The Potential of Automatic Word Comparison for Historical Linguistics
Identifieur interne : 000258 ( Main/Exploration ); précédent : 000257; suivant : 000259The Potential of Automatic Word Comparison for Historical Linguistics
Auteurs : Johann-Mattis List [France] ; Simon J. Greenhill [Allemagne, Australie] ; Russell D. Gray [Allemagne]Source :
- PLoS ONE [ 1932-6203 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- history : Language, Linguistics.
- statistics & numerical data : Linguistics.
- Algorithms, Cluster Analysis, Databases, Factual, History, Ancient, Humans, Semantics, Vocabulary.
Abstract
The amount of data from languages spoken all over the world is rapidly increasing. Traditional manual methods in historical linguistics need to face the challenges brought by this influx of data. Automatic approaches to word comparison could provide invaluable help to pre-analyze data which can be later enhanced by experts. In this way, computational approaches can take care of the repetitive and schematic tasks leaving experts to concentrate on answering interesting questions. Here we test the potential of automatic methods to detect etymologically related words (cognates) in cross-linguistic data. Using a newly compiled database of expert cognate judgments across five different language families, we compare how well different automatic approaches distinguish related from unrelated words. Our results show that automatic methods can identify cognates with a very high degree of accuracy, reaching 89% for the best-performing method
Url:
DOI: 10.1371/journal.pone.0170046
PubMed: 28129337
PubMed Central: 5271327
Affiliations:
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Greenhill</name><affiliation wicri:level="1"><nlm:aff id="aff002"><addr-line>Department for Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, 07743, Jena, Germany</addr-line></nlm:aff><country xml:lang="fr">Allemagne</country><wicri:regionArea>Department for Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, 07743, Jena</wicri:regionArea><wicri:noRegion>07743, Jena</wicri:noRegion><wicri:noRegion>07743, Jena</wicri:noRegion><wicri:noRegion>Jena</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="aff003"><addr-line>ARC Centre of Excellence for the Dynamics of Language, Australian National University, Canberra, 2600, Australia</addr-line></nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>ARC Centre of Excellence for the Dynamics of Language, Australian National University, Canberra, 2600</wicri:regionArea><wicri:noRegion>2600</wicri:noRegion></affiliation></author><author><name sortKey="Gray, Russell D" sort="Gray, Russell D" uniqKey="Gray R" first="Russell D." last="Gray">Russell D. Gray</name><affiliation wicri:level="1"><nlm:aff id="aff002"><addr-line>Department for Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, 07743, Jena, Germany</addr-line></nlm:aff><country xml:lang="fr">Allemagne</country><wicri:regionArea>Department for Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, 07743, Jena</wicri:regionArea><wicri:noRegion>07743, Jena</wicri:noRegion><wicri:noRegion>07743, Jena</wicri:noRegion><wicri:noRegion>Jena</wicri:noRegion></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Cluster Analysis</term><term>Databases, Factual</term><term>History, Ancient</term><term>Humans</term><term>Language (history)</term><term>Linguistics (history)</term><term>Linguistics (statistics & numerical data)</term><term>Semantics</term><term>Vocabulary</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Analyse de regroupements</term><term>Bases de données factuelles</term><term>Histoire ancienne</term><term>Humains</term><term>Langage (histoire)</term><term>Linguistique ()</term><term>Linguistique (histoire)</term><term>Sémantique</term><term>Vocabulaire</term></keywords><keywords scheme="MESH" qualifier="histoire" xml:lang="fr"><term>Langage</term><term>Linguistique</term></keywords><keywords scheme="MESH" qualifier="history" xml:lang="en"><term>Language</term><term>Linguistics</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Linguistics</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Cluster Analysis</term><term>Databases, Factual</term><term>History, Ancient</term><term>Humans</term><term>Semantics</term><term>Vocabulary</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de regroupements</term><term>Bases de données factuelles</term><term>Histoire ancienne</term><term>Humains</term><term>Linguistique</term><term>Sémantique</term><term>Vocabulaire</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The amount of data from languages spoken all over the world is rapidly increasing. Traditional manual methods in historical linguistics need to face the challenges brought by this influx of data. Automatic approaches to word comparison could provide invaluable help to pre-analyze data which can be later enhanced by experts. In this way, computational approaches can take care of the repetitive and schematic tasks leaving experts to concentrate on answering interesting questions. Here we test the potential of automatic methods to detect etymologically related words (cognates) in cross-linguistic data. Using a newly compiled database of expert cognate judgments across five different language families, we compare how well different automatic approaches distinguish related from unrelated words. Our results show that automatic methods can identify cognates with a very high degree of accuracy, reaching 89% for the best-performing method <italic>Infomap</italic>. We identify the specific strengths and weaknesses of these different methods and point to major challenges for future approaches. Current automatic approaches for cognate detection—although not perfect—could become an important component of future research in historical linguistics.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Greenhill, Sj" uniqKey="Greenhill S">SJ Greenhill</name></author><author><name sortKey="Blust, R" uniqKey="Blust R">R Blust</name></author><author><name sortKey="Gray, Rd" uniqKey="Gray R">RD Gray</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dunn, M" uniqKey="Dunn M">M Dunn</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Greenhill, Sj" uniqKey="Greenhill S">SJ Greenhill</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kitchen, A" uniqKey="Kitchen A">A Kitchen</name></author><author><name sortKey="Ehret, C" uniqKey="Ehret C">C Ehret</name></author><author><name sortKey="Assefa, S" uniqKey="Assefa S">S 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Hamed</name></author><author><name sortKey="Wang, F" uniqKey="Wang F">F Wang</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Starostin, G" uniqKey="Starostin G">G Starostin</name></author><author><name sortKey="Krylov, P" uniqKey="Krylov P">P Krylov</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="List, Jm" uniqKey="List J">JM List</name></author></analytic></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Allemagne</li><li>Australie</li><li>France</li></country><region><li>Île-de-France</li></region><settlement><li>Paris</li></settlement></list><tree><country name="France"><region name="Île-de-France"><name sortKey="List, Johann Mattis" sort="List, Johann Mattis" uniqKey="List J" first="Johann-Mattis" last="List">Johann-Mattis List</name></region></country><country name="Allemagne"><noRegion><name sortKey="Greenhill, Simon J" sort="Greenhill, Simon J" uniqKey="Greenhill S" first="Simon J." last="Greenhill">Simon J. Greenhill</name></noRegion><name sortKey="Gray, Russell D" sort="Gray, Russell D" uniqKey="Gray R" first="Russell D." last="Gray">Russell D. Gray</name></country><country name="Australie"><noRegion><name sortKey="Greenhill, Simon J" sort="Greenhill, Simon J" uniqKey="Greenhill S" first="Simon J." last="Greenhill">Simon J. Greenhill</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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|area= AustralieFrV1
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|texte= The Potential of Automatic Word Comparison for Historical Linguistics
}}
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