By the same authors

Learning implicational models of universal grammar parameters

Research output: Chapter in Book/Report/Conference proceedingChapter

Standard

Learning implicational models of universal grammar parameters. / Kazakov, Dimitar Lubomirov; Cordoni, Guido; Algahtani, Eyad; Ceolin, Andrea; Irimia, Monica-Alexandrina; Kim, Shin-Sook; Michelioudakis, Dimitris; Radkevich, Nina; Guardiano, Cristina ; Longobardi, Giuseppe.

The Evolution of Language: Proceedings of the 12th International Conference (EVOLANGXII). ed. / C. Cuskley; M. Flaherty; H. Little; L. McCrohon; A. Ravignani; T. Verhoef. Torun, Poland : Online at http://evolang.org/torun/proceedings/papertemplate.html?p=176, 2018.

Research output: Chapter in Book/Report/Conference proceedingChapter

Harvard

Kazakov, DL, Cordoni, G, Algahtani, E, Ceolin, A, Irimia, M-A, Kim, S-S, Michelioudakis, D, Radkevich, N, Guardiano, C & Longobardi, G 2018, Learning implicational models of universal grammar parameters. in C Cuskley, M Flaherty, H Little, L McCrohon, A Ravignani & T Verhoef (eds), The Evolution of Language: Proceedings of the 12th International Conference (EVOLANGXII). Online at http://evolang.org/torun/proceedings/papertemplate.html?p=176, Torun, Poland.

APA

Kazakov, D. L., Cordoni, G., Algahtani, E., Ceolin, A., Irimia, M-A., Kim, S-S., Michelioudakis, D., Radkevich, N., Guardiano, C., & Longobardi, G. (2018). Learning implicational models of universal grammar parameters. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 12th International Conference (EVOLANGXII) Online at http://evolang.org/torun/proceedings/papertemplate.html?p=176.

Vancouver

Kazakov DL, Cordoni G, Algahtani E, Ceolin A, Irimia M-A, Kim S-S et al. Learning implicational models of universal grammar parameters. In Cuskley C, Flaherty M, Little H, McCrohon L, Ravignani A, Verhoef T, editors, The Evolution of Language: Proceedings of the 12th International Conference (EVOLANGXII). Torun, Poland: Online at http://evolang.org/torun/proceedings/papertemplate.html?p=176. 2018

Author

Kazakov, Dimitar Lubomirov ; Cordoni, Guido ; Algahtani, Eyad ; Ceolin, Andrea ; Irimia, Monica-Alexandrina ; Kim, Shin-Sook ; Michelioudakis, Dimitris ; Radkevich, Nina ; Guardiano, Cristina ; Longobardi, Giuseppe. / Learning implicational models of universal grammar parameters. The Evolution of Language: Proceedings of the 12th International Conference (EVOLANGXII). editor / C. Cuskley ; M. Flaherty ; H. Little ; L. McCrohon ; A. Ravignani ; T. Verhoef. Torun, Poland : Online at http://evolang.org/torun/proceedings/papertemplate.html?p=176, 2018.

Bibtex - Download

@inbook{56a1c56b89b84ef2a88df3dd3a4f744f,
title = "Learning implicational models of universal grammar parameters",
abstract = "The use of parameters in the description of natural language syntax has to balance between the need to discriminate among (sometimes subtly different) languages, which can be seen as a cross-linguistic version of Chomsky's descriptive adequacy (Chomsky, 1964), and the complexity of the acquisition task that a large number of parameters would imply, which is a problem for explanatory adequacy. Here we first present a novel approach in which machine learning is used to detect hidden dependencies in a table of parameters. The result is a dependency graph in which some of the parameters can be fully predicted from others. These findings can be then subjected to linguistic analysis, which may either refute them by providing typological counter-examples of languages not included in the original dataset, dismiss them on theoretical grounds, or uphold them as tentative empirical laws worth of further study. Machine learning is also used to explore the full sets of parameters that are sufficient to distinguish one historically established language family from others. These results provide a new type of empirical evidence about the historical adequacy of parameter theories.",
author = "Kazakov, {Dimitar Lubomirov} and Guido Cordoni and Eyad Algahtani and Andrea Ceolin and Monica-Alexandrina Irimia and Shin-Sook Kim and Dimitris Michelioudakis and Nina Radkevich and Cristina Guardiano and Giuseppe Longobardi",
year = "2018",
month = jan,
day = "29",
language = "English",
editor = "C. Cuskley and M. Flaherty and H. Little and L. McCrohon and A. Ravignani and T. Verhoef",
booktitle = "The Evolution of Language: Proceedings of the 12th International Conference (EVOLANGXII)",
publisher = "Online at http://evolang.org/torun/proceedings/papertemplate.html?p=176",

}

RIS (suitable for import to EndNote) - Download

TY - CHAP

T1 - Learning implicational models of universal grammar parameters

AU - Kazakov, Dimitar Lubomirov

AU - Cordoni, Guido

AU - Algahtani, Eyad

AU - Ceolin, Andrea

AU - Irimia, Monica-Alexandrina

AU - Kim, Shin-Sook

AU - Michelioudakis, Dimitris

AU - Radkevich, Nina

AU - Guardiano, Cristina

AU - Longobardi, Giuseppe

PY - 2018/1/29

Y1 - 2018/1/29

N2 - The use of parameters in the description of natural language syntax has to balance between the need to discriminate among (sometimes subtly different) languages, which can be seen as a cross-linguistic version of Chomsky's descriptive adequacy (Chomsky, 1964), and the complexity of the acquisition task that a large number of parameters would imply, which is a problem for explanatory adequacy. Here we first present a novel approach in which machine learning is used to detect hidden dependencies in a table of parameters. The result is a dependency graph in which some of the parameters can be fully predicted from others. These findings can be then subjected to linguistic analysis, which may either refute them by providing typological counter-examples of languages not included in the original dataset, dismiss them on theoretical grounds, or uphold them as tentative empirical laws worth of further study. Machine learning is also used to explore the full sets of parameters that are sufficient to distinguish one historically established language family from others. These results provide a new type of empirical evidence about the historical adequacy of parameter theories.

AB - The use of parameters in the description of natural language syntax has to balance between the need to discriminate among (sometimes subtly different) languages, which can be seen as a cross-linguistic version of Chomsky's descriptive adequacy (Chomsky, 1964), and the complexity of the acquisition task that a large number of parameters would imply, which is a problem for explanatory adequacy. Here we first present a novel approach in which machine learning is used to detect hidden dependencies in a table of parameters. The result is a dependency graph in which some of the parameters can be fully predicted from others. These findings can be then subjected to linguistic analysis, which may either refute them by providing typological counter-examples of languages not included in the original dataset, dismiss them on theoretical grounds, or uphold them as tentative empirical laws worth of further study. Machine learning is also used to explore the full sets of parameters that are sufficient to distinguish one historically established language family from others. These results provide a new type of empirical evidence about the historical adequacy of parameter theories.

M3 - Chapter

BT - The Evolution of Language: Proceedings of the 12th International Conference (EVOLANGXII)

A2 - Cuskley, C.

A2 - Flaherty, M.

A2 - Little, H.

A2 - McCrohon, L.

A2 - Ravignani, A.

A2 - Verhoef, T.

PB - Online at http://evolang.org/torun/proceedings/papertemplate.html?p=176

CY - Torun, Poland

ER -