Machine-Aided Linguistic Discovery - An Introduction and Some Examples - Vladimir Pericliev

Machine-Aided Linguistic Discovery - An Introduction and Some Examples - Vladimir Pericliev

Inferring Simplest Laws/Patterns: MINTYP and the Problem of Describing a Typology

Machine-Aided Linguistic Discovery - An Introduction and Some Examples - Vladimir Pericliev

Vladimir Pericliev [+-]
Bulgarian Academy of Sciences
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Vladimir Pericliev is Senior Researcher at the Institute of Mathematics & Informatics, Bulgarian Academy of Sciences, with over 60 publications in general and computational linguistics, Artificial Intelligence and philosophy of science.

Description

Given a dataset, a common problem in scientific knowledge discovery is to summarize this set by a collection of rules (laws, patterns, etc.) such that the resultant description is the simplest, or most economic. In linguistics, the problem occurs e.g. in attempts to describe a linguistic typology in terms of the smallest set of implicational universals that allow all actually attested, and none of the unattested, language types. In this chapter, I introduce the MINTYP (Minimum TYPological description) program, which handles this problem, illustrating it on the typologies in Greenberg’s Appendix II (Greenberg 1966a) and Hawkins’ Expanded Sample (Hawkins 1983).

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Citation

Pericliev, Vladimir . Inferring Simplest Laws/Patterns: MINTYP and the Problem of Describing a Typology. Machine-Aided Linguistic Discovery - An Introduction and Some Examples. Equinox eBooks Publishing, United Kingdom. p. 231-251 Jan 2010. ISBN 9781845536602. https://www.equinoxpub.com/home/view-chapter/?id=25591. Date accessed: 25 Jun 2024 doi: 10.1558/equinox.25591. Jan 2010

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