Constructing a Lexical Resource of Russian Derivational Morphology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10457036" target="_blank" >RIV/00216208:11320/22:10457036 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.298.pdf" target="_blank" >http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.298.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Constructing a Lexical Resource of Russian Derivational Morphology

Popis výsledku v původním jazyce

Words of any language are to some extent related thought the ways they are formed. For instance, the verb exempl-ify and the noun example-s are both based on the word example, but the verb is derived from it, while the noun is inflected. In Natural Language Processing of Russian, the inflection is satisfactorily processed; however, there are only a few machine-tractable resources that capture derivations even though Russian has both of these morphological processes very rich. Therefore, we devote this paper to improving one of the methods of constructing such resources and to the application of the method to a Russian lexicon, which results in the creation of the largest lexical resource of Russian derivational relations. The resulting database dubbed DeriNet.RU includes more than 300 thousand lexemes connected with more than 164 thousand binary derivational relations. To create such data, we combined the existing machine-learning methods that we improved to manage this goal. The whole approach is eva

Název v anglickém jazyce

Constructing a Lexical Resource of Russian Derivational Morphology

Popis výsledku anglicky

Words of any language are to some extent related thought the ways they are formed. For instance, the verb exempl-ify and the noun example-s are both based on the word example, but the verb is derived from it, while the noun is inflected. In Natural Language Processing of Russian, the inflection is satisfactorily processed; however, there are only a few machine-tractable resources that capture derivations even though Russian has both of these morphological processes very rich. Therefore, we devote this paper to improving one of the methods of constructing such resources and to the application of the method to a Russian lexicon, which results in the creation of the largest lexical resource of Russian derivational relations. The resulting database dubbed DeriNet.RU includes more than 300 thousand lexemes connected with more than 164 thousand binary derivational relations. To create such data, we combined the existing machine-learning methods that we improved to manage this goal. The whole approach is eva

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022)

ISBN

979-10-95546-72-6

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

2788-2797

Název nakladatele

European Language Resources Association

Místo vydání

Marseille, France

Místo konání akce

Marseille, France

Datum konání akce

20. 6. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—