1006731.pdf
This volume seeks to infer large phylogenetic networks from phonetically encoded lexical data and contribute in this way to the historical study of language varieties. The technical step that enables progress in this case is the use of causal inference algorithms. Sample sets of words from language...
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Language Science Press
2020
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oapen-20.500.12657-234192024-03-22T19:22:54Z Information-theoretic causal inference of lexical flow Dellert, Johannes Linguistics thema EDItEUR::C Language and Linguistics::CF Linguistics This volume seeks to infer large phylogenetic networks from phonetically encoded lexical data and contribute in this way to the historical study of language varieties. The technical step that enables progress in this case is the use of causal inference algorithms. Sample sets of words from language varieties are preprocessed into automatically inferred cognate sets, and then modeled as information-theoretic variables based on an intuitive measure of cognate overlap. Causal inference is then applied to these variables in order to determine the existence and direction of influence among the varieties. The directed arcs in the resulting graph structures can be interpreted as reflecting the existence and directionality of lexical flow, a unified model which subsumes inheritance and borrowing as the two main ways of transmission that shape the basic lexicon of languages. 2020-03-10 03:00:38 2020-04-01T09:14:37Z 2020-04-01T09:14:37Z 2019-09-11 book 1006731 9783961101436 http://library.oapen.org/handle/20.500.12657/23419 eng Language Variation application/pdf n/a 1006731.pdf Language Science Press 10.5281/zenodo.3247415 104951 10.5281/zenodo.3247415 0bad921f-3055-43b9-a9f1-ea5b2d949173 b818ba9d-2dd9-4fd7-a364-7f305aef7ee9 9783961101436 Berlin 104951 Language Science Press 2018 - 2020 Knowledge Unlatched open access |
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This volume seeks to infer large phylogenetic networks from phonetically encoded lexical data and contribute in this way to the historical study of language varieties. The technical step that enables progress in this case is the use of causal inference algorithms. Sample sets of words from language varieties are preprocessed into automatically inferred cognate sets, and then modeled as information-theoretic variables based on an intuitive measure of cognate overlap. Causal inference is then applied to these variables in order to determine the existence and direction of influence among the varieties. The directed arcs in the resulting graph structures can be interpreted as reflecting the existence and directionality of lexical flow, a unified model which subsumes inheritance and borrowing as the two main ways of transmission that shape the basic lexicon of languages. |
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2020 |
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