ArticleScientific data2018
Global-scale phylogenetic linguistic inference from lexical resources.
Article in Scientific data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- Still No Evidence for an Effect of the Proportion of Non-Native Speakers on Natural Language Complexity.Entropy (Basel, Switzerland) · 2024Article
- Deep history of cultural and linguistic evolution among Central African hunter-gatherers.Nature human behaviour · 2024Article
- Languages with more speakers tend to be harder to (machine-)learn.Scientific reports · 2023Article
- Function predicts how people treat their dogs in a global sample.Scientific reports · 2023Article
- Kinbank: A global database of kinship terminology.PloS one · 2023Article
- Ultraviolet light affects the color vocabulary: evidence from 834 languages.Frontiers in psychology · 2023Article
- Tone and word length across languages.Frontiers in psychology · 2023Article
- Gaussian process models for geographic controls in phylogenetic trees.Open research Europe · 2023Article
- Exploring correlations in genetic and cultural variation across language families in northeast Asia.Science advances · 2021Article
- Language extinction triggers the loss of unique medicinal knowledge.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- The temperature of emotions.PloS one · 2021Article
- TuLeD (Tupían lexical database): introducing a database of a South American language family.Language resources and evaluation · 2021Article
- Phylogenetic Typology.Frontiers in psychology · 2021Article
- Article
- The ecological drivers of variation in global language diversity.Nature communications · 2019Article
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Authors and funding
1 author.
Funding
Abstract
Automatic phylogenetic inference plays an increasingly important role in computational historical linguistics. Most pertinent work is currently based on expert cognate judgments. This limits the scope of this approach to a small number of well-studied language families. We used machine learning techniques to compile data suitable for phylogenetic inference from the ASJP database, a collection of almost 7,000 phonetically transcribed word lists over 40 concepts, covering two thirds of the extant world-wide linguistic diversity. First, we estimated Pointwise Mutual Information scores between sound classes using weighted sequence alignment and general-purpose optimization. From this we computed a dissimilarity matrix over all ASJP word lists. This matrix is suitable for distance-based phylogenetic inference. Second, we applied cognate clustering to the ASJP data, using supervised training of an SVM classifier on expert cognacy judgments. Third, we defined two types of binary characters, based on automatically inferred cognate classes and on sound-class occurrences. Several tests are reported demonstrating the suitability of these characters for character-based phylogenetic inference.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.