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Title of the dataset: "Data used by SMARTool for English-speaking learners of Russian"

DOI: https://doi.org/10.18710/QNAPNE

Contact information:

• Name: Janda, Laura A.
• Institution: UiT The Arctic University of Norway
• Email: [email protected]
• ORCID: https://orcid.org/0000-0001-5047-1909

Authors:

• Bast, Radovan (UiT The Arctic University of Norway) - ORCID: 0000-0002-7658-1847
• Janda, Laura A. (UiT The Arctic University of Norway) - ORCID: 0000-0001-5047-1909
• Kopotev, Mikhail (University of Helsinki): ORCID: 0000-0002-4998-2952
• Lyashevskaya, Olga (National Research University Higher School of Economics) - ORCID: 0000-0001-8374-423X
• McDonald, James D.
• Nesset, Tore (UiT The Arctic University of Norway): - ORCID: 0000-0003-1308-3506
• Rakhilina, Ekaterina (National Research University Higher School of Economics) - ORCID: 0000-0002-7126-0905
• Sokolova, Svetlana (UiT The Arctic University of Norway) - ORCID: 0000-0002-1353-0452
• Sudarikova, Evgeniia (Higher School of Economics in Moscow)
• Tyers, Francis M. (Indiana University) - ORCID: 0000-0001-6108-2220
• Zhukova, Valentina (UiT The Arctic University of Norway) - ORCID: 0000-0002-8058-6480
• Elizaveta Kibisova (Higher School of Economics in Moscow) - ORCID: 0000-0001-8922-9239

This dataset is used by: SMARTool for English-speaking learners of Russian (website: https://smartool.github.io/smartool-rus-eng/, source code: https://github.com/smartool/smartool-rus-eng)

The SMARTool is a free web resource for L2 learners of Russian that implements findings of a learning simulation experiment and corpus research to optimize the acquisition of Russian vocabulary and morphology. Corpus data (Janda & Tyers 2021) reveals that for any given Russian lexeme, only a few (usually 1-3) wordforms account for nearly all attestations, while remaining wordforms are rare or unattested. Our computational learning experiment shows that learning of Russian inflection is best when training is restricted to high frequency wordforms. The SMARTool builds on these findings for a basic vocabulary of 3000 nouns, adjectives, and verbs culled from major textbooks and other sources to represent levels A1, A2, B1, and B2 (CEFR scale). We have identified both a) the three most frequent wordforms for each lexeme (reducing the target number of wordforms to learn from 150,000 to 9,000) and b) the most typical grammatical constructions and collocations that motivate each wordform. We have also created c) corpus-based example sentences instantiating typical use.

Audio for all sentences (in both female and male voices) and English translations are available at the click of a button. The SMARTool has filters that facilitate searching by: a) CEFR Level, b) Topic (such as время/time, еда/food, здоровье/health etc.), c) Analysis (such as Ins.Sing, facilitating grammatical exercises), and d) Dictionary form.

We include files with the example sentences that show the typical use of each wordform cited in the SMARTool, stratified according to CEFR proficiency levels A1, A2, B1, and B2. In addition, there are files that link abbreviations for linguistic categories to their full names (for example, Acc.Plur is mapped to Accusative Plural and Acc is mapped to Accusative while Plur is mapped to Plural), as well as files that link the Russian names of Topics to their English equivalents (for example время is mapped to time).

Lexemes were selected from a merged list of vocabulary from five Russian language textbooks (Hertz et al. 2001, Chernyshov 2004, Robin, Shatalina, and Evans-Romaine 2012, deBenedette et al. 2013, Bondar’ and Lutin 2013) plus the Лексический минимум по русскому языку как иностранному (Andriushchina et al. 2014–2015) for the corresponding levels. A panel of experienced teachers of Russian from three universities in Russia and Europe collaborated on the selection of lexemes (see SMARTool Authors):

• Andriushchina, N. P., G. A. Bitekhtina, L. P. Klobukova, L. N. Noreiko, I. V. Odintsova, eds. 2014–2015. Leksicheskii mimimum po russkomu iazyku kak inostrannomu. Levels A1–B2. St. Petersburg: Zlatoust.
• Bondar’, Nataliia I., and Sergei A. Lutin. 2013. Kak sprosit’? Kak skazat’? 2nd ed. Moscow: Russkii iazyk.
• Chernyshov, Stanislav. 2004. Poexali! St. Petersburg: Zlatoust.
• deBenedette, Lynne, William J. Comer, Alla Smyslova, and Jonathan Perkins. 2013. Между нами (Between You and Me): An Interactive Introduction to Russian. Accessed April 1, 2018. http://www.mezhdunami.org/.
• Hertz, Birgitte, Hanne Leervad, Henrik Lærkes, Henrik Møller, and Peter Schousboe. 2001. Свидание в Петербурге. Møde i Petersborg. Copenhagen: Gyldendal.
• Robin, Richard, Galina Shatalina, and Karen Evans-Romaine. 2012. Golosa. Vols 1–2. 5th ed. New York: Pearson.

• README.md: Contains metadata about the dataset
• SMARTool_data_A1.csv: Example sentences for the A1 level
• SMARTool_data_A2.csv: Example sentences for the A2 level
• SMARTool_data_Abbreviations.csv: Mapping abbreviations to their full names
• SMARTool_data_B1.csv: Example sentences for the B1 level
• SMARTool_data_B2.csv: Example sentences for the B2 level
• SMARTool_data_Topics.csv: Mapping topics to their translations

Each of the files can be used together, or separately.

File SMARTool_data_Abbreviations.csv lists and explains the linguistic abbreviations that are used in the following files: SMARTool_data_A1.csv, SMARTool_data_A2.csv, SMARTool_data_B1.csv, SMARTool_data_B2.csv.

File SMARTool_data_Topics.csv lists and explains the topics that are used in column "Topic(s)" in the following files: SMARTool_data_A1.csv, SMARTool_data_A2.csv, SMARTool_data_B1.csv, SMARTool_data_B2.csv.

• Target language lemma: The lemma of the selected Russian lexemes.

• ID: Unique identifier for each line in the dataset.

• User language gloss: English gloss for each target language lemma.

• English gloss: This column is empty.

• POS: Part of speech for each target language lemma.

• Level: CEFR (The Common European Framework of Reference for Languages) level for each target language lemma. Values: A1, A2, B1, B2.

• Word source: This column indicates vocabularies that contain the lemma. Values: Golosa (stands for the textbook “Golosa”[Voices]), MiP (stands for the textbook “Свидание в Петербурге. Møde i Petersborg”[Meeting in Saint Petersburg]), LM (stands for Leksicheskii mimimum po russkomu iazyku kak inostrannomu [Lexical minimum for Russian as a foreign language]), МН (stands for the textbook “Между нами” [Between You and Me]), Поехали (stands for the textbook “Поехали” [Let’s go]), Кс (stands for the textbook “Kak sprosit’? Kak skazat’?” [How to ask? How to say?])

• Ex. ID: The ordinal number of the example sentence for each target language lemma. Values: 1, 2, 3.

• Form: The exact word form of the corresponding target language lemma.

• Analysis: Morphological analysis for each word form.

• Target language example sentence: The example sentence illustrating the use of corresponding word form in Russian for each target language lemma. In some rows, this column is intentionally left empty.

• User language translation: The English translation of the corresponding example sentence.

• English translation: This column is empty.

• Reference: Reference to a particular source for the corresponding example sentence.

• Topic(s): Topic or topics to which each target language lemma in the dataset belongs.

• Lesson: The ordinal number of the lesson in “Min russiske reise” textbook to which each target language lemma in the dataset belongs.

• Unnamed column: This column can contain the annotators’ comments needed for the internal work on the project. Most of the rows of this column are empty.

• Abbreviation: The abbreviation that is used in the dataset.
• Spellout: The linguistic term that the abbreviation refers to.
• English gloss: This column is empty.
• English translation: This column is empty.

• Topic: The topic for lexemes in the dataset in the target language.
• User language translation: The English translation of the topic.

The dataset is provided under the CC-BY 4.0 license

1. Janda, Laura A. and Francis M. Tyers. 2021. Less is more: why all paradigms are defective, and why that is a good thing. Corpus Linguistics and Linguistic Theory 14(2), 33pp. <doi.org/10.1515/cllt-2018-0031>.

2. Janda, Laura A. 2019. Businessmen and Ballerinas Take Different Forms: A Strategic Resource for Acquiring Russian Vocabulary and Morphology. Russian Language Journal 69, 175--193.

3. Janda, Laura A. 2021. Getting more out of limited input: strategic mastery of foreign languages. In: Вызовы и тренды мировой лингвистики: Казанский международный лингвистический саммит (Казань, 16–20 ноября 2020 г.): тр. и матер.: в 2 т. / под общ. ред. Р.Р. Замалетдинова, Ф.Х. Тарасовой, Е.А. Горобец. -- Казань: Издательство Казанского университета. -- Т. 1. -- 450 с. [papers of the Kazan International Linguistic Summit "Challenges and Trends in World Linguistics", vol. 1], https://cloud.mail.ru/public/xeKT/vGMAQBUMa, pp. 280--284.