10.6092/issn.2785-0943/13861
We investigate the correlations between A- and P-lability for verbal arguments with other typological parameters using large, syntactically annotated corpora of online news in 28 languages. To estimate how much lability is observed in a language, we measure associations between Verbs or Verb + Noun combinations and the alternating constructions in which they occur. Our correlational analyses show that high P-lability scores correlate strongly with the following parameters: little or no case marking; weaker associations between lexemes and the grammatical roles A and P; rigid order of Subject and Object; and a high proportion of verb-medial clauses (SVO). Low P-lability correlates with the presence of case marking, stronger associations between nouns and grammatical roles, relatively flexible ordering of Subject and Object, and verb-final order. As for A-lability, it is not correlated with any other parameters. A possible reason is that A-lability is a result of more universal discourse processes, such as deprofiling of the object, and also exhibits numerous lexical and semantic idiosyncrasies. The fact that P-lability is strongly correlated with other parameters can be interpreted as evidence for a more general typology of languages, in which some tend to have highly informative morphosyntactic and lexical cues, whereas others rely predominantly on contextual environment, which is possibly due to fixed word order. We also find that P-lability is more strongly correlated with the other parameters than any of these parameters are with each other, which means that it can be a very useful typological variable.&nbsp;
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John A. Hawkins
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Linguistic Typology at the Crossroads
tight-fit and loose-fit languages
Verb-argument lability and its correlations with other typological parameters. A quantitative corpus-based study
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Research articles
2022-04-14
2021-11-24
2022-04-14
2022-04-14
94-120
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John A. Hawkins
2022/04/14
10.6092/issn.2785-0943/13861
94
University of California Davis, Cambridge University
2785-0943
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tight-fit and loose-fit languages
en
120
Wijffels, Jan, Milon Straka &amp; Jana Straková. 2018. udpipe: Tokenization, parts of speech tagging, lemmatization and dependency parsing with the UDPipe NLP Toolkit. R package version 0.7.
Verb-argument lability and its correlations with other typological parameters. A quantitative corpus-based study
https://typologyatcrossroads.unibo.it/article/download/13861/14097
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           Linguistic Typology at the Crossroads 2-1 (2021): 94-120    ISSN 2785-0943



    Verb-argument lability and its correlations with other
typological parameters: a quantitative corpus-based study

                         NATALIA LEVSHINA1, JOHN A. HAWKINS2,3
                  1
                      MAX PLANCK INSTITUTE FOR PSYCHOLINGUISTICS, NIJMEGEN,
                  2
                      UNIVERSITY OF CALIFORNIA, DAVIS, 3CAMBRIDGE UNIVERSITY



                       Submitted: 24/11/2021    Revised version: 28/02/2022
                         Accepted: 10/03/2022       Published: 14/04/2022




                                              Abstract
We investigate the correlations between lability for verbal arguments with other typological
parameters using large, syntactically annotated corpora of online news in 28 languages. We
focus on A-lability, when the A argument alternates with S (e.g., She is singing vs. She is singing
a song), and P-lability, when the P-argument alternates with S (e.g., She opened the door vs.
The door opened). To estimate how much lability is observed in a language, we measure
associations between Verbs or Verb + Noun combinations and the alternating constructions
in which they occur. Our correlational analyses show that high P-lability scores correlate
strongly with the following parameters: little or no case marking; weaker associations
between lexemes and the grammatical roles A and P; rigid order of Subject and Object; and
a high proportion of verb-medial clauses (SVO). Low P-lability correlates with the presence
of case marking, stronger associations between nouns and grammatical roles, relatively
flexible ordering of Subject and Object, and verb-final order. As for A-lability, it is not
correlated with any other parameters. A possible reason is that A-lability is a result of more
universal discourse processes, such as deprofiling of the object, and also exhibits numerous
lexical and semantic idiosyncrasies. The fact that P-lability is strongly correlated with other
parameters can be interpreted as evidence for a more general typology of languages, in which
some have highly informative morphosyntactic and lexical cues, whereas others rely
predominantly on contextual environment, which is enabled by fixed word order. We also
find that P-lability is more strongly correlated with the other parameters than any of these
parameters are with each other, which means that it can be a very useful typological variable.


Keywords: verb-argument lability; corpora; Universal Dependencies; word order; case
marking; tight-fit and loose-fit languages.




https://doi.org/10.6092/issn.2785-0943/13861                                                    94
Linguistic Typology at the Crossroads 2-1 (2022): 94-120


1. Theoretical background


The usefulness of a typological parameter depends on how many other parameters it
helps us to predict. Greenberg’s (1963) word order correlations have been such a
major achievement in linguistics because they connected many diverse and seemingly
unrelated word order patterns. In this paper we demonstrate that the strength of
attraction of verbs (as well as their arguments) to specific subcategorization frames,
which can be defined in terms of verb-argument lability, can be a useful parameter,
because it is strongly correlated with many others.
   The attraction of verbs to specific subcategorization frames has been argued to be
a part of the typology of tight-fit versus loose-fit languages. The terms were coined
by Hawkins (1986: 121–127, 1995; see also Müller-Gotama 1994). Generally
speaking, tight-fit languages have unique surface forms that map onto more
constrained meanings, whereas loose-fit languages have vaguer forms with less
constrained meanings and they rely on word-external and contextual features as
defined in Hawkins (2019) for the assignment of meanings. For example, grammatical
roles in tight-fit languages have a narrower semantic range than grammatical roles in
loose-fit languages. The languages Jakaltek and Halkomelem strictly exclude
inanimate subjects in transitive clauses (Aissen 2003), while English and Swedish
merely strongly disprefer them (Dahl 2000). There are also more gradient
distinctions. Both English and German allow for different kinds of subjects, but
English is looser than German in the semantic roles that can fill the subject position
(Hawkins 1986, 2019), and also than Russian and Korean.
   The strength of the associations with grammatical roles is correlated with other
linguistic parameters, including more explicit grammatical coding (e.g., formal case
marking and use of complementizers and relativizers), avoidance of raisings and long
distance WH-movements. Tight-fit languages have fewer instances of syntactic
category ambiguity. For example, the English word book can be both a noun and a
verb, while in German the corresponding noun and verb have different forms, Buch –
buchen. Moreover, verb-final languages are often semantically tight (see also Levshina
2020).
   If these parameters change, they often change together. English is a well-known
case (Hawkins 1986, 2012). The loss of morphology correlated, in particular, with
the emergence of SVO order, long distance movement and raising, greater category
ambiguity and other features, including fewer restrictions on the semantics of



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Levshina, Hawkins                                                     Verb-argument lability


syntactic arguments. In contrast, German is more conservative. It preserves case
marking, verb-final order (for all verbs in subordinate clauses and for non-finite verbs
in main clauses), and it still has some variability in the order of Subject and Object.
In addition, German has fewer instances of category ambiguity, tighter associations
between semantics and roles, and very limited examples of raising. Generally
speaking, English is more structurally ambiguous than German. For example, raising
and control constructions are not distinguished formally in surface structure. Compare
Sue happened to win the lottery (raising) and Sue hoped to win the lottery (control). In
German, these are distinguished by formally different constructions. In Hawkins’
terminology (2019), English relies more on word-external properties to derive
meanings from ambiguous or vague surface forms, whereas German relies more on
distinct grammatical and lexical patterns and on word-internal properties.
  Importantly for our study, verbs in loose-fit languages have a broader set of
subcategorization frames than in tight-fit languages. For example, the English verb
open can be both transitive (e.g, I opened the door) and intransitive (The door opened),
while German distinguishes formally between the transitive öffnen "open (tr.)" and
the reflexive verb sich öffnen "open (intr.)".
  The term 'lability' in the context of verb-argument structure is used in the
typological literature to refer to alternations between the arguments of one and the
same verb (as e.g., P or S with a verb like open) or between the arguments of
alternating verbs in pairs such as teach and learn that describe a common event from
different perspectives. The precise definitions proposed by different authors have
varied in terms of how general or restrictive they are with respect to the alternations
in question (compare, e.g., Nichols 1986; Nichols et al. 2004; Dixon 1994;
Haspelmath 1993; Letuchiy 2009; Creissels 2014). In the present context we focus on
a set of core patterns that most definitions subsume, which can be readily identified
in our corpora and in which one and the same verb can be used transitively or
intransitively without any formal change while the arguments alternate in their status
as A or P or S. Examples from English are break, open, eat and sing. We are agnostic
with regard to the question of which use (transitive or intransitive) is basic, and which
is a result of valency derivation (but see Nichols et al. 2004 for insightful discussion
of this issue and typological comparison). Syntactically annotated corpora enable us
to track the arguments of individual verbs in many different languages and so permit
us to quantify the degree of lability within and across languages. The resulting lability




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Linguistic Typology at the Crossroads 2-1 (2022): 94-120


scores can then be tested against other typological variables derived from the same
corpora.1
     More precisely, we consider two types of verb-argument lability, referred to as A-
lability and P-lability. A-lability is defined in (1):


(1)     A-lability occurs when the A argument of a verb in a transitive clause remains
        the same in a corresponding intransitive clause from which the P argument has
        been removed without any formal change to the verb.


In other words, with the same verb, the A-argument can turn into an S-argument:
A=S (Dixon 1994).2 Examples are the unspecified object alternation (2a), the
understood body-part alternation (2b) and the characteristic property alternation (2c)
(Levin 1993).


(2)     a. Unspecified object alternation
           Jack ate the cake. - Jack ate.
        b. Understood body-part alternation
           The Queen waved her hand at the crowd. - The Queen waved at the crowd.
        c. Characteristic property alternation
           The dog bites strangers. - The dog bites.


P-lability is defined in (3):


(3)     P-lability occurs when the same argument can be used as the subject of a verb
        in an intransitive clause (S) and as the direct object (P) of a corresponding
        transitive clause without any formal change to the verb.


1
    Notice that the large-scale empirical study described here makes it straightforward to identify
alternating arguments for one and the same verb, but does not lend itself readily to identifying
alternating arguments among semantically related pairs of verbs such as teach and learn or like and
please, in the absence of semantic features or tags that can identify the relevant pairs of verbs. Our
definition is accordingly more restricted in this respect than that proposed in, e.g., Letuchiy's (2009)
summary paper of the typology of lability, but less restricted in other respects (see fn.2).
2
    Note that this type of lability is not considered as such by Letuchiy (2009), for whom lability
necessarily involves a change in the semantic role of the subject. For him, verbs like sing and drink
occurring with and without a direct object are 'pseudo-labile'. Letuchiy's definition of lability proper is
accordingly more restrictive in this respect than ours (compare fn.1).


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Levshina, Hawkins                                                          Verb-argument lability




In other words, with the same verb S=P (Dixon 1994). Examples are the causative-
inchoative alternation (4a), the middle alternation (4b) and the induced action
alternation (4c) (Levin 1993).


(4)     a. Causative-inchoative alternation
           The boy broke the vase. - The vase broke.
        b. Middle alternation
           The publisher sells the book. - The book sells well.
        c. Induced action alternation
           She jumped the horse over the fence. - The horse jumped over the fence.


The above-mentioned contrast between English open (both transitive and intransitive)
and German öffnen "open" (transitive only) with its reflexive verb counterpart in the
corresponding intransitive sich öffnen "open (intr.)" suggests that English has more P-
lability (causative-inchoative alternations, in particular) than German. However, this
has not yet been examined in corpora and using quantitative measures.
     In this paper we fill this gap, measuring A- and P-lability in languages with the
help of large corpora, which are described in Section 2. We compute the Mutual
Information between verbs, or combinations of verbs and nouns, and the alternating
constructions in which they occur. The procedure and the scores are presented in
Section 3. Then, we test the correlations between different measures of A- and P-
lability and four other variables which have been used in the literature on tight-fit
and loose-fit languages and more generally: word order rigidity; the position of the
verb in the sentence; case marking; and the strength of associations between nouns
and the grammatical roles of Subject and Object (Section 4). Finally, in Section 5 we
discuss our findings and conclusions.


2. Data and method


We used the Leipzig Corpus Collection (Goldhahn et al. 2012)3. We first selected 30
online news corpora with 1M sentences in each of the following languages: Arabic,
Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French,
German, Greek (modern), Hindi, Hungarian, Indonesian, Italian, Japanese, Korean,

3
    http://wortschatz.uni leipzig.de/en/download/ (accessed 2022.02.27).


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Linguistic Typology at the Crossroads 2-1 (2022): 94-120


Latvian, Lithuanian, Persian, Portuguese, Romanian, Russian, Slovenian, Spanish,
Swedish, Tamil, Turkish and Vietnamese. The corpora were annotated with the
Universal Dependencies pipeline udpipe (Wijffels, Straka & Straková 2018), which
allowed us to extract the subject, the direct object, the predicate, as well as their
lemma, part of speech and morphological features, and other useful information. Due
to our doubts about the quality and consistency of verb lemmatization in the data
from Tamil and Turkish, these languages were later excluded. This is why we had 28
languages in the final sample.
   In order to find patterns of A-lability, we extracted the frequencies of all verb
lemmas with the same noun in subject position (represented by the Universal
Dependency 'nsubj') with and without any kind of nominal or pronominal direct
object (the Universal Dependency 'obj'). Consider the examples in Table 1.


                             A-lability Frequencies
            Verb                Subject              Transitive         Intransitive
            be                  idea                 0                  140
            learn               student              21                 35
            play                team                 55                 47


                        Table 1: Examples of frequencies relevant for A-lability.


The table shows that the verb be with the noun idea as subject occurs 140 times (e.g.,
the idea was…), only in intransitive clauses. This is not surprising. The combination
student + learn occurs 21 times with a direct object (e.g., the students learn languages)
and 35 times without (e.g., the students learn). This is an example of A-lability. In this
paper, we measure the degree of A-lability by estimating the degree of the skew of a
Verb + Noun combination towards the Transitive construction, where the noun is
the A-argument, or the Intransitive use, where the noun is the S-argument. The
stronger the bias towards one or the other use across different Verb + Noun
combinations in a corpus, the weaker the A-lability. If many combinations behave
like play + team, the A-lability will be high. If more combinations behave like be +
idea, the A-lability will be low.
   In order to identify examples of P-lability, we extracted the frequencies of all verb
lemmas (only predicates of main clauses) with the same noun occurring as direct
object and as intransitive subject. Consider the examples in Table 2.




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Levshina, Hawkins                                                                 Verb-argument lability


                          P-lability Frequencies
          Verb               Noun                 Intr. subject +     Verb + Object
                                                  Verb
          die                people               64                  0
          open               door                 36                  149
          begin              work                 35                  33


                      Table 2: Examples of frequencies relevant for P-lability.


The numbers should be read as follows. The verb die occurs with the noun people only
as an intransitive subject (64 times), and never as an object. The verb open with the
noun door as intransitive subject (The door opened) occurs 36 times, and as a direct
object (I opened the door) 149 times. This is an example of P-lability. The stronger the
skew of the Verb + Noun combinations towards one or the other use, the weaker the
P-lability in a given corpus. If many rows contain the frequencies of begin + work,
the P-lability will be high. If more Verb + Noun combinations behave like die +
people, the P-lability will be low.
  If we simply counted intransitive and transitive uses of verbs, it would be
impossible to distinguish A-lability from P-lability. As will be shown below, making
this distinction is crucial, and it is why it was necessary to control for the nouns as A,
P or S.
  Note that we only selected the verbs that served as predicates of main clauses.
Particle verbs and verbs with separable prefixes were treated as one lemma (e.g.,
break+out, um+leiten). We also excluded verbs with reflexive, passive, antipassive,
middle morphology or auxiliaries because of the substantial cross-linguistic
differences in their semantics, formal properties and annotation. One consequence of
this decision is that we are primarily measuring looseness vs. non-looseness (the
formal marking of which can be quite variable across languages). We also excluded
ditransitive clauses, in which one and the same verb had dependencies labelled as
'iobj' (indirect object) and 'obj' (direct object). The measures of lability presented
below are based only on combinations of verbs and nouns that occur ten times or
more in a corpus.
  Lability measures were computed using two methods. According to the first, we
controlled for both the verb and the noun, which means that our measures took into
account not only the flexibility of the verb with regard to the alternation variants, but
also the flexibility of the noun with regard to the roles of A or S (in cases of A-lability)



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Linguistic Typology at the Crossroads 2-1 (2022): 94-120


and S or P (in cases of P-lability). In the second method, we took into account the
verbs only, adding up the frequencies of all nouns occurring as A and S, or as S and
O with a given verb. The formulas and the scores are discussed in the next section.


3. Measures of lability


3.1. Mutual Information related to A-lability


Using the kinds of frequencies shown in Table 1, we computed Mutual Information
(MI) related to A-lability for twenty-eight languages. For Verb + Noun combinations,
the formula was as follows:

                                                           & '(&*! , +," -
(5)   𝐼 (𝑉&𝑁; 𝐶𝑥) = ∑!, " 𝑝 .𝑉&𝑁! , 𝐶𝑥" 0 𝑙𝑜𝑔#          & '(&*! ) & (+," -



where V&N represents Verb + Noun combinations, and Cx stands for the
constructional alternation, which includes the transitive construction ('nsubj' + Verb
+ some object) and the intransitive construction ('nsubj' + Verb). The higher MI, the
stronger on average the associations between the Verb + Noun combinations and the
particular constructions. Therefore, high MI scores indicate weak lability,
characteristic of a tight-fit language, and low MI scores correspond to strong lability,
characteristic of a loose-fit language.
   For verbs only, the formula was as follows, where V stands for a verb:

                                                & '(! , +," -
(6)   𝐼 (𝑉; 𝐶𝑥) = ∑!, " 𝑝 .𝑉! , 𝐶𝑥" 0 𝑙𝑜𝑔#    & '(! ) & (+," -



Both types of scores are shown in Figure 1. The languages are ordered by their MI
scores based on Verb + Noun combinations, but the two types of scores are strongly
correlated: Spearman's rank-based correlation coefficient is 0.97, and the p-value<
0.0001 (but see a more precise measure with genetic dependencies taken into account
in Section 4). This means that the measures represent very similar information. The
scores based on verbs only are lower in all languages, but the ordering is more or less
the same, as the high correlation coefficient suggests. The highest scores are found in
Portuguese, followed by Italian, Hindi, English and Slovene. The lowest score belongs
to Lithuanian, followed by Vietnamese, Korean, Arabic and Persian. This ranking is



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Levshina, Hawkins                                                                Verb-argument lability


not predictable from any typological, genealogical or areal properties of the
languages.




Figure 1: Distribution of MI scores representing A-lability. The higher the score, the weaker this type
                                       of lability in a language.




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Linguistic Typology at the Crossroads 2-1 (2022): 94-120


3.2. Mutual Information related to P-lability


To compute MI related to P-lability, we used the same approach as for A-lability, but
took the frequencies of verbs and nouns in the construction 'nsubj' + Verb without
object and the construction Verb + 'obj' (regardless of the presence or absence of any
subject). The two methods, Verb + Noun (as 'nsubj' or 'obj') and Verb only, yield
scores that are highly correlated: Spearman's correlation coefficient rho is 0.96, with
the p-value < 0.001.
     Figure 2 displays both types of MI scores. The top scores belong to Hungarian,
Russian, Estonian, Latvian, Korean and Finnish. The high scores mean that these
languages have strong associations between the Verb + Noun combinations and the
constructions in which they appear as 'nsubj' or 'obj' respectively, characteristic of
tight-fit languages. These languages also have formal case marking and relatively free
word order of the core arguments. Many of the languages at the top are verb-final, or
at least allow for the V-final order. The two languages at the bottom are Indonesian
and Vietnamese, followed by English, French and Romanian. These have weaker
associations between the Verb + Noun combinations and the constructions in which
they appear as 'nsubj' or 'obj'. So they display stronger P-lability characteristic of
loose-fit languages. They also have fairly rigid SVO order and no case morphology.
     If we compare the range of values in Figure 1 and Figure 2, we see that the MI
scores related to A-lability are on average lower than the MI scores related to P-
lability. This impression is supported by paired Wilcoxon tests. The difference
between the A- and P-lability scores is significant for both methods (p = 0.028
for verbs only, and p < 0.001 for Verb + Noun combinations). This means that
languages are more tolerant with regard to A-lability in general. In addition, the
spread of the P-lability scores is greater, which suggests more substantial cross-
linguistic differences.


3.3. How variable is lability across individual verbs and arguments?


An important question is whether low MI scores, which characterize high-lability
languages, are due to most verbs being weakly associated with a specific construction,
or whether they are primarily influenced by a handful of very frequent idiosyncratic
verbs with high verb-argument lability, e.g., English break or learn.4

4
    We thank Thomas Hörberg for pointing out this important distinction to us.


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Levshina, Hawkins                                                                Verb-argument lability




Figure 2: Distribution of MI scores representing P-lability. The higher the score, the weaker this type
                                       of lability in a language.


In order to answer this question, we computed how strongly the frequencies of a Verb
+ Noun combination (or of a Verb only) deviated from the expected proportion based
on the total frequencies for the alternating constructions in the datasets. This measure
is called Kullback—Leibler Divergence (DKL) in information theory. It is also known as


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Linguistic Typology at the Crossroads 2-1 (2022): 94-120


relative entropy. The formula for a Verb + Noun combination V&N and Construction
Cx is as follows:

                                                   2 '(&*, +," -
(7)   𝐷01 (𝑃||𝑄) = ∑ " 𝑃 .𝑉&𝑁, 𝐶𝑥" 0 𝑙𝑜𝑔#          3 ((&*, +," )



where P stands for the observed proportions of the specific Verb + Noun combination
in each of the constructional variants, whereas Q represents the probability of this
constructional variant for all Verb + Noun combinations, derived from the corpus
frequencies. The higher the measure, the more this Verb + Noun combination
contributes to MI. For verbs only, the procedure is identical, but instead of the
observed and expected proportions of constructional variants in all Verb + Noun
combinations we compute the proportions of constructional variants for Verbs only.
   As a result, we obtained DKL scores for individual combinations of Verb + Noun
and for Verbs only. For example, the combinations drive + people, open + room, begin
+ work, show + video and ask + woman, had very low DKL scores for P-lability because
the Noun in these combinations occurred as S and P of the Verb with comparable
frequencies. In contrast, the combinations have + opportunity, do + job, play + role,
score + point and make + sense had very high DKL scores for P-lability because the
Noun was only used as P with these verbs.
   The average DKL scores were strongly correlated with the analogous MI scores
presented in Sections 3.1 and 3.2. When computed on the A-lability data, Pearson's
correlations were r = 0.89 (Verb + Noun combinations) and r = 0.67 (Verbs only),
both p < 0.001. As for P-lability, the correlations were even stronger: r = 0.98 for
Verb + Noun combinations and r = 0.86 for verbs, both p < 0.001. This means that
both measures reflect similar information.
   In order to estimate how strongly the individual Verbs or Verb + Noun
combinations vary, we computed standard deviations of DKL for every language. The
higher the standard deviations, the more variability there is between the Verb +
Noun combinations or individual verbs with regard to their association to one or the
other construction.
   Figures 3a and 3b display the MI scores and the standard deviations of the DKL scores
for A-lability. Figure 3a shows the scores for Verb + Noun combinations, and Figure
3b displays the scores for Verbs only. There are no obvious correlations between the
measures in either plot.




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Levshina, Hawkins                                                                Verb-argument lability


Now let us turn to P-lability. Figure 4a shows the MI scores and the standard
deviations of the DKL scores for Verb + Noun combinations. There is a clear negative
correlation: lower MI (and higher P-lability) scores correspond to higher DKL, although
there is also quite a lot of variability in the middle of the plot. Figure 4b displays the
same data, but for Verbs only. In this case, the negative correlation is even more
obvious.
  How can we interpret these results? The looser languages on the left-hand side of
the plot are loose due to certain individual Verb + Noun combinations which are
highly labile, while other verbs are less or not labile. By contrast, if a language is
generally tight, as are the languages on the right-hand side, the individual verbs in
such a language are quite uniformly tight.




    Figure 3a: MI scores representing A-lability (horizontal axis) and the standard deviations of the
DKL scores for individual Verb + Noun combinations (vertical axis). The curve is based on the LOESS
                                         smoothing method.




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Linguistic Typology at the Crossroads 2-1 (2022): 94-120




 Figure 3b: MI scores representing A-lability (horizontal axis) and the standard deviations of the DKL
   scores for individual Verbs (vertical axis). The curve is based on the LOESS smoothing method.




 Figure 4a: MI scores representing P-lability (horizontal axis) and the standard deviations of the DKL
  scores for individual Verb + Noun combinations (vertical axis). The curve is based on the LOESS
                                          smoothing method.



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Levshina, Hawkins                                                                Verb-argument lability




 Figure 4b: MI scores representing P-lability (horizontal axis) and the standard deviations of the DKL
   scores for individual Verbs (vertical axis). The curve is based on the LOESS smoothing method.


4. Correlations with other typological parameters


In this section we test for correlations between the A-lability and P-lability scores
based on Mutual Information and the following parameters: rigidity of Subject and
Object order; position of the lexical verb in the clause; case marking; and associations
between lexemes and grammatical roles, which serves as a proxy for semantic
tightness. We recycle the data from Levshina (2021), where the parameters were
estimated by using the same online news corpora. More specifically, rigidity of
Subject and Object order was computed as 1 minus entropy of SO and OS orders. To
compute entropy, we used Shannon's (1948) famous formula, as shown below:


(8)   𝐻(𝑋) = − (𝑃(𝑆𝑂) 𝑙𝑜𝑔# 𝑃(𝑆𝑂) + 𝑃(𝑂𝑆) 𝑙𝑜𝑔# 𝑃(𝑂𝑆))


The proportions of SO and OS orders in transitive clauses were computed first based
on the corpora, and then these entropy scores were computed (see Levshina 2019). If
the proportions of SO and OS orders are equal (0.5), this leads to an entropy score of
1. If only one of the orders is used (either SO or OS), this leads to zero entropy. Since



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entropy represents word order variability, we subtracted the entropy scores from 1 in
order to obtain measures of word order rigidity. Lithuanian, Hungarian, Latvian,
Czech and Estonian had the lowest scores and therefore the most variable orders, and
Indonesian, French, English, Danish and Swedish had the highest scores and thus the
most rigid orders. Note that in all languages, the SO order was the more frequent one.
So we can speak about the rigidity of SO order. This variable was called 'Rigid Order
(SO)'.
   Another measure was the proportion of main clauses with a lexical verb between
the Subject and Object. As expected, it was near-zero in verb-final languages, such as
Japanese, Korean, Persian and Hindi, and close to one in Indonesian, English, French,
Vietnamese and Portuguese. This variable was labelled 'Verb between Subj and Obj'.
   We also took into account how much case marking was present to help in
identifying the Subject and the Object. In Levshina (2021), the scores represented
Mutual Information between case and the corresponding grammatical roles (A and
P). For languages with adpositional case marking, the data were extracted
automatically. As an illustration, consider the frequencies for Spanish in Table 3.


                  Case                   Transitive Subject          Direct Object
                  Zero marking           126,736                     569,252
                  Preposition a          0                           55,442


    Table 3: Frequencies of zero case marking and the direct object marker a for Subject and Object
                                              in Spanish.


For languages with case morphology, random samples were drawn and analyzed
manually in order to take account of case syncretism. Next, the counts were
extrapolated to all occurrences of Subjects and Objects in transitive clauses in a
corpus. Consider an illustration in Table 4, which contains frequencies for Russian. In
languages with distinct forms for Subject and Object and also forms with case
syncretism, as in Russian, these three situations were represented by separate rows.


         Case                                               Transitive Subject       Direct Object
         Nominative                                         47,521                   0
         Accusative                                         0                        93,520
         Nominative/Accusative (case syncretism)            42,884                   246,361

         Table 4: Frequencies of Nominative, Accusative and case syncretism forms in Russian



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Levshina, Hawkins                                                    Verb-argument lability


German was a special case, where all feminine, neuter and plural forms were treated
as ambiguous, since their Nominative and Accusative forms are formally
indistinguishable, whereas masculine nouns were analyzed as Nominative or
Accusative only in the presence of determiners or adjectives, which normally carry
the distinct marking in combination with the noun. See more details about the
procedure in Levshina (2021).
  Based on numbers like those displayed in Tables 3 and 4, we computed the Mutual
Information between cases (C) and grammatical roles (R) for each language, using the
formula in (9).

                                          & '+! , 4" -
(9)   𝐼 (𝐶; 𝑅) = ∑!, " 𝑝 .𝐶, 𝑅" 0 𝑙𝑜𝑔#   & '+) & (4" -



The higher the Mutual Information, the more strongly the case forms are associated
with the grammatical roles in question. Languages with zero scores had no case
marking on Subject and Object (Danish, Dutch, English, Indonesian, Swedish and
Vietnamese). Languages with the highest scores were those with rich morphological
case marking (Lithuanian, Hungarian, Latvian, Estonian and Japanese). Languages
with some type of differential, lexically restricted or optional marking were in-
between (the Slavic languages, Hindi, Korean, German, Persian and Turkish). The
variable with these scores was called 'Case Marking'.
  Finally, we took the Mutual Information between Nouns only and the grammatical
role of Subject and Object as a proxy for semantic tightness. If the proportions with
which a Noun is found as a transitive Subject and Object are similar to the baseline
proportions of Subject and Object, this contributes to the semantic looseness of a
language. If a Noun is strongly biased towards one of these roles, this increases its
semantic tightness (see Levshina 2021 for more details). The higher the Mutual
Information, the tighter the language. The languages with the highest scores were
Hindi, Korean, Russian, Hungarian and Japanese. They are known as tight-fit
languages in the literature with regard to the relationships between arguments and
their semantics. Indonesian had the lowest score, followed by English and Spanish.
These were the loosest languages in our sample. This tightness measure was labelled
as 'MI Nouns'.
  The correlation analyses were based on Spearman's rank-based correlations. In
order to control for the genealogical dependencies in our data (i.e., the fact that many
languages come from one and the same genus), we used a sampling procedure


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Linguistic Typology at the Crossroads 2-1 (2022): 94-120


whereby we created 1,000 samples. For every sample, we drew randomly only one
language per genus and computed the correlation coefficient (ρ). For null hypothesis
significance testing, the test statistic was first computed and logged for the original
pairs of scores in every simulation. Next, we also ran 1,000 permutations, in which
the original scores of the second variable were randomly reshuffled. The permutation
scores helped us to capture the distribution of the test statistic under the null
hypothesis. Next, we counted the number of cases out of 1,000 permutations where
the permuted scores were equal to or more extreme than the original test. After we
had these data for all samples, we averaged the coefficients and the p-values.
   Figure 5 represents the correlation coefficients between the parameters, with the
p-values being shown in Figure 6. One can see in Figure 5 that both types of P-lability
scores (Verbs and Verb + Noun) are correlated with the other typological parameters.




 Figure 5: Correlations between the typological parameters and lability scores. The colour intensity
represents the strength of the correlation. Blue cells stand for positive correlations. Red cells display
                                         negative correlations.




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Levshina, Hawkins                                                            Verb-argument lability




                    Figure 6: Average p-values based on resampling and permutation


The correlation between both types of MI scores for P-lability and case marking is
strong and positive. This means that languages with systematic case marking have
low P-lability. There is also a positive correlation between MI related to P-lability and
MI based on Nouns only. At the same time, P-lability scores are negatively correlated
with verb-medialness and word order rigidity. This means, in turn, that languages
with SVO and rigid SO order have more P-lability. Judging from the magnitude of the
coefficients, we can also see that the P-lability scores based on verbs only are overall
less strongly correlated with the other typological parameters than the P-lability
scores based on Verb + Noun combinations.
  We also observe significant negative correlations between rigid SO order and case
marking, and between verb-medialness and case marking. In addition, there is a
strong and significant negative correlation between Mutual Information based on
associations between nouns and grammatical roles, and verb-medialness.
  The A-lability scores do not participate in any significant correlations, however.
They are only strongly correlated between themselves. This means that we do not find
evidence that A-lability is correlated with any of the typological parameters.


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Linguistic Typology at the Crossroads 2-1 (2022): 94-120


5. Conclusions


Our quantitative analyses reveal that P-lability scores are systematically correlated
with the other parameters related to tight and loose fit. Languages with low P-lability
(and high MI scores) tend to have case marking, stronger associations between nouns
and grammatical roles, relatively flexible order of Subject and Object, and verb-
final order. These features are associated with tight-fit languages. In contrast,
languages with high P-lability (and low MI scores) tend to have little or no case
marking, quite rigid SVO order, and weaker associations between nouns and
grammatical roles. These features are associated with loose-fit languages. Therefore,
our data support Hawkins' (1986, 1995) observation that verbs in loose-fit languages
are used in more diverse subcategorization frames, while the argument co-
occurrences of verbs in tight-fit languages are more constrained. More generally, our
analysis provides further evidence that the classification of languages into loose- and
tight-fit is a theoretically useful one.
   In order to explain why these and other grammatical features of this general
typology correlate in the ways they do Hawkins (1995, 2014, 2019) appealed to
language processing, and especially to the crucial role of the verb in online processing.
At issue is: does the verb precede its co-occurring arguments (as in VSO and SVO
languages), or do these latter precede the verb (i.e. SOV)? In the former case there is
early activation in processing of the verb's co-occurrence possibilities (with temporary
ambiguities and possible garden paths when parsing English-type languages as the
intended frame is gradually selected). In SOV languages, co-occurrence frame
activation and selection based on preceding material will be almost instantaneous.
   Hawkins (1995, 2014, 2019) formulated predictions for correlating properties
based on this difference. Languages with verb-final structures (like Japanese and
Korean) should exhibit what he called greater 'predicate frame differentiation' and
'argument differentiation'. Predicate frame differentiation refers to the degree to
which a verb is distinctive from others by virtue of its unique selectional restrictions
or syntactic co-occurrence possibilities. A verb that is uniquely transitive is more
differentiated, and less labile in the terminology of the present paper, than one that
is ambiguously transitive or intransitive. A verb that selects restricted direct objects
for 'putting on clothing' according to the body part and the type of clothing in question
(hats on the head, a coat over the rest of the body, etc.) is more differentiated than
one (like English put on) that is compatible with many different types of body parts



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Levshina, Hawkins                                                     Verb-argument lability


and clothing activities (Planck 1984, Hawkins 1986). Argument differentiation refers
to the degree to which arguments are, e.g., differentially case-marked versus
ambiguous as to case, and the degree to which they are assigned a narrow set of
thematic roles like Agent and Patient rather than the broader set of English (which
permits e.g., Locative and Instrumental subjects as in This tent sleeps four people and
The key opened the door).
  When the verb is the last constituent in the clause and the very next item to be
parsed belongs in an altogether different clause, the parser must succeed instantly in
selecting the correct frame and its arguments. The grammar and lexicon must,
Hawkins hypothesized, help the parser of a verb-final language by ensuring that
predicate frame selection is immediately successful. More differentiated predicate
frames and arguments can accomplish this, and in a number of observable ways.
   First, subcategorization restrictions can be made tighter in SOV languages. For
example, it is desiderable to avoid transitive/intransitive ambiguities with the result
that arguments can be paired with their predicates more uniquely and more easily
and with less variability and lability. Second, additional selectional restrictions can
be imposed so that certain verb-NP pairings are more constrained, frequently co-
occurring and easily recognizable. Third, subjects and objects can be made less
semantically diverse in SOV languages with the result that there are fewer co-
occurrence possibilities to choose from and more constrained assignments of thematic
roles to NPs (transitive subjects are agents, transitive objects are patients, etc.). We
expect fewer assignments of thematic roles to a transitive subject such as Location
and Instrument in these languages. Fourth, surface coding devices can be
grammaticalized for arguments that permit immediate thematic role recognition. This
is what case marking generally does. It constrains the thematic roles that can be
assigned to surface NPs, making them less semantically diverse (and also making them
less labile) compared with case-less languages. This has the dual advantage of making
thematic role information available early on-line, prior to the verb, and of facilitating
argument-predicate assignments once the verb is encountered. And indeed it has long
been known that there is a strong correlation between SOV and case marking, as
confirmed in the correlations of the present paper.
   More generally we expect to see verb-final languages with a more constrained set
of verb co-occurrence possibilities. We expect more predicate frame differentiation
and more argument differentiation, but less 'argument trespassing', which Hawkins
(1995, 2014, 2019) defined to mean less movement of NP arguments into clauses in



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Linguistic Typology at the Crossroads 2-1 (2022): 94-120


which they contract no semantic relations with their most immediate predicates. For
verb-early (SVO, VSO, VOS) languages, however, no such constraints are predicted.
These languages do not need to conventionalize devices that permit immediate and
correct predicate frame recognition at the verb, because the parser still has the
remainder of the clause in which to complete its predicate frame selection and
argument recognition, and because aspects of the verb’s processing (e.g. its precise
interpretation and disambiguation) that depend on subsequent arguments cannot be
identified at the verb anyway. As a consequence, the need for immediate and correct
decision-making at the verb, and the resulting need for clear predicate-frame
differentiation, argument differentiation, and for local argument-predicate matching
will impose much weaker requirements on the grammars and lexicons of such
languages. This is why verb-early languages are quite variable: Indonesian (SVO)
reveals many similarities with English, for example, whereas Hebrew (SVO) has far
fewer (Müller-Gotama 1994). Verb-final languages should be more constrained in
these respects, therefore, whereas languages with earlier verbs in the clause are
predicted to be more variable. Verb-final languages are accordingly generally tight-
fit, whereas verb-early and verb-medial languages can be tighter (like Hebrew) or
looser (like English), as we have seen in this paper.
   What ultimately underlies the tight vs loose typology, as explained in Section 1, is
the mapping between forms and meanings. Tight-fit languages have richer, more
complex and more unique surface forms that map onto less ambiguous and more
constrained meanings, i.e. there is more of a one-to-one correspondence between form
and meaning. This simplifies the mapping between them, but at the expense of
processing more complex forms, for example case-marked nouns. Loose fit languages
involve simpler processing of their more minimal and semantically general forms like
caseless nouns, but they require more complex contextual processing and
disambiguation of meaning assignments through 'word-external properties', see
Hawkins (2019). This trade-off can be measured and made more precise in terms of
the different MI and lability scores that we have seen in this paper for different
languages, in the assignments of different nouns to grammatical roles, and in the
further correlations with case marking, SOV, verb-mediality and rigid order. For
further exploration of the processing basis for these correlations in terms of competing
efficiencies and the general theory of efficiency in processing (as laid out in Gibson
et al. 2019; and Levshina 2022), and for suggestions for psycholinguistic experiments




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Levshina, Hawkins                                                    Verb-argument lability


that need to be conducted on different language types from this perspective, the
reader is referred to Hawkins (2019).
  We have found further that P-lability scores based on verbs only are overall less
strongly correlated with the other typological parameters than the P-lability scores
based on Verb + Noun combinations. This is not surprising, because these latter
scores also include the attraction of nouns to different grammatical roles. At the same
time, both of these scores are more strongly correlated with word order rigidity and
case marking than semantic tightness scores based on nouns only, but the latter has
a stronger correlation with verb-final order (since the languages in the sample, except
for Arabic, are either verb-medial, or verb-final). This may have to do with the fact
that the attraction of nouns to one or the other role helps to avoid costly reanalysis
when the verb comes last. Whether or not the verb has special marking depending on
the roles of its arguments is less important for that purpose.
  It is remarkable that our P-lability scores are more strongly correlated with the
other typological parameters than the latter are among themselves. This is an
unexpected finding, but it can be explained by the fact that P-lability scores convey
information not only about the verbs, but also (explicitly or implicitly) about the
nouns in different roles. These scores can thus be a useful indicator of the word-
external or word-internal orientation of the language in question (Hawkins 2019).
  We should also mention that high P-lability for a language in general seems to be
due to some highly labile verbs, which behave 'promiscuously', appearing in both
constructional frames. At the same time, there are also lexemes with low promiscuity.
In contrast, the verbs or combinations of verbs and nouns in tight languages with low
P-lability tend to be uniformly faithful to one or the other constructional frame. These
conclusions are based on our analysis of lexical variability based on Kullback—Leibler
Divergence scores (also known as relative entropy).
  In contrast to P-lability, the A-lability scores are not correlated with any of the
typological properties we have examined here. A-lability is also found more
frequently in our corpora than P-lability, as we see from the lower MI values in the
former. A possible explanation for this is that A-lability is often driven by general
pragmatic factors. For example, the object can be omitted due to its high accessibility,
e.g., And Italy wins [the final]! Many objects are omitted due to specific
conventionalized inferences, e.g., He drinks again [liquor]. Object omission is also
possible if the focus is on the action, while the object has low discourse prominence,
e.g., She chopped and chopped [e.g., meat] (Goldberg 2005). Other reasons are cultural.



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Linguistic Typology at the Crossroads 2-1 (2022): 94-120


For example, the object can be omitted when it is taboo, e.g., Pat sneezed [mucus] onto
the computer screen, or for feelings of tact, I contributed [$1,000] to UNICEF (Goldberg
2005). In addition, many rules allowing for object omission are also lexically and
semantically specific (Fillmore 1986). All these pragmatic factors and lexical
idiosyncrasies explain the lack of systematic correlations between A-lability and the
other typological properties of the languages in question. It would be very interesting
to investigate if there exist cross-linguistic regularities at the level of individual verbs
and verb classes, and if so which semantic roles alternate (cf. Letuchiy 2009). As
mentioned above, it would not be realistic to annotate manually all verbs for their
semantics in the twenty-eight languages, so we have to leave this question for future
research.
   We also hope to have demonstrated that information-theoretic measures, such as
Mutual Information, entropy and Kullback-Leibler Divergence, can be fruitfully used
for language comparison. As initiatives such as the Universal Dependencies progress
and typologically diverse large corpora are made available it becomes increasingly
possible to infer typological variables directly from texts and to build on, and refine,
earlier typological patterns derived largely from grammars, as in the present study.
We hope that new corpora and tools will allow for further testing of the findings of
this study using a larger and more diverse sample of languages and genres. A further
question is then whether there are causal relationships between the parameters
themselves, and what they would look like exactly. A causal analysis in Levshina
(2021) showed that case marking is more likely to be affected by other typological
parameters (word order and associations between lexemes and syntactic roles) than
the other way round. We need a larger sample of languages in order to answer this
question and to test all possible causal links. It would also be interesting to add the
other parameters of Hawkins (1995, 2014, 2019), such as the frequency of long-
distance syntactic dependencies or of categorial ambiguity, and to test their
relationships with the parameters examined here.


Data availability statement
The dataset with the Mutual Information and other values for every language in the
sample is available as supplementary materials on the journal website.




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Levshina, Hawkins                                                     Verb-argument lability


Acknowledgements
The first co-author's work on this paper was funded by the Netherlands Organisation
for Scientific Research (NWO) under Gravitation grant "Language in Interaction",
grant number 024.001.006. We thank the reviewers for their insightful and
constructive feedback, which has helped us improve the paper. All remaining errors
are only ours.


References


Aissen, Judith. 2003. Differential object marking: Iconicity vs. economy. Natural
  Language and Linguistic Theory 21. 435-483.
Creissels, Denis. 2014. P-lability and radical P-alignment. Linguistics 52(4). 911-944.
Dahl, Östen. 2000. Egophoricity in discourse and syntax. Functions of Language 7(1).
  37-77.
Dixon, R.M.W. 1994. Ergativity. Cambridge: Cambridge University Press.
Fillmore, Charles J. 1986. Pragmatically Controlled Zero Anaphora. Proceedings of the
  Berkeley Linguistics Society 12. 95-107.
Gibson, Edward, Richard Futrell, Steven T. Piantadosi, Isabelle Dautriche, Kyle
  Mahowald, Leon Bergen, & Richard Levy. 2019. How efficiency shapes human
  language. Trends In Cognitive Sciences 23(5). 389-407.
Goldberg, Adele E. 2005. Argument realization: the role of constructions, lexical
  semantics and discourse factors. In Jan-Ola Östman & Miriam Fried (eds.),
  Construction Grammars: Cognitive grounding and theoretical extensions, 17-44.
  Amsterdam: John Benjamins.
Goldhahn, Dirk, Thomas Eckart & Uwe Quasthoff. 2012. Building large monolingual
  dictionaries at the Leipzig Corpora Collection: From 100 to 200 languages. In
  Nicoletta Calzolari, Khalid Choukri, Thierry Declerck et al. (eds.), Proceedings of the
  Eighth International Conference on Language Resources and Evaluation, 759-765.
  Istanbul: ELRA.
Greenberg, Joseph H. 1963. Some universals of grammar with particular reference to
  the order of meaningful elements. In Joseph H. Greenberg (ed.), Universals of
  human language, 73–113. Cambridge, Mass: MIT Press.
Haspelmath, Martin. 1993. More on the typology of inchoative/causative
  alternations. In Bernard Comrie & Maria Polinsky (eds.), Causatives and transitivity,
  87–120. Amsterdam: John Benjamins.



                                                                                        118
Linguistic Typology at the Crossroads 2-1 (2022): 94-120


Hawkins, John A. 1986. A Comparative Typology of English and German: Unifying the
   Contrasts. London: Croom Helm.
Hawkins, John A. 1995. Argument-predicate structure in grammar and performance:
   A comparison of English and German. In Irmengard Rauch & Gerald F. Carr (eds.),
   Insights in Germanic Linguistics, Vol. 1 Methodology in Transition, 127-44. Berlin:
   Mouton de Gruyter.
Hawkins, John A. 2012. The drift of English towards invariable word order from a
   typological and Germanic perspective. In Terttu Nevalainen & Elizabeth C.
   Traugott (eds.), The Oxford Handbook of the History of English, 622-632. Oxford:
   Oxford University Press.
Hawkins, John A. 2014. Cross-linguistic Variation and Efficiency. Oxford: Oxford
   University Press.
Hawkins, John A. 2019. Word external properties in a typology of Modern English: A
   comparison with German. English Language and Linguistics 23(3). 701-723.
Letuchiy, Alexander. 2009. Towards a typology of labile verbs: Lability vs. derivation.
   In Patience Epps & Alexandre Arkhipov (eds.), New Challenges in Typology:
   Transcending the Borders and Refining the Distinctions, 247-268. Berlin / New York:
   De Gruyter Mouton.
Levin, Beth. 1993. English Verb Classes and Alternations: A Preliminary Investigation.
   Chicago: University of Chicago Press.
Levshina, Natalia. 2019. Token-based typology and word order entropy. Linguistic
   Typology 23(3). 533-572.
Levshina, Natalia. 2020. How tight is your language? A semantic typology based on
   Mutual Information. In Proceedings of the 19th International Workshop on Treebanks
   and Linguistic Theories, 70-78. Düsseldorf: ACL.
Levshina, Natalia. 2021. Cross-linguistic trade-offs and causal relationships between
   cues to grammatical subject and object, and the problem of efficiency-related
   explanations. Frontiers in Psychology 12. 648200.
Levshina, Natalia. 2022. Communicative Efficiency: Language Structure and Use.
   Cambridge: Cambridge University Press.
Müller-Gotama, Franz. 1994. Grammatical Relations: A Cross-Linguistic Perspective on
   Their Syntax and Semantics. Berlin: Mouton de Gruyter.
Nichols, Johanna. 1986. On form and content in typology. In Winfred P. Lehmann
   (ed.), Language Typology 1985: Papers from the Linguistic Typology Symposium,
   Moscow, 9-13 Dec. 1985, 141 - 162. Amsterdam: John Benjamins.



                                                                                   119
Levshina, Hawkins                                               Verb-argument lability


Nichols, Joanna, David A. Peterson, & Jonathon Barnes. 2004. Transitivizing and
  detransitivizing languages. Linguistic Typology 8. 149-211.
Plank, Frans. 1984. Verbs and objects in semantic agreement: Minor differences
  between English and German that might suggest a major one. Journal of Semantics
  3(4). 305-360.
Shannon, Claude E. 1948. A Mathematical Theory of Communication. Bell System
  Technical Journal 27. 379-423, 623-656.
Wijffels, Jan, Milon Straka & Jana Straková. 2018. udpipe: Tokenization, parts of
  speech tagging, lemmatization and dependency parsing with the UDPipe NLP
  Toolkit. R package version 0.7.


Corpora
Leipzig Corpora Collection
  https://wortschatz.uni-leipzig.de/en/download



CONTACT
natalia.levshina@mpi.nl




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