cross-Linguistic Data Formats Technical Aspects
The Zen of CLDF (following Robert Forkel)
A lot of the guidelines put forward for Python code in The Zen of Python
(https://www.python.org/dev/peps/pep-0020/) can also be
used to characterize CLDF. In particular
Explicit is better than implicit.
Readability counts.
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Cross-Linguistic Data Formats Technical Aspects
The Zen of CLDF (following Robert Forkel)
A lot of the guidelines put forward for Python code in The Zen of Python
(https://www.python.org/dev/peps/pep-0020/) can also be
used to characterize CLDF. In particular
Explicit is better than implicit.
Readability counts.
Errors should never pass silently (Unless explicitly silenced).
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Cross-Linguistic Data Formats Technical Aspects
The Zen of CLDF (following Robert Forkel)
A lot of the guidelines put forward for Python code in The Zen of Python
(https://www.python.org/dev/peps/pep-0020/) can also be
used to characterize CLDF. In particular
Explicit is better than implicit.
Readability counts.
Errors should never pass silently (Unless explicitly silenced).
Simple is better than complex.
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Cross-Linguistic Data Formats Technical Aspects
The Zen of CLDF (following Robert Forkel)
A lot of the guidelines put forward for Python code in The Zen of Python
(https://www.python.org/dev/peps/pep-0020/) can also be
used to characterize CLDF. In particular
Explicit is better than implicit.
Readability counts.
Errors should never pass silently (Unless explicitly silenced).
Simple is better than complex.
In the face of ambiguity, refuse the temptation to guess.
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Cross-Linguistic Data Formats Standards
Standards
"The language, the meaning, and
the form of the linguistic sign are
the triple pillars upon which CLDF
rests. Together, they will restore
comparability of linguistic data in
the world."
— Tommen Baratheon
(King of the Iron Throne, † in 6x10)
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Cross-Linguistic Data Formats Standards
Standards
use Glottolog (Hammarström et al. 2017) to refer to language
varieties
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Cross-Linguistic Data Formats Standards
Standards
use Glottolog (Hammarström et al. 2017) to refer to language
varieties
use Concepticon (List et al. 2016) to refer to concepts
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Cross-Linguistic Data Formats Standards
Standards
use Glottolog (Hammarström et al. 2017) to refer to language
varieties
use Concepticon (List et al. 2016) to refer to concepts
develop a cross-linguistic phonetic notation system to evaluate
whether phonetic transcriptions for word forms conform to
cross-linguistic standards
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Cross-Linguistic Data Formats Standards
Standards: Concepticon
Concepticon (List et al. 2016)
link concept labels in published concept lists
(questionnaires) to concept sets
link concept sets to meta-data
define relations between concept sets
never link one concept in a given list to more than one
concept set (guarantees consistency)
provide an API to check the consistency of the data and
to query the data
provide a web-interface to browse through the data
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Cross-Linguistic Data Formats Standards
http://concepticon.clld.org
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Cross-Linguistic Data Formats Standards
Standards: Cross-Linguistic Phonetic Notations
There are too many IPAs!
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Cross-Linguistic Data Formats Standards
Standards: Cross-Linguistic Phonetic Notations
There are too many IPAs!
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Cross-Linguistic Data Formats Standards
Standards: Cross-Linguistic Phonetic Notations
Cross-Linguistic Phonetic Notations (in prep.)
normalize ambiguities of IPA
establish a pool of phonetic segments which are linked
to other datasets (Phoible, Moran et al. 2014, PBase,
Mielke 2008, etc.)
provide or link to feature systems or additional kinds of
metadata
provide generic transformation tables for frequently
used phonetic transcription systems
provide an API to check the consistency of a given
transcription
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Cross-Linguistic Data Formats Standards
GLD to CLPN
https://github.com/LinguList/Hmong-Mien_Language_family/
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Word Lists in CLDF
WORDLIST
DATA
Word Lists in CLDF
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Word Lists in CLDF Basic Aspects
Basic Aspects
ID (unique identifier for each word)
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Word Lists in CLDF Basic Aspects
Basic Aspects
ID (unique identifier for each word)
Language and Language_ID (Glottolog)
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Word Lists in CLDF Basic Aspects
Basic Aspects
ID (unique identifier for each word)
Language and Language_ID (Glottolog)
Concept and Paramter_ID (Concepticon)
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Word Lists in CLDF Basic Aspects
Basic Aspects
ID (unique identifier for each word)
Language and Language_ID (Glottolog)
Concept and Paramter_ID (Concepticon)
Value, Form, and Segments (CLPN)
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Word Lists in CLDF Basic Aspects
Basic Aspects
ID (unique identifier for each word)
Language and Language_ID (Glottolog)
Concept and Paramter_ID (Concepticon)
Value, Form, and Segments (CLPN)
Source (key of BibTex file)
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Word Lists in CLDF Basic Aspects
Basic Aspects
ID (unique identifier for each word)
Language and Language_ID (Glottolog)
Concept and Paramter_ID (Concepticon)
Value, Form, and Segments (CLPN)
Source (key of BibTex file)
Comment (free text)
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Word Lists in CLDF Etymological Annotation
Etymological Annotation
Concept
Cognate Set
Alignment
MEANING
RELATION
Morphemes
Slice
Form
Value
Segments
FORM
ID
A B
requires
important
language-internal
language-external
Language
LANGUAGE
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Word Lists in CLDF Examples
Examples: The Etymological Dictionary Editor
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Word Lists in CLDF Examples
Examples: The Etymological Dictionary Editor
web-based tool for the annotation of etymological data
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Word Lists in CLDF Examples
Examples: The Etymological Dictionary Editor
web-based tool for the annotation of etymological data
online available at http://edictor.digling.org
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Word Lists in CLDF Examples
Examples: The Etymological Dictionary Editor
web-based tool for the annotation of etymological data
online available at http://edictor.digling.org
description available in List (2017)
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Word Lists in CLDF Examples
Examples: The Etymological Dictionary Editor
web-based tool for the annotation of etymological data
online available at http://edictor.digling.org
description available in List (2017)
works client-side and offline
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Word Lists in CLDF Examples
Examples: The Etymological Dictionary Editor
web-based tool for the annotation of etymological data
online available at http://edictor.digling.org
description available in List (2017)
works client-side and offline
allows for morpheme annotation, alignment analysis, cognate
annotation, and phonological analysis
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Word Lists in CLDF Examples
Examples: The Etymological Dictionary Editor
web-based tool for the annotation of etymological data
online available at http://edictor.digling.org
description available in List (2017)
works client-side and offline
allows for morpheme annotation, alignment analysis, cognate
annotation, and phonological analysis
serves as a litmus test for the expressive power of wordlists in CLDF
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Word Lists in CLDF Examples
http://edictor.digling.org
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Outlook
Outlook
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Danke für Ihre Aufmerksamkeit!
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