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1. Title Title of document Bibliography - Machine-Aided Linguistic Discovery
2. Creator Author's name, affiliation, country Vladimir Pericliev; Bulgarian Academy of Sciences;
3. Subject Discipline(s) Linguistics
4. Subject Keyword(s) Saussurian; Greenbergian language; genetic language classifications; structural semantics; phonology; typology; historical linguistics; computational linguistics
5. Subject Subject classification Computational linguistics (CFX)
6. Description Abstract Solving linguistic problems frequently reduces to carrying out tasks that are computationally complex and therefore requires automation. This book is an introduction to machine-aided linguistic discovery, a novel research area, and argues for the fruitfulness of the computational approach by presenting a basic conceptual apparatus and several intelligent discovery programs.

One of the programs models the fundamental Saussurian notion of “system” and thus, almost a century after the introduction of this concept and structuralism in general, linguists are for the first time capable of handling adequately this recurring computationally complex task. Another program models the problem of searching for Greenbergian language universals and is capable of stating its discoveries in an intelligible form, a comprehensive English language text. It is the first computer program to generate a whole scientific article. A third program detects potential inconsistencies in genetic language classifications. These, and the other programs described in this book, are applied with noteworthy results to substantial problems from diverse linguistic disciplines such as structural semantics, phonology, typology and historical linguistics.

Machine-Aided Linguistic Discovery will be of interest to linguists and to scholars working in the areas of computational linguistics, Artificial Intelligence and the philosophy of science.

7. Publisher Organizing agency, location Equinox Publishing Ltd
8. Contributor Sponsor(s)
9. Date (YYYY-MM-DD) 01-Jan-2010
10. Type Status & genre Peer-reviewed Article
11. Type Type
12. Format File format PDF
13. Identifier Uniform Resource Identifier
14. Identifier Digital Object Identifier 10.1558/equinox.25602
15. Source Journal/conference title; vol., no. (year) Equinox eBooks Publishing; Machine-Aided Linguistic Discovery
16. Language English=en en
18. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
19. Rights Copyright and permissions Copyright 2014 Equinox Publishing Ltd