RuSkELL: Online Language Learning Tool for Russian Language

APRESJAN, Valentina, Vít BAISA, Olga BUIVOLOVA and Olga KULTEPINA. RuSkELL: Online Language Learning Tool for Russian Language. Online. In Tinatin Margalitadze, George Meladze. Proceedings of the XVII EURALEX International congress. Tbilisi: Ivane Javakhishvili Tbilisi State University, 2016, p. 292-299. ISBN 978-9941-13-542-2.

Basic information

• Original name: RuSkELL: Online Language Learning Tool for Russian Language
• Authors: APRESJAN, Valentina (643 Russian Federation), Vít BAISA (203 Czech Republic, guarantor, belonging to the institution), Olga BUIVOLOVA (643 Russian Federation) and Olga KULTEPINA (643 Russian Federation).
• Edition: Tbilisi, Proceedings of the XVII EURALEX International congress, p. 292-299, 8 pp. 2016.
• Publisher: Ivane Javakhishvili Tbilisi State University

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Georgia
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• RIV identification code: RIV/00216224:14330/16:00090690
• Organization unit: Faculty of Informatics
• ISBN: 978-9941-13-542-2
• UT WoS: 000392695200030
• Keywords in English: online language tool; Sketch Engine for Language Learning; sketches; collocations
• Tags: International impact, Reviewed

Abstract

RuSkELL ("Russian + Sketch Engine for Language Learning") is a new online resource intended for researchers and learners of Russian. It incorporates a specially pre-processed corpus and the interface which allows users to search for phrases in sentences, extract salient collocates and show similar words. The tool builds upon its English counterpart SkELL (Baisa & Suchomel 2014). The aim of the project is to adapt the existing SkELL tool to Russian, improve its performance and make it user-friendly to Russian users. The existing problems include errors in query output and insufficiently transparent interface. The project aspires to solve them by 1) modifying Sketch grammar rules to exclude irrelevant output and to add informative collocations unaccounted for in the existing Sketch grammar; 2) providing collocation groups with easy-to-understand labels in Russian. We describe the process of building the language data and problems we need to address to accommodate the tool for the specificities of the Russian language.

Links

• LM2015071, research and development project: Name: Jazyková výzkumná infrastruktura v České republice (Acronym: LINDAT-Clarin)

Investor: Ministry of Education, Youth and Sports of the CR

• MUNI/A/0945/2015, interní kód MU: Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace V.

Investor: Masaryk University, Category A

• 7F14047, research and development project: Name: Harvesting big text data for under-resourced languages (Acronym: HaBiT)

Investor: Ministry of Education, Youth and Sports of the CR