Predicting word order universals

• Autorzy: Merlo P.

Identyfikatory

DOI

10.15398/jlm.v3i2.112

• Języki publikacji: EN

Abstrakty

EN

This paper shows a computational learning paradigm to compare and test theories about language universals. Its main contribution lies in the illustration of the encoding and comparison of theories about typological universals to measure the generalisation ability of these theories. In so doing, this method uncovers hidden dependencies between theoretical dimensions and primitives that were considered independent and independently motivated.

Słowa kluczowe

EN

frequency; word order universals; Greenberg’s Universal 20; computational modelling; probabilistic models; Naive Bayes

• Wydawca: Instytut Podstaw Informatyki PAN
• Czasopismo: Journal of Language Modelling
• Rocznik: 2015
• Tom: Vol. 3, No. 2
• Strony: 317--344
• Opis fizyczny: Bibliogr. 16 poz., rys., tab.

Twórcy

autor

Merlo P.

• University of Geneva, Department of Linguistics, Geneva, Switzerland

Bibliografia

• [1] Steven Abney (2011), Data-intensive experimental linguistics, Linguistic Issues in Language Technology (LILT), 6 (2): 1-27.
• [2] Guglielmo Cinque (2005), Deriving Greenberg’s Universal 20 and its exceptions, Linguistic Inquiry, 36 (3): 315-332.
• [3] Jennifer Culbertson and Paul Smolensky (2012), A Bayesian model of biases in artificial language learning: The case of a word-order universal, Cognitive Science, 36 (8): 1468-1498.
• [4] Jennifer Culbertson, Paul Smolensky, and Geraldine Legendre (2012), Learning biases predict a word order universal, Cognition, 122 (3): 306-329.
• [5] Michael Cysouw (2010a), Dealing with diversity: towards an explanation of NP word order frequencies, Linguistic Typology, 14 (2): 253-287.
• [6] Michael Cysouw (2010b), On the probability distribution of typological frequencies, in Proceedings of the 10th and 11th Biennial conference on the mathematics of language, MOL’07/09, pp. 29-35, Springer-Verlag, Berlin, Heidelberg.
• [7] Matthew Dryer (2006), The order demonstrative, numeral, adjective and noun: an alternative to Cinque, http://attach.matita.net/caterinamauri/sitovecchio/1898313034_cinqueH09.pdf. Accessed on 19th August, 2015.
• [8] Matthew Dryer (2005), “Genealogical language list”, in Haspelmath, Martin and Dryer, Matthew and Gil, David and Comrie, Bernard (eds.), The World Atlas of Language Structures, Oxford University Press, Oxford, 584-644.
• [9] Matthew S. Dryer (1992), The Greenbergian word order correlations, Language, 68: 81-138, doi:10.2307/416370.
• [10] Michael Dunn, Simon J. Greenhill, Stephen C. Levinson, and Russell D. Gray (2011), Evolved structure of language shows lineage-specific trends in word-order universals, Nature, 473: 79-82.
• [11] Richard Futrell, Kyle Mahowald, and Edward Gibson Large-scale evidence of dependency length minimization in 37 languages, Proceedings of the National Academy of Sciences of the United States of America, 112 (33): 10336-10341, doi: 10.1073/pnas.1502134112.
• [12] Joseph H. Greenberg (1966), Language universals, Mouton, The Hague, Paris.
• [13] Richard Kayne (1994), The antisymmetry of syntax, MIT Press, Cambridge, MA.
• [14] Stuart Russel and Peter Norvig (1995), Artificial intelligence: a modern approach, Prentice Hall Series in Artificial Intelligence, Prentice Hall, Upper Saddle River, NJ.
• [15] Mark Steedman (2011), Greenberg’s 20th: the view from the long tail, unpublished manuscript, University of Edinburgh.
• [16] G. I. Webb, J. Boughton, and Z. Wang (2005), Not so Naive Bayes: aggregating one-dependence estimators, Machine Learning, 58 (1): 5-24.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).