Why Russian Prefixes Aren’t Empty (doi:10.18710/OHPUPU)

View:

Part 1: Document Description
Part 2: Study Description
Part 5: Other Study-Related Materials
Entire Codebook

Why Russian Prefixes Aren’t Empty

doi:10.18710/OHPUPU

DataverseNO

2014-06-16

2

Janda, Laura A., 2014, "Why Russian Prefixes Aren’t Empty", https://doi.org/10.18710/OHPUPU, DataverseNO, V2

Why Russian Prefixes Aren’t Empty

doi:10.18710/OHPUPU

Janda, Laura A. (UiT The Arctic University of Norway)

UiT The Arctic University of Norway

2013

196907

DataverseNO

The Tromsø Repository of Language and Linguistics (TROLLing)

Janda, Laura A.

2014-06-11

https://doi.org/10.18710/OHPUPU

Arts and Humanities, Russian, aspect, prefixes, classifiers

Field: Morphology, Time-depth: synchronic, Topic: aspect, Topic: affixes

Publication abstract: We claim that Russian verbal prefixes always express meaning, even when they are used to form the perfective partners of aspectual pairs. The prefixes in verbs like написать/na-pisat’ ‘write’ and сварить/s-varit’ ‘cook’ have a semantic purpose, even though the corresponding imperfective verbs писать/pisat’ ‘write’ and варить/varit’ ‘cook’ have the same lexical meanings. We set forward a new hypothesis, namely that the Russian verbal prefixes function as verb classifiers, parallel to numeral classifiers. This book is designed to be theory-neutral and attractive to both linguists and non-linguists. Our studies make use of quantitative research on corpus data and statistical models (chi-square, logistic regression, etc.), which are presented in a common-sense way that assumes no special expertise. A user-friendly interactive webpage at http://emptyprefixes.uit.no/book.htm houses links to our database, plus additional data from the studies we cite. This book narrates recent breakthroughs in research on Russian aspect and demonstrates a range of methodologies designed to probe the relationship between the meaning and distribution of linguistic forms. These methodologies are used to investigate the “empty” prefixes, alternating constructions, prefix variation, and aspectual triplets. Though these phenomena have long been known to exist, their extent and behavior have not been previously explored in detail. We propose that the verbal prefixes select verbs according to broad semantic traits, categorizing them the way numeral classifiers categorize nouns. The purpose of the prefixes is to convert amorphous states and activities into discrete events and to group verbs according to the types of events they express. In other words, Russian prefixes are in effect a verb classifier system similar to those proposed for Mandarin Chinese, Hindi-Urdu, and a number of Australian languages, and this hypothesis facilitates cross-linguistic comparisons. The description of Russian prefixes as a verb classifier system furthermore has pedagogical value since we can redesign our curricula to teach students the system according to its meaningful groupings rather than simply requiring them to memorize hundreds of combinations of prefixes with simplex verbs. In short, the proposal to recognize Russian prefixes as verb classifiers supports the community of people interested in Russian grammar to be better linguists, better instructors, and better learners.

The datasets and other files in this study present the data and R code for the analyses of semantic profiles of prefixes in Chapter 3, constructional profiles for the verb gruzit’ ‘load’ and its prefixed perfectives in Chapter 4, verbs that use multiple prefixes to form multiple Natural Perfectives in Chapter 5, and aspectual triplets in Chapter 6.

Norway

Russia

corpus

Why Russian aspectual prefixes aren’t empty: prefixes as verb classifiers. Laura A. Janda, Anna Endresen, Julia Kuznetsova, Olga Lyashevskaya, Anastasia Makarova, Tore Nesset, Svetlana Sokolova. Bloomington, IN: Slavica Publishers. 2013. 227pp.

https://muse.jhu.edu/book/25198

Why Russian aspectual prefixes aren’t empty: prefixes as verb classifiers. Laura A. Janda, Anna Endresen, Julia Kuznetsova, Olga Lyashevskaya, Anastasia Makarova, Tore Nesset, Svetlana Sokolova. Bloomington, IN: Slavica Publishers. 2013. 227pp.

“Russian ‘purely aspectual’ prefixes: Not so ‘empty’ after all?”, Anna Endresen, Laura A. Janda, Julia Kuznetsova, Olga Lyashevskaya, Anastasia Makarova, Tore Nesset, Svetlana Sokolova. Scando-Slavica 58:2 (2012), 231-291.

10.1080/00806765.2012.740247

“Russian ‘purely aspectual’ prefixes: Not so ‘empty’ after all?”, Anna Endresen, Laura A. Janda, Julia Kuznetsova, Olga Lyashevskaya, Anastasia Makarova, Tore Nesset, Svetlana Sokolova. Scando-Slavica 58:2 (2012), 231-291.

Janda, Laura A. and Olga Lyashevskaya. "Semantic Profiles of Five Russian Prefixes: po-, s-, za-, na-, pro-." Journal of Slavic Linguistics, vol. 21 no. 2, 2013, p. 211-258. Project MUSE, doi:10.1353/jsl.2013.0012.

10.1353/jsl.2013.0012

Janda, Laura A. and Olga Lyashevskaya. "Semantic Profiles of Five Russian Prefixes: po-, s-, za-, na-, pro-." Journal of Slavic Linguistics, vol. 21 no. 2, 2013, p. 211-258. Project MUSE, doi:10.1353/jsl.2013.0012.

“Is Russian a verb classifier language?” Laura A. Janda. In Gianina Iordăchioaia, Isabelle Roy, Kaori Takamine (eds.) 2013. Categorization and Category Change, 59-86. Cambridge: Cambridge Scholars Publishing.

978-1-4438-5140-4

“Is Russian a verb classifier language?” Laura A. Janda. In Gianina Iordăchioaia, Isabelle Roy, Kaori Takamine (eds.) 2013. Categorization and Category Change, 59-86. Cambridge: Cambridge Scholars Publishing.

“Pristavki v svete kognitivnoj lingvistiki i tipologii: otklik na stat’ju A.A. Zaliznjak i I.L. Mikaèljan”, Kuznetsova and Laura A. Janda. 2013. Voprosy jazykoznanija 2013, Nr. 4, 87-96.

10037/5768

“Pristavki v svete kognitivnoj lingvistiki i tipologii: otklik na stat’ju A.A. Zaliznjak i I.L. Mikaèljan”, Kuznetsova and Laura A. Janda. 2013. Voprosy jazykoznanija 2013, Nr. 4, 87-96.

“Russkie pristavki kak sistema glagol’nyx klassifikatorov”. Laura A. Janda. Voprosy jazykoznanija 6 (2012), 3-47.

https://vja.ruslang.ru/en/archive/2012-6/3-47

“Russkie pristavki kak sistema glagol’nyx klassifikatorov”. Laura A. Janda. Voprosy jazykoznanija 6 (2012), 3-47.

“The Locative Alternation and the Russian ‘empty’ prefixes: A case study of the verb gruzit’ ‘load’”, co-authored with Svetlana Sokolova, Olga Lyashevskaya, and Laura A. Janda. In: D. Divjak & St. Th Gries (eds.). Frequency effects in language representation (Trends in Linguistics. Studies and Monographs. 244.2), 2012, 51-86. Berlin: Mouton de Gruyter.

10.1515/9783110274073

“The Locative Alternation and the Russian ‘empty’ prefixes: A case study of the verb gruzit’ ‘load’”, co-authored with Svetlana Sokolova, Olga Lyashevskaya, and Laura A. Janda. In: D. Divjak & St. Th Gries (eds.). Frequency effects in language representation (Trends in Linguistics. Studies and Monographs. 244.2), 2012, 51-86. Berlin: Mouton de Gruyter.

“Prefix variation as a challenge to Russian aspectual pairs: Are завязнуть and увязнуть ‘get stuck’ the same or different?”, Laura A. Janda and Olga Lyashevskaya. Russian Linguistics 35 (2011): 147-167.

10.1007/s11185-011-9076-9

“Prefix variation as a challenge to Russian aspectual pairs: Are завязнуть and увязнуть ‘get stuck’ the same or different?”, Laura A. Janda and Olga Lyashevskaya. Russian Linguistics 35 (2011): 147-167.

Label:

00_Readme_Janda_Russian-prefixes.txt

text/plain

Label:

Ch3Table1.exp.values.R

Text:

We used a simple formula involving the row and column totals in order to calculate the expected values for all the cells in Table 1. Here it is: expected value = (row sum x column sum) / total sum. The total sum for Table 1 is 382, and all of the row and column totals are listed in Table 1 in Chapter 3. This R script will compute and print out for you all of the expected values for Table 1 in Chapter 3.

text/plain; charset=US-ASCII

Label:

Ch3Table1Correct.R

Text:

This R script will give you the chi-squared value, the degrees of freedom, the p-value, and the effect size for Table 1 in Chapter 3. You can open and read the commentary in the R script to see how it is done.

text/plain; charset=US-ASCII

Label:

Ch4.R

Text:

The first thing the R script does is to give you a summary of the dataset. Scroll up to the top of the results and you will find a table that looks like this: CONSTRUCTION VERB REDUCED PARTICIPLE goal : 871 _zero:393 no :1353 no : 895 theme:1049 na :368 yes: 567 yes:1025 po :703 za :456 This table tells you how many items of each type are in each column of the dataset. Next comes the logistic regression analysis, which you find under the heading "Logistic Regression Model" in the R output. We used a procedure (following Baayen 2008 and Gries 2009) for discovering the minimal adequate model for our data. This means that we started with a hypothetical model in which all independent variables serve as both main effects and have interactions with each other, and then we progressively stripped away those that were not significant until we arrived at a model that represented only significant relationships. We will not walk you through this whole procedure, but just show you the optimal model. This model has all of the independent variables as main effects, plus an interaction between the VERB and PARTICIPLE variables. The formula for this model is represented this way in your R output (and in the R script): lrm(formula = CONSTRUCTION ~ VERB + REDUCED + PARTICIPLE + VERB:PARTICIPLE, data = loaddata, x = T, y = T, linear.predictors = T) This can be stated in prose thus: "CONSTRUCTION varies according to VERB, REDUCED, and PARTICIPLE as main effects, and an interaction between VERB and PARTICIPLE." Next comes a little table telling you the overall number of items for each value for the dependent variable CONSTRUCTION: goal has 871, and theme has 1049. Next come some figures that indicate how well the model performs.

text/plain; charset=US-ASCII

Label:

Ch4data.csv

Text:

The Russian verb gruzit’ ‘load’ is special for three reasons. First, this verb has two syntactic constructions it can appear in, second it has three Natural Perfectives, and third all three Natural Perfectives can also use both constructions. The two constructions that gruzit’ ‘load’ can appear in are called the “theme-object” construction and the “goal-object” construction. The names of the constructions come from the direct object that is marked with the accusative case. Let’s say that we have some boxes that we want to transport and a cart that we can use for this purpose. The boxes are the theme (the item that is put somewhere) and the cart is the goal (the place where the item is put). In the theme-object construction the theme is the direct object, as in грузить/gruzit’ ящики на телегу ‘load the boxes onto the cart’. The goal appears in a prepositional phrase in the theme-object construction, usually with the preposition на ‘onto’ or в ‘into’. In the goal-object construction the goal is the direct object, as in грузить/gruzit’ телегу ящиками ‘load the cart with boxes’. The theme in the goal-object construction often appears in the instrumental case as in our example: ящиками ‘with boxes’. Грузить/gruzit’ ‘load’ uses not just one, but three prefixes to form Natural Perfectives: na-, za-, and po-. Collectively we call these four verbs (the simplex and the three Natural Perfectives) “the ‘load’ verbs”. All three Natural Perfectives can appear in both the theme-object and the goal-object constructions. Chapter 4 explores whether the choice of prefix makes a difference in the distribution of the theme-object and goal-object constructions. The database: There are 1920 lines of data, each corresponding to one of the examples extracted from the Russian National Corpus. This file does not contain the actual examples, but rather just the relevant data on each variable for each example. If you open the .csv file, you will see that there are four columns, corresponding to our four variables: CONSTRUCTION: This is our dependent variable, and it has two values, theme, and goal. VERB: This is an independent variable, and it has four values, _zero (for the unprefixed verb грузить|gruzit’ ‘load’), na, za, and po (for the three prefixed variants). REDUCED: This is an independent variable, and it has two values, yes and no. This refers to whether the construction was reduced (yes) or full (no). PARTICIPLE: This is an independent variable, and it has two values, yes and no. This refers to whether the construction was passive (yes) or active (no).

text/plain; charset=US-ASCII

Label:

Ch5data.csv

Text:

In Chapter 5 we look at simplex verbs that have two or more Natural Perfectives formed by attaching prefixes. When a simplex verb uses more than one prefix to form Natural Perfectives, we call this “prefix variation”. For example if you look up грузить/ gruzit’ ‘load’ in the Ožegov & Švedova (2001) dictionary, you find that it uses three prefixes, za-, na-, and po-, to form perfective partner verbs. Is this just an exception? The answer to this question has long been buried in hundreds of entries scattered across the pages of dictionaries. Fortunately the Exploring Emptiness database (henceforth “EE database”) makes this information easy to access. The information cited on this webpage is extracted from the EE database. It is reasonable to suspect that some prefix combinations are fairly common, while others will be less common or non-existent. This is indeed the case. Table below lists all possible binary prefix combinations with the number of the simplex verbs that use it. Theoretically it is possible to make 120 binary combinations of sixteen prefixes (120 = 16x15/2). There are thirty-one binary combinations that are used by three or more simplex verbs, twenty-three that are rare and sixty-six that are not found at all.

text/csv

Label:

Ch6data.csv

Text:

Aspectual Triplets: An aspectual triplet is a set of three verbs, consisting of a simplex verb, a prefixed Natural Perfective, and a secondary imperfective derived via suffixation of the Natural Perfective. All three verbs have the same lexical meaning and the members of a triplet set differ from each other primarily in terms of aspect. An example is the triplet set множиться/množit’sja (simplex imperfective), умножиться/u-množit’sja (prefixed Natural Perfective), умножaться/u-množat’sja (secondary imperfective, with the suffix -a). All three verbs mean ‘multiply’. Triplets, like prefix variation explored in Chapter 5, present a challenge to the “pair” model of Russian aspect. The “pair” model assumes that Russian verbs are organized aspectually into pairs with a single imperfective verb and a single perfective verb. Prefix variation contradicts the “pair” model because many imperfective simplex verbs have two or more perfective partner verbs. Triplets show us that in addition, many perfective verbs have not one, but two imperfective partner verbs. We explore whether triplets are a marginal phenomenon or they are widespread. Methodology of this study: The "Exploring Emptiness” database lists 1,981 prefixed Natural Perfectives for Russian formed from 1,429 simplex imperfectives. This is the most comprehensive list of Natural Perfectives available, aggregated from two authoritative dictionaries and a list (see description). Using the three imperfectivizing suffixes -ыва/-ивa, -вa, and -а/-я, we formed hypothetical secondary imperfectives for all 1,981 Natural Perfectives. We then conducted searches for all of the hypothetical secondary imperfectives in both the Russian National Corpus (RNC) and the Google search engine. Because we know in advance that all of the Natural Perfectives in our database have corresponding simplex imperfectives, every instance where we find a secondary imperfective means that we have evidence of an aspectual triplet. Findings of this study: 753 of our 1,981 hypothetical secondary imperfectives are attested in the Russian National Corpus. Many of these are of low frequency: 230 are found in from two to nine examples, and 122 are found in only one example. In terms of percentages, 38% of the hypothetical secondary imperfectives exist in the Russian National Corpus. 12% of the possible secondary imperfectives are of low frequency, and 6% are attested only once. But 20% (401) of the theoretically possible secondary imperfectives are attested in ten or more examples. Google searches yielded even more aspectual triplets. 1,583 (80%) of the possible secondary perfectives were found. 171 (8%) are of low frequency (two to nine examples), and 92 (5%) turned up only once, but 1,320 (67%) are attested ten or more times. We found many times more aspectual triplets than previous scholars have reported. Indeed, it appears that the aspectual triplet phenomenon has been underestimated by an entire order of magnitude. Aspectual triplets are anything but marginal. Some of them are of low frequency, but there are many that are relatively common. Full list of verbs in this study A full list of triplets analyzed in Chapter 6 is shown here. The table is structured as follows. The first three columns show a simplex verb, a prefixed Natural Perfective, and a secondary imperfective derived via suffixation of the Natural Perfective. The fourth column presents the gloss for the triplet. The next three columns show raw frequencies of a simplex verb, a prefixed Natural Perfective, and a secondary imperfective in the modern subpart (>1950) of the RNC respectively. The last column shows the number of attestations for the secondary imperfective found in Google in year 2010.

text/plain; charset=US-ASCII