D 2023

Rapid Ukrainian-English Dictionary Creation Using Post-Edited Corpus Data

BLAHUŠ, Marek, Michal CUKR, Ondřej HERMAN, Miloš JAKUBÍČEK, Vojtěch KOVÁŘ et. al.

Basic information

Original name

Rapid Ukrainian-English Dictionary Creation Using Post-Edited Corpus Data

Authors

BLAHUŠ, Marek (203 Czech Republic), Michal CUKR (203 Czech Republic), Ondřej HERMAN (203 Czech Republic, belonging to the institution), Miloš JAKUBÍČEK (203 Czech Republic, belonging to the institution), Vojtěch KOVÁŘ (203 Czech Republic, belonging to the institution), Jan KRAUS (203 Czech Republic), Marek MEDVEĎ (703 Slovakia, belonging to the institution), Vlasta OHLÍDALOVÁ (203 Czech Republic, belonging to the institution) and Vít SUCHOMEL (203 Czech Republic, belonging to the institution)

Edition

Brno, Czech Republic, Electronic lexicography in the 21st century (eLex 2023): Invisible Lexicography. Proceedings of the eLex 2023 conference, p. 613-637, 25 pp. 2023

Publisher

Lexical Computing CZ s.r.o.

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

RIV identification code

RIV/00216224:14330/23:00131469

Organization unit

Faculty of Informatics

ISSN

Keywords in English

Ukrainian; post-editing; dictionary; lexicography

Tags

International impact, Reviewed
Změněno: 9/4/2024 00:09, RNDr. Pavel Šmerk, Ph.D.

Abstract

V originále

This paper describes the development of a new corpus-based Ukrainian-English dictionary. The dictionary was built from scratch, we used no pre-existing dictionary data. A rapid dictionary development method was used which consists of generating dictionary parts directly from a large corpus, and of post-editing the automatically generated data by native speakers of Ukrainian (not professional lexicographers). The method builds on Baisa et al. (2019) which was improved and updated, and we used a diferent data management model. As the data source, a 3-billion-word Ukrainian web corpus from the TenTen series (Jakubíček et al., 2013) was used. The paper briefy describes the corpus, then we thoroughly explain the individual steps of the miQKiB+ ;2M2`iBQMěTQbi@2/BiBM; workfow, including the volume of the manual work needed for the particular phases in terms of person-days. We also present details about the newly created dictionary and discuss directions for its further development.