AustralieFrV1, PubMed, Corpus, bibRecord, 001316

The Potential of Automatic Word Comparison for Historical Linguistics.

Identifieur interne : 001316 ( PubMed/Corpus ); précédent : 001315; suivant : 001317

The Potential of Automatic Word Comparison for Historical Linguistics.

Auteurs : Johann-Mattis List ; Simon J. Greenhill ; Russell D. Gray

Source :

PloS one [ 1932-6203 ] ; 2017.

RBID : pubmed:28129337

English descriptors

KwdEn :
- Algorithms, Cluster Analysis, Databases, Factual, History, Ancient, Humans, Language (history), Linguistics (history), Linguistics (statistics & numerical data), Semantics, Vocabulary.
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 Infomap. 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.

DOI: 10.1371/journal.pone.0170046
PubMed: 28129337

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pubmed:28129337

Le document en format XML

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<author><name sortKey="List, Johann Mattis" sort="List, Johann Mattis" uniqKey="List J" first="Johann-Mattis" last="List">Johann-Mattis List</name>
<affiliation><nlm:affiliation>Centre des Recherches Linguistiques sur l'Asie Orientale, École des Hautes Études en Sciences Sociales, 2 Rue de Lille, 75007 Paris, France.</nlm:affiliation>
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<author><name sortKey="Greenhill, Simon J" sort="Greenhill, Simon J" uniqKey="Greenhill S" first="Simon J" last="Greenhill">Simon J. Greenhill</name>
<affiliation><nlm:affiliation>Department for Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Kahlaische Straße 10, 07743, Jena, Germany.</nlm:affiliation>
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<author><name sortKey="Gray, Russell D" sort="Gray, Russell D" uniqKey="Gray R" first="Russell D" last="Gray">Russell D. Gray</name>
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<front><div type="abstract" xml:lang="en">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 Infomap. 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.</div>
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The Potential of Automatic Word Comparison for Historical Linguistics.

The Potential of Automatic Word Comparison for Historical Linguistics.

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English descriptors

Abstract

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