=Paper=
{{Paper
|id=Vol-3396/paper5
|storemode=property
|title=Register Distribution of English Detached Nonfinite/ Nonverbal with Explicit Subject Constructions: a Corpus-Based and Machine-Learning Approach
|pdfUrl=https://ceur-ws.org/Vol-3396/paper5.pdf
|volume=Vol-3396
|authors=Viktoriia Zhukovska,Olexandr Mosiiuk,Solomija Buk
|dblpUrl=https://dblp.org/rec/conf/colins/ZhukovskaMB23
}}
==Register Distribution of English Detached Nonfinite/ Nonverbal with Explicit Subject Constructions: a Corpus-Based and Machine-Learning Approach==
https://ceur-ws.org/Vol-3396/paper5.pdf
Register Distribution of English Detached Nonfinite/ Nonverbal with Explicit
Subject Constructions: a Corpus-Based and Machine-Learning Approach
Viktoriia Zhukovska1, Oleksandr Mosiiuk1 and Solomija Buk2
1
Zhytomyr Ivan Franko State University, Velyka Berdychivska str. 40, Zhytomyr, 10008, Ukraine
2
Ivan Franko National University of Lviv, Universytetska str. 1, Lviv, 79000, Ukraine
Abstract
This article presents the findings of a quantitative corpus-based analysis of the register
distribution of detached nonfinite/ nonverbal with explicit subject constructions in present-day
English. Despite substantial research on the linguistic diversity of the syntactic patterns under
study, no quantitative corpus and machine-learning analysis of the distribution of all their types
in modern English registers has been presented. Thus, the statistical platform R was employed
to accomplish two goals: 1) to undertake a quantitative corpus-based analysis of register
distribution of the analyzed clauses in the BNC corpus and 2) to assess the possibility of
occurrence of the clauses under research in the registers of present-day English on the basis of
a machine-learning model. The findings of this study provide compelling evidence for the
applicability of an integrated quantitative corpus linguistic and machine learning analysis for
investigating the linguistic behavior of complex clause-level constructions, such as English
detached nonfinite/ nonverbal with explicit subject constructions. The obtained results refute
the prevalent view in contemporary English grammars that detached nonfinite/ nonverbal with
explicit subject constructions have a limited scope of use and demonstrate that the analyzed
syntactic patterns expand their register distribution, penetrating both the written and spoken
registers of contemporary formal and informal English discourse.
Keywords 1
Cognitive-quantitative construction grammar, clause-level construction, statistical platform,
integrated approach
1. Introduction
Since the 1990s, the field of linguistics has undergone a significant methodological shift. It gradually
reopened the empirical methods of corpus and experimental linguistics, transforming itself from a
primarily rationalist discipline. This shift in methodology has changed the way linguists approach and
analyze language, allowing for more accuracy and precision in their analysis. Consequently, the
application of quantitative methods appears to have altered “the ecology of methodology in linguistics
research” [1, p. 4], and text corpora have become “the alpha and omega of linguistics” [2, p. 8]. The
use of corpus data has become indispensable in many areas of language study [3, p. 114], including
those traditionally favoring a rationalist approach, such as syntax.
This research focuses on detached nonfinite/ nonverbal clauses with an explicit subject in English.
The following contexts from the British National Corpus (BNC) [4] illustrate the clauses under
consideration (1–5):
(1) Katherine sat silently for a long moment, [ØAUG[NPher eyes] [XPgrowing perceptibly wider]],
[[NPthe color] [XPdraining from her cheeks]] (BNC; FNT);
(2) Nathan was standing defiantly, [ØAUG[NPhands] [XPin pockets]], near the window (BNC; AD9);
COLINS-2023: 7th International Conference on Computational Linguistics and Intelligent Systems, April 20–21, 2023, Kharkiv, Ukraine
EMAIL: victoriazhukovska@gmail.com (V. Zhukovska); mosxandrwork@gmail.com (O. Mosiiuk); solomija@gmail.com (S. Buk)
ORCID: 0000-0002-4622-4435 (V. Zhukovska); 0000-0003-3530-1359 (O. Mosiiuk); 0000-0001-8026-3289 (S. Buk)
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
(3) The Coroner was in his early forties, a gaunt, greying man, [[AUGwith] [NPthick spectacles]
[XPperched at the very end of his nose]] (BNC; CES);
(4) Its recommendation for a global oil strategy has so far received no recognition, [[AUGdespite]
[NPoil being] [XPthe lifeblood of industrial (modern) society]] (BNC; B1W);
(5) She had a head start, of course, [[AUGwhat with] [NPher mother] [XPbeing immaculate too]]
(BNC; HGJ).
The purpose of this study is to investigate the register distribution of detached nonaugmented (ØAUG)
and augmented (AUG) (with, without, despite, what with) nonfinite and nonverbal clauses with explicit
subject in present-day English. With this in mind, two objectives are attained: 1) to undertake a
quantitative corpus-based analysis of register distribution of the analyzed clauses in the BNC and 2) to
assess the possibility of occurrence of the clauses under research in the registers of present-day English
on the basis of a machine-learning model. Integrating quantitative corpus linguistics and machine
learning approaches, this study contributes to a better understanding of the distributional properties of
detached nonfinite/ nonverbal clauses with explicit subject in contemporary English.
2. Related Works
Researchers from various linguistic trends and schools have repeatedly drawn attention to English
nonfinite/ nonverbal clauses with explicit subject: descriptive grammar (Quirk et al. (1985), Timofeeva
(2011), Kortmann (2013)); generative grammar (Riemsdijk (1978), McCawley (1983), Beukema,
Felser, Britain (2007)); corpus linguistics (Duffley, Dion-Girardeau (2015), Fonteyn, van de Pol
(2015)); functional systemic grammar (He, Yang (2015), Khamesian (2016)); construction grammar
(Riehemann, Bender (1999), Bouzada-Jabois, Pérez-Guerra (2016)). Despite the number of studies
conducted, the linguistic diversity of the investigated nonfinite/ nonverbal clauses raises a number of
questions that have not yet been conclusively answered by previous research. Specifically, the
distribution of the clauses in the registers of present-day English needs to be further examined using the
most recent developments of the cognitive-quantitative linguistic framework.
English detached nonfinite/ nonverbal clauses with explicit subject are conventionally regarded as
rare, archaic Latinisms mainly used in official discourse [5, p. 250; 6, p. 95]. Therefore, the ‘formal vs.
informal’ nature of a text or communication situation serves as the main criterion for differentiating the
spheres of their use. According to R. Quirk and his co-authors, nonfinite/ nonverbal clauses with an
overt subject are rather formal and uncommon in contemporary English [7, p. 1120]. This assertion is
supported by the diachronic studies, which show that in Old English such clauses were predominantly
Latin borrowings and were most likely used by educated people in official texts [8]. During the Middle
English and New English periods (at least until 1660), these syntactic patterns were characteristic of
classical, bookish, and scientific style and were widely used in religious and legal texts [9]. Modern
usage recognizes such clauses as stylistically marked syntactic structures used more frequently in
written, especially in formal and narrative, texts [10, p. 122], than in spoken ones, except for a few
cliché expressions such as present company excepted, all told, weather/ time permitting, God willing
[7, p. 1120]. Otherwise, subordinate clauses are almost always used in speech where nonfinite/
nonverbal clauses with explicit subject may appear in writing.
Previous research on the distributional properties of English nonfinite and nonverbal clauses with
explicit subject has primarily presented synchronic or diachronic accounts on some types of nonfinite
clauses in texts of specific registers [5; 6; 10], however, no comprehensive quantitative corpus and
machine-learning analysis of the distribution of all their types in modern English registers has been
presented.
3. Theoretical and methodological background
The presented study is based on the theoretical and methodological foundations of the new
framework of contemporary grammar studies – cognitive-quantitative construction grammar
(CQCxGr). The framework is built on the synergy of the theoretical tenets advocated by cognitive
linguistics (Langacker (1987; 1991); Janda, (2013)) and the constructionist approach (specifically, the
updated version of the Berkley construction grammar (Fillmore (1988); Östman, Fried, (2004)),
cognitive construction grammar (Goldberg (1995; 2006; 2019)), and usage-based construction grammar
(Hoffmann (2016); Hilpert (2019)) and the methodological principles of quantitative linguistics
(Levytskyi (2007)), quantitative corpus linguistics (Gries, Stefanowitsch (2004, 2013); Brezina (2018)),
automatic speech processing (Darchuk (2013)), and experimental linguistics (Gillioz, Zufferey (2020)),
thereby providing a competent qualitative-quantitative approach for examining general and
idiosyncratic features of language units.
The epistemological guidelines of cognitive-quantitative construction grammar entail providing an
explanation for the semiotic phenomena of language and speech on their mental basis and developing
a psychologically plausible description of language as one of the many cognitive and social systems
available to humans. From the framework’s perspective, language constitutes a repertoire of generalized
‘form-meaning’ pairings – constructions – of various degrees of schematicity and complexity. As non-
compositional, (fully) productive, cognitively entrenched (automated), and complex units,
constructions are holistic semiotic models for language representation – syntax, morphology, and
vocabulary – stored in a constructicon, a structured inventory of taxonomic networks of constructions
[11; 12].
A comprehensive account of the linguistic properties of a particular construction is the result of the
analysis of interrelated parameters of its form and meaning (prosodic, morphological, syntactic,
semantic, distributional, functional, pragmatic, etc.). The research toolkit of the cognitive-quantitative
construction grammar is determined by a usage-based orientation to language study, extensive corpus
data reference, active use of quantitative methods, and the application of specialized computer programs
for processing massive arrays of linguistic data. From the usage-based perspective, the mental
constructicon of speakers emerges as a result of recurrent interaction with language expressions
(constructions), with frequency of occurrence playing a key role in the cognitive entrenchment of
constructions. Consequently, corpus data are employed to explore constructions that conceptualize
fundamental human experience and/or are frequently used in a language community. Large arrays of
linguistic data cannot be efficiently analyzed without specialized software, which encourages
researchers in the field to utilize high-tech, sophisticated methods of quantitative analysis with
computer support. These methods open up new avenues for language research and have the potential to
solve numerous theoretical and practical aspects of language research.
From the perspective of the cognitive-quantitative construction grammar, detached nonfinite/
nonverbal clauses with explicit subject acquire the status of grammatical constructions, which we
nominate as ‘D(etached) N(on)F(inite)/ N(on)V(erbal) (with) E(xplicit) S(ubject) constructions’
(hereinafter referred to as DNF/NVES-constructions). DNF/NVES-constructions as syntactic structures
that include a nonfinite/nonverbal clause belong to the class of syntagmatically complex clause-level
constructions.
The DNF/NVES-constructions constitute a taxonomic constructional network, with every node
representing an individual type of construction. The given taxonomic network is organized around a
constructional schema (macro-construction – (dt-SubjPredNF/NV–cxn)), the characteristics of which are
inherited by less abstract meso-constructions and further acquired by individual micro-constructions
(dt-øaug-Subj PredNF/NV–cxn, dt-with-Subj PredNF/NV–cxn, dt-despite-Subj PredNF/NV–cxn, dt-without–
Subj PredNF/NV–cxn, dt-what_with-Subj PredNF/NV–cxn {N(on)F(inite): PI, PII, to-Inf; N(on)V(erbal):
NP, AdjP, AdvP, PP}). As micro-constructions are the most linguistically rich patterns, they serve as
the basis for linguistic and quantitative analysis.
4. Experiment: corpus, data, statistical software R and computer-
quantitative procedure
Today, corpora are used to solve a variety of issues, ranging from linguistic analysis to data mining
and machine learning. Corpora have paved the way for the development of programs for automatic text
translation and natural language processing, as they provide a large-scale data set to facilitate the
training and testing of various algorithms. The use of specialized statistical software to examine data
sets from certain corpora and then construct machine learning models from them is one of the most
significant developments in the field.
In this study, linguo-quantitative analysis is carried out for English DNF/NVES-constructions,
collected from the British National Corpus (https://www.english-corpora.org/) [4]. The data were
retrieved automatically between 2018 and 2020 using the in-built BNC search engine. In total, the
queries yielded 650 724 tokens that were then manually inspected to avoid spurious hits and formally
similar but functionally different constructions. After removing the false hits, the database includes 11
000 tokens for analysis.
The analysis of the distributional characteristics of the DNF/NVES-constructions involves a
quantitative linguistic corpus analysis of their distribution in the registers of contemporary English,
reflected in the parameter “Register distribution” (RegDSTN).
The basis for distinguishing the registers of the contemporary English language is the typology of
registers established by the developers of the British National Corpus, where registers are “language
varieties associated with a particular combination of situational characteristics and communicative
purpose” [13, p. 436]. The BNC includes texts of such registers as spoken (Spoken), newspaper
(Newspaper), magazine (Magazine), fiction (Fiction), academic (Academic), nonacademic (popular
academic) (Non-academic) and unclassified (Miscellaneous) texts. Each of the registers is represented
by a number of genres. For instance, Fiction is represented by drama (W_fict_drama), poetry
(W_fict_poetry), and prose (W_fict_prose). Unclassified texts include advertisements, biographies, e-
mails, school and university essays, etc. (For more information on the codification of genres in the
British National Corpus, see [14]) (6–9):
(6) [With the grass being so long]DNF/NVES-construction, you know? (SP:PS066) (unclear) grass (unclear)
wasn't it (BNC; KBP) – S_conv;
(7) [His breath ragged]DNF/NVES-construction, [his eyes near wild]DNF/NVES-construction, he stared at her, and
it came to him then that he wanted it all: the house, the money, and Theda, too (BNC; HE) –
W_fict_prose;
(8) [Despite these views being diametrically opposed]DNF/NVES-construction, both exist simultaneously in
attitudes to retired people (BNC; CE1) – W_ac_soc_science;
(9) Both parties feeling that they have achieved an agreement they can live with, [without it being
constantly undermined]DNF/NVES-construction (BNC; CFV) – W_advert.
The analysis of the DNF/NVES-constructions in terms of their distribution by registers is carried out
in the factors “spoken texts” (RegSpkn), “fiction texts” (RegFict), “magazine texts” (RegMag),
“newspaper texts” (RegNews), “non-academic texts” (RegNonAc), “academic texts” (RegAc), and
“unclassified texts” (RegMisc).
The frequency of constructions in the corpus indicates the degree of their entrenchment in the
language community and correlates with the number of tokens associated with the corresponding
parameter/ factor. The verification of the data retrieved from the BNC and the establishment of
statistically significant indicators are carried out using a three-stage linguistic and quantitative
procedure that involves the consistent application of the following statistical metrics: 1) multivariate
analysis of variance (MANOVA), 2) one-factor analysis of variance (ANOVA) and 3) Tukey’s multiple
comparison method. The obtained results are used to build a machine learning model (linear
discriminant analysis) to predict the register distribution of the DNF/NVES-constructions outside the
corpus. All quantifications are performed using the statistical data analysis platform R.
The statistical platform R is one of the most frequently used software applications in philological
research. This software is distributed as an open source program with a large number of free libraries
designed to solve problems of varying levels of complexity.
5. Results/ Discussion
5.1. Register distribution of the DNF/NVES-constructions: a quantitative
corpus-based analysis
A sample of 11 000 tokens of the micro-constructions of the network of English DNF/NVES-
constructions (dt-øaug-Subj PredNF/NV–cxn, dt-with-Subj PredNF/NV–cxn, dt-despite-Subj PredNF/NV–cxn,
dt-without–Subj PredNF/NV–cxn, dt-what_with-Subj PredNF/NV–cxn) has been quantitatively processed.
Table 1 displays the raw frequencies of the analyzed micro-constructions depending on the type of the
nonfinite and nonverbal predicate in the registers of the BNC.
Table 1
Raw frequencies of the micro-constructions within the “RegDSTN” parameter
Frequency of a micro-construction
PredNF PredNV
construction
Micro-
Factors of the “RegDSTN”
parameter
to-Inf
AdvP
AdjP
NP
PII
PP
PI
spoken texts (RegSpkn) 63 – – 1 – – 8
Subj PredNF/NV–cxn
fiction texts (RegFict) 1681 434 5 33 337 48 262
dt-øaug-
magazine texts (RegMag) 116 17 – 7 1 – 4
newspaper texts (RegNews) 188 9 1 3 2 2 26
non-academic texts
295 18 2 15 4 2 32
(RegNonAc)
academic texts (RegAc) 258 15 – 22 4 2 4
unclassified texts (RegMisc) 458 41 3 13 28 3 20
spoken texts (RegSpkn) 56 7 2 1 4 13 5
Subj PredNF/NV–cxn
fiction texts (RegFict) 559 193 49 2 68 73 70
dt-with-
magazine texts (RegMag) 319 77 33 1 21 26 32
newspaper texts (RegNews) 723 138 35 5 34 25 51
non-academic texts
639 185 64 – 66 29 61
(RegNonAc)
academic texts (RegAc) 456 156 34 – 32 3 60
unclassified texts (RegMisc) 996 285 67 5 75 44 112
spoken texts (RegSpkn) 6 – – – – – –
Subj PredNF/NV–cxn
–
dt-what_with-
fiction texts (RegFict) 18 2 2 1 3 1
magazine texts (RegMag) 6 – – – – – –
newspaper texts (RegNews) 5 – – – – – 2
non-academic texts
– – – – – – –
(RegNonAc)
academic texts (RegAc) 4 – – – – – –
unclassified texts (RegMisc) 5 – – – – – –
spoken texts (RegSpkn) 5 – – – – – 1
Subj PredNF/NV–cxn
fiction texts (RegFict) 18 – 3 – – 6 3
dt-without–
magazine texts (RegMag) 11 1 – – – – –
newspaper texts (RegNews) 2 – – – – – –
non-academic texts
8 1 – – – 1 –
(RegNonAc)
academic texts (RegAc) 19 2 1 – 1 – –
unclassified texts (RegMisc) 19 2 – 1 – – 1
spoken texts (RegSpkn) 2 – 1 – – – –
Subj PredNF/NV–
dt-despite-
fiction texts (RegFict) 26 3 10 1 – 3 –
magazine texts (RegMag) 12 7 14 – 3 – –
cxn
newspaper texts (RegNews) 32 6 16 – – 2 –
non-academic texts
13 20 33 – 1 1 –
(RegNonAc)
academic texts (RegAc) 22 23 32 – 1 1 –
unclassified texts (RegMisc) 19 17 24 – 3 3 1
According to the data in Table 1, there is a clear connection between the frequency of micro-
constructions with a particular type of predicate and a register. The unaugmented dt-øaug-
Subj PredNF/NV–cxn micro-construction tends to be most strongly associated with the fiction register,
demonstrating a significantly lower frequency of use in academic/non-academic texts, newspaper and
magazine texts, and it appears to be least frequently used in spoken texts. The augmented dt-with-
Subj PredNF/NV–cxn micro-construction performs nearly identically in fictional, non-academic, and
newspaper texts. However, if newspaper and magazine articles are taken to represent mass media
discourse generally, the indicators change slightly. The dt-with-Subj PredNF/NV–cxn micro-construction
is used most frequently in mass media texts and is almost as prevalent in popular academic and fictional
texts. The highest usage rates of the augmented dt-despite-Subj PredNF/NV–cxn are found in academic
texts, followed by popular academic and newspaper texts. The least frequently micro-construction
occurs in informal speech. The high frequency of the augmented dt-without–Subj PredNF/NV–cxn and dt-
without–Subj PredNF/NV–cxn micro-constructions in literary texts is correlated with the lowest frequency
in colloquial texts. However, in academic writing, dt-without–Subj PredNF/NV–cxn is slightly more
common.
The statistical significance of the observed quantitative differences is examined using a three-stage
computer and statistical strategy. At first, multivariate analysis of variance (MANOVA) is employed to
statistically validate the differences between the constructions in terms of factors within “RegDSTN”
parameter realization. Second, using one-factor analysis of variance (ANOVA), the statistically
significant differences in the use of micro-constructions for each of the selected factors are determined.
When such differences exist, Tukey’s multiple comparison is used to confirm the results and determine
which pairs of micro-constructions a particular factor is significant for. The calculations are performed
with the statistical software R and its freely available libraries.
As seen in Table 1, the frequency of constructions is represented by discrete interval values, some
data are missing, and the difference between the minimum and maximum values is substantial.
Therefore, for the designed computer and statistical strategy to be implemented, the collected data must
be standardized. Consequently, several data transformations are carried out. Initially, missing data are
replaced with zero values. The data are then transformed logarithmically to produce continuous interval
data using the formula: ln(𝑥𝑖𝑗 + 𝑐𝑜𝑛𝑠𝑡), where 𝑥𝑖𝑗 is the table value and const is set to 2. Because
ln(0 + 1) = 0, it is possible to set any positive number other than 1 [15, р. 5]. Subsequent calculations
are performed on the standardized data provided in Table 2.
Table 2
Standardized data of the micro-constructions within the “RegDSTN” parameter
Factors of the “RegDSTN” parameter
construction
Micro-
RegNonAc
Factors of the
RegNews
RegSpkn
RegMisc
RegMag
RegFict
“RegDSTN”
RegAc
parameter
PredPI 4,1743873 7,4283 4,7707 4,7875 5,6937 5,5607 6,1312
Subj PredNF/NV–cxn
PredPII 0,6931472 6,1862 2,9444 2,3979 2,9957 2,8332 3,7612
dt-øaug-
Predto-Inf 0,6931472 1,9459 0,6931 1,0986 1,3863 0,6931 1,6094
PredNP 1,0986123 3,5553 2,1972 1,6094 2,8332 3,1781 2,7081
PredAdjP 0,6931472 5,826 1,0986 1,3863 1,7918 1,7918 3,4012
PredAdvP 0,6931472 3,912 0,6931 1,3863 1,3863 1,3863 1,6094
PredPP 2,3025851 5,5759 1,7918 3,3322 3,5264 1,7918 3,091
NF/NV–
with-
dt
Pred
PredPI 4,060443 6,3297 5,7714 6,5862 6,1269 6,9058 8,2324
Subj
cxn
-
PredPII 2,1972246 5,273 4,3694 4,9416 5,0626 5,6595 6,9489
construction Factors of the “RegDSTN” parameter
Micro-
RegNonAc
Factors of the
RegNews
RegSpkn
RegMisc
RegMag
RegFict
“RegDSTN”
RegAc
parameter
Predto-Inf 1,3862944 3,9318 3,5553 3,6109 3,5835 4,2341 5,6664
PredNP 1,0986123 1,3863 1,0986 1,9459 0,6931 1,9459 2,7726
PredAdjP 1,7917595 4,2485 3,1355 3,5835 3,5264 4,3438 5,7104
PredAdvP 2,7080502 4,3175 3,3322 3,2958 1,6094 3,8286 5,3706
PredPP 1,9459101 4,2767 3,5264 3,9703 4,1271 4,7362 5,9738
PredPI 2,0794415 2,9957 2,0794 1,9459 0,6931 1,7918 1,9459
Subj PredNF/NV–cxn
dt-what_with-
PredPII 0,6931472 1,3863 0,6931 0,6931 0,6931 0,6931 0,6931
Predto-Inf 0,6931472 1,3863 0,6931 0,6931 0,6931 0,6931 0,6931
PredNP 0,6931472 0,6931 0,6931 0,6931 0,6931 0,6931 0,6931
PredAdjP 0,6931472 1,0986 0,6931 0,6931 0,6931 0,6931 0,6931
PredAdvP 0,6931472 1,6094 0,6931 0,6931 0,6931 0,6931 0,6931
PredPP 0,6931472 1,0986 0,6931 0,6931 0,6931 0,6931 0,6931
PredPI 1,9459101 2,9957 2,5649 1,3863 2,3026 3,0445 3,0445
Subj PredNF/NV–cxn
dt-without–
PredPII 0,6931472 0,6931 1,0986 0,6931 1,0986 1,3863 1,3863
Predto-Inf 0,6931472 1,6094 0,6931 0,6931 0,6931 1,0986 0,6931
PredNP 0,6931472 0,6931 0,6931 0,6931 0,6931 0,6931 1,0986
PredAdjP 0,6931472 0,6931 0,6931 0,6931 0,6931 1,0986 0,6931
PredAdvP 0,6931472 2,0794 0,6931 0,6931 1,0986 0,6931 0,6931
PredPP 1,0986123 1,6094 0,6931 0,6931 0,6931 0,6931 1,0986
PredPI 1,3862944 3,3322 2,6391 3,5264 2,7081 3,1781 3,0445
Subj PredNF/NV–cxn
PredPII 0,6931472 1,6094 2,1972 2,0794 3,091 3,2189 2,9444
dt-despite-
Predto-Inf 1,0986123 2,4849 2,7726 2,8904 3,5553 3,5264 3,2581
PredNP 0,6931472 1,0986 0,6931 0,6931 0,6931 0,6931 0,6931
PredAdjP 0,6931472 0,6931 1,6094 0,6931 1,0986 1,0986 1,6094
PredAdvP 0,6931472 1,6094 0,6931 1,3863 1,0986 1,0986 1,6094
PredPP 0,6931472 0,6931 0,6931 0,6931 0,6931 0,6931 1,0986
On the first stage, statistically significant differences in the frequency distribution of the DNF/NVES-
constructions in the BNC registers are measured. Multivariate analysis of variance (MANOVA) [25, p.
198] is employed to statistically substantiate the differences between the micro-constructions (dt-øaug-
Subj PredNF/NV–cxn, dt-with-Subj PredNF/NV–cxn, dt-despite-Subj PredNF/NV–cxn, dt-without–
Subj PredNF/NV–cxn, dt-what_with-Subj PredNF/NV–cxn) in terms of realization of the “RegDSTN”
parameter. In Table 2, the factors of the “RegDSTN” parameter (independent variables) are displayed
in columns, and their values are represented in rows. The following statistical hypotheses are developed:
H0: The differences between the micro-constructions within the “RegDSTN” parameter are
insignificant, and the measured dependencies are random.
H1: The differences between the micro-constructions within the “RegDSTN” parameter are
significant, and the measured dependencies are important and regular.
The program is run in the RStudio console to perform MANOVA:
library('openxlsx')
file = file.choose()
tab <- read.xlsx(file, sheet = 1, startRow = 1, colNames = TRUE, rowNames = FALSE)
manova_test <- manova(cbind(RegSpkn, RegFict, RegMag, RegNews, RegNonAc, RegAc,
RegMisc) ~ as.factor(Construction), data=tab)
summary(manova_test)
The results of MANOVA test are as follows.
Df Pillai approx F num Df den Df Pr(>F)
as.factor(Construction)4 2.0144 3.9132 28 108 1.651e-07 ***
Residuals 30
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The results show that Pr(F > F*) is 1.651e-07 and significantly less than 0,01; therefore, the null
hypothesis is rejected, and the alternative hypothesis is accepted: The differences between the micro-
constructions (dt-øaug-Subj PredNF/NV–cxn, dt-with-Subj PredNF/NV–cxn, dt-despite-Subj PredNF/NV–cxn,
dt-without–Subj PredNF/NV–cxn, dt-what_with-Subj PredNF/NV–cxn) within the “RegDSTN” parameter
are significant, and the measured dependencies are important for distinguishing their prevalent spheres
of usage.
The second stage is aimed at examining the impact of each of the specified factors within the
“RegDSTN” parameter on the analyzed constructions. For this purpose, one-way analysis of variance
(ANOVA) [16, p. 171] is carried out. For each of the factors specified, the two statistical hypotheses
are reformulated:
Н0: The differences between the micro-constructions within the factor “RegSpkn” (“RegFict”/
“RegMag”/ “RegNews”/ “RegNonAc”/ “RegAc”/ “RegMisc”) of the the “RegDSTN” parameter are
insignificant, and the identified dependencies are random.
Н1: The differences between the micro-constructions within the factor “RegSpkn” (“RegFict”/
“RegMag”/ “RegNews”/ “RegNonAc”/ “RegAc” “RegMisc”) of the the “RegDSTN” parameter are
important and regular.
The following are the results obtained for the factor “RegSpkn”:
Df Sum Sq Mean Sq F value Pr(>F)
Construction 4 9.017 2.2543 3.409 0.0206 *
Residuals 30 19.838 0.6613
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The results indicate statistically significant differences at the 95% significance level
(Pr(>F)=0,0206˂0,05) between the micro-constructions under study within the factor “RegSpkn”, i.e.
the frequency of certain micro-constructions in spoken texts can be a factor differentiating them from
the rest of the analyzed constructions. In general, the results of the ANOVA test revealed statistically
significant differences among the micro-constructions based on the factors “literary texts”
(Pr(>F)=4,22e-06˂0,001), “magazine texts” (Pr(>F)=0,000454˂0,001), “newspaper texts”
(Pr(>F)=1,54e-05˂0,001), and “non-academic texts” (Pr(>F)=3,97e-05˂0,001).
As ANOVA indicates the presence of differences but does not specify where these differences are
best manifested, the third stage requires the application of the Tukey post-hoc test. All the calculations,
including the ANOVA test, are performed by the script provided:
anova_item <- aov(RegSpkn ~ Construction, data = tab)
summary(anova_item)
TukeyHSD(anova_item, ordered = FALSE, conf.level = 0.95)
Below is the output of the command that calculates the Tukey test:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = RegSpkn ~ Construction, data = tab)
$Construction
diff lwr upr p adj
what_with-despite 0.04109744 -1.21970747 1.30190235 0.9999808
with-despite 1.31966450 0.05885959 2.58046941 0.0366813
øaug -despite 0.62821869 -0.63258622 1.88902360 0.6043795
without-despite 0.07994511 -1.18085980 1.34075002 0.9997284
with-what_with 1.27856706 0.01776215 2.53937197 0.0455838
øaug-what_with 0.58712125 -0.67368366 1.84792616 0.6625967
without-what_with 0.03884767 -1.22195724 1.29965258 0.9999846
øaug-with -0.69144581 -1.95225072 0.56935910 0.5145378
without-with -1.23971939 -2.50052430 0.02108552 0.0557355
without- øaug -0.54827358 -1.80907849 0.71253134 0.7160811
The results indicate that the following pairs of compared micro-constructions have the greatest
differences in use in spoken texts (level of significance p < 0,05): 1) dt-with-Subj PredNF/NV–cxn and dt-
despite-Subj PredNF/NV–cxn; 2) dt-with-Subj PredNF/NV–cxn and dt-what_with-Subj PredNF/NV–cxn.
The Tukey’s multiple comparison method applied to other factors revealed that the greatest number
of statistically significant differences are found in the factor “literary texts” (RegFict) between 6 pairs
of micro-constructions, in the factor “newspaper texts” (RegNews) and “academic texts” (RegAc)
between 4 pairs, in the factor “magazine texts” (RegMag) and “non-academic texts” (RegNonAc)
between 3 pairs, and in the factor “spoken texts” between 2 pairs of micro-constructions.
Among the constructions, the greatest statistically significant differences are recorded between dt-
with-Subj PredNF/NV–cxn construction and without-, despite-, what_with-augmented micro-
constructions. The indicators of dt-with-Subj PredNF/NV–cxn and dt-what_with-Subj PredNF/NV–cxn
differ in all of the identified factors. The dt-with-Subj PredNF/NV– cxn and dt-despite-Subj PredNF/NV–cxn
constructions do not differ only in the factor “academic texts”, the dt-with-Subj PredNF/NV–cxn and dt-
without–Subj PredNF/NV–cxn do not differ in the factor “spoken texts”, but the differences between these
micro-constructions in the other factors are significant.
The unaugmented micro-construction dt-øaug-Subj PredNF/NV–cxn demonstrates statistically
significant differences with despite-, what_with-, without-augmented constructions in terms of
occurrence in fiction texts, but does not show differences in this factor with dt-with-Subj PredNF/NV–
cxn.
Multiple Tukey’s comparisons revealed no statistically significant differences with respect to the
analyzed factors between the dt-despite-Subj PredNF/NV–cxn, dt-without–Subj PredNF/NV–cxn and dt-
what_with-Subj PredNF/NV–cxn micro-constructions. The findings demonstrate that the distribution of
these micro-constructions across registers in the BNC is homogeneous, with a general tendency toward
low usage in spoken text types and prevalence in written texts.
Based on the results of a three-stage linguistic and quantitative procedure on the BNC, it is evident
and statistically proven that certain registers (factors) have a greater influence on the occurrence of the
DNF/NVES-constructions in them, i.e., certain micro-constructions tend to occur more frequently in
texts of certain registers than others. At this point of our research, the question arises: Can the
established register distribution of the DNF/NVES-constructions be extrapolated beyond the BNC? To
answer this question, the data obtained in the quantitative corpus-based procedure are subjected to
machine-learning modeling. The machine-learning model will probabilistically predict the distribution
of the analyzed constructions in present-day English usage.
5.2. Register distribution of the DNF/NVES-constructions: a machine-
learning approach
To predict the register distribution of the DNF/NVES-constructions beyond the analyzed corpus, it
is essential to assess the viability of building a machine learning model to classify the constructions
based on statistical test results. Consequently, linear discriminant analysis [17, p. 667] is employed:
1) to build a model for classifying dt-øaug-Subj PredNF/NV–cxn, dt-with-Subj PredNF/NV–cxn, dt-despite-
Subj PredNF/NV–cxn, dt-without–Subj PredNF/NV–cxn, dt-what_with-Subj PredNF/NV–cxn constructions,
given that the statistical indicators will determine their register distribution in the corpus; 2) to identify
the variables (i.e., factors “spoken texts” (RegSpkn), “fiction texts” (RegFict), “magazine texts”
(RegMag), “newspaper texts” (RegNews), “non-academic texts” (RegNonAc), “academic texts”
(RegAc), and “unclassified texts” (RegMisc)) that contribute most to the separation of constructions.
The objective of linear discriminant analysis is to find an additional axis (axes) that will pass through
the entire set of points (each point is a construction represented in the coordinate system of categories)
so that their projections on it will provide the greatest possible separation between classes [18]. The
location of such an axis is determined by a linear discriminant function, which defines the impact of
each feature (specifically, the corpus register) using the calculated coefficients.
The data from Table 2 and the specialized package MASS [19; 20] are used to build the model for
linear discriminant analysis in R. Presented is the code performing the calculations:
library('openxlsx')
library('caret')
library('MASS')
file = file.choose()
tab <- read.xlsx(file,sheet = 1, startRow = 1, colNames = TRUE,rowNames = FALSE)
set.seed(101)
training.pattern <- createDataPartition(y = tab$Category, p = 0.75, list = FALSE)
train.data <- tab[training.pattern, ]
test.data <- tab
lda_data <- lda(Category ~ ., data = train.data)
lda_data
predictions <- predict(lda_data, test.data)
p1 <- predictions$class
conf_tab <- table(Predicted = p1, Actual = test.data$Category)
conf_tab
Since the authors explain each command in detail in the article [15], only the analysis of the results
is provided here.
Call:
lda(Construction ~ ., data = train.data)
Prior probabilities of groups:
despite what_with with with_less without
0.2 0.2 0.2 0.2 0.2
Group means:
RegSpkn RegFict RegMag RegNews RegNonAc RegAc RegMisc
despite 0.8762492 1.737047 1.7674338 1.8781271 2.0408021 2.1356103 2.2607577
what_with 0.6931472 1.212066 0.6931472 0.6931472 0.6931472 0.6931472 0.6931472
with 2.2070640 4.247804 3.5437580 3.9939997 3.4336548 4.4862832 5.7835696
øaug 1.5415935 5.145737 1.9986316 2.3981321 2.7966955 2.3428092 3.2672570
without 0.9695185 1.460676 1.0726917 0.8086717 1.0965419 1.2681451 1.3357226
Coefficients of linear discriminants:
LD1 LD2 LD3 LD4
RegSpkn -1.0291488 0.1787200 -1.71021035 -0.1257059
RegFict -1.2299897 1.1801267 -0.03610643 0.2861375
RegMag -1.2218508 -1.1909062 0.94767175 1.0348112
RegNews 2.3230869 -0.5053956 1.67740983 1.8661933
RegNonAc -2.1161569 0.7294720 0.46712888 -1.6034093
RegAc -0.5048724 -1.0978915 -0.92176444 -0.8206950
RegMisc 3.7993088 0.8826389 -0.68935723 -0.5295016
Proportion of trace:
LD1 LD2 LD3 LD4
0.8144 0.1663 0.0148 0.0044
Actual
Predicted despite what_with with øaug without
despite 6 0 0 0 1
what_with 1 7 0 0 2
with 0 0 7 0 0
øaug 0 0 0 6 0
without 0 0 0 1 4
As demonstrated by the results, the greatest separation exists along the LD1 and LD2 axes. The
weight coefficients enable to determine the contribution of each variable (register) to distinguishing
between the objects (more precisely, dt-øaug-Subj PredNF/NV–cxn, dt-with-Subj PredNF/NV–cxn, dt-
despite-Subj PredNF/NV–cxn, dt-without–Subj PredNF/NV–cxn, dt-what_with-Subj PredNF/NV–cxn micro-
constructions). According to the obtained results for the first linear discriminant function, the most
significant for the separation of the DNF/NVES-constructions are “unclassified texts” (RegMisc),
“newspaper texts” (RegNews) and “non-academic texts” (RegNonAc). In the LD2 function, the most
significant are “fiction texts” (RegFict), “magazine texts” (RegMag) and “academic texts” (RegAc).
The confusion matrix [21, p. 217-218; 22], however, is more important for assessing the model’s
effectiveness. It is constructed using the commands:
conf_tab <- table(Predicted = p1, Actual = test.data$Category)
conf_tab
The confusion matrix is a five-by-five table (Table 3), with the columns representing the actual
values of the constructions and the rows displaying the predicted values. The number of predicted
constructions is located at the intersection of the row and column. The main diagonal of the matrix
represents the number of correct classifications performed by the newly constructed model. The
obtained results show that these are 30 out of 35 records in the test sample. This allows for determining
one of the estimates of the created classifier’s effectiveness, namely Accuracy, i.e., the proportion of
accurate predictions to the total number of test sample constructions. Formula (1) describes the
calculation:
30 (1)
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = = 0,857,
35
The conducted classification of the constructions is reasonably accurate, indicating that the created
model is significantly more effective than the model created in the study of the DNF/NVES-
constructions based on part-of-speech data [15]. To estimate the precision and recall values for this
model the F-measure (harmonic mean of Precision and Recall) for each construction is calculated. Each
calculation is displayed in the table (Table 3).
The data in Table 3 show that the created model is effective for classifying all the analyzed
constructions, except for without-augmented one. The value of F-measure (harmonic mean for
Precision and Recall) for each construction confirms this (2).
Fdespite = 0,86; (2)
Fwhat_with = 0,82;
Fwith = 1;
Føaug = 0,92;
Fwithout = 0,67;
Table 3
Confusion matrix and Precision and Recall results
Actual values
despite what_with with øaug without
despite 6 0 0 0 1 0,857
Precision
Predicted
values
what_with 1 7 0 0 2 0,7
with 0 0 7 0 0 1
øaug 0 0 0 6 0 1
without 0 0 0 1 4 0,8
0,857 1 1 0,857 0,57
Recall
Importantly, a number of notable outcomes emerge from the analysis of the collected data:
1) The overall efficiency (Accuracy = 0.857) of the machine learning model for classifying dt-øaug-
Subj PredNF/NV–cxn, dt-with-Subj PredNF/NV–cxn, dt-despite-Subj PredNF/NV–cxn, dt-without–
Subj PredNF/NV–cxn, dt-what_with-Subj PredNF/NV–cxn micro-constructions in the BNC registers is quite
high, therefore it can be used to predict the occurrence of the DNF/NVES-constructions in the specified
registers outside the analyzed corpus.
2) The model is the most effective in separating between dt-with-Subj PredNF/NV–cxn and dt-øaug-
Subj PredNF/NV–cxn micro-constructions. Less effectively it classifies dt-despite–Subj PredNF/NV–cxn and
dt-what_with-Subj PredNF/NV–cxn micro-constructions. The least effective the model separates dt-
without–Subj PredNF/NV–cxn micro-construction.
3) “Unclassified texts” (RegMisc), “newspaper texts” (RegNews), “non-academic texts”
(RegNonAc), “fiction texts” (RegFic), “magazine texts” (RegMag), and “academic texts” (RegAc) have
the most weight in separating the micro-constructions.
The dot diagram displays the results of the linear discriminant analysis for the register distribution
of the DNF/NVES-constructions in the BNC (Fig. 1):
Figure 1: Graphic representation of the linear discriminant analysis of register distribution for the
DNF/NVES-constructions in the BNC
As can be seen from Fig. 1, the data demonstrate a clear distinction between two micro-constructions
– dt-with-Subj PredNF/NV–cxn and dt-øaug-Subj PredNF/NV–cxn, lending credibility to the results of
Tukey’s aposteriori tests. Therefore, it is reasonable to assume that these micro-constructions will be
distinguished similarly in the investigated registers outside the BNC. However, it is also necessary to
consider the difficulty of distinguishing between the dt-despite–Subj PredNF/NV–cxn, dt-without–
Subj PredNF/NV–cxn and dt-what_with-Subj PredNF/NV–cxn micro-constructions, which reduces the
overall accuracy of the model (<0.9) and prevents us from drawing conclusions about the model’s
overall effectiveness for the entire language. To address the limitations of this model, additional
research is required, such as the construction of a new Ida model with a larger sample size or the
implementation of an alternative method.
6. Conclusions
The results of this study conclusively demonstrate the applicability of an integrated quantitative
corpus linguistic and machine learning analysis to the investigation of the linguistic behavior of
complex clause-level constructions, such as English detached nonfinite/nonverbal with explicit subject
constructions.
The analysis of the register distribution of the English DNF/NVES-constructions reveals that these
syntactic patterns are more productive in present-day English usage than the data of diachronic studies
indicate. At the current stage, the English DNF/NVES-constructions exhibit a steady tendency toward
further improvement and development, as evidenced by the analysis of their distribution by registers,
based on a representative sample from the British National Corpus. The observed results refute the
prevalent view in modern English grammars that these constructions have a limited scope of use and
prove that the DNF/NVES-constructions expand their distribution, penetrating both the written and
spoken registers of contemporary English discourse.
The findings presented in this paper show the need for future investigations. Clearly, additional
studies of the analyzed syntactic patterns from the cognitive-quantitative construction grammar
standpoint will be of great interest. The proposed computerized linguo-quantitative strategy will be used
to investigate other linguistic parameters (positional, referential, functional, etc.) of the analyzed
constructions and statistically validate the determining parameters (factors) that affect the functional
dynamics and variability of the network of detached nonfinite/ nonverbal with explicit subject
constructions in present-day English.
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