Detailed Information on Publication Record
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
References:
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.