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  <front>
    <journal-meta>
      <journal-title-group>
        <journal-title>September</journal-title>
      </journal-title-group>
    </journal-meta>
    <article-meta>
      <title-group>
        <article-title>Modelling Italian construction flexibility with distributional semantics: Are constructions enough?</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Lucia Busso Ludovica Pannitto Alessandro Lenci</string-name>
          <email>alessandro.lenci@unipi.it</email>
          <email>ellepannitto@gmail.com</email>
          <email>lucia.busso90@gmail.com</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>CoLing Lab, University of Pisa</institution>
        </aff>
      </contrib-group>
      <pub-date>
        <year>2017</year>
      </pub-date>
      <volume>1</volume>
      <fpage>8</fpage>
      <lpage>20</lpage>
      <abstract>
        <p>English. The present study combines psycholinguistic evidence on Italian valency coercion and a distributional analysis. The paper suggests that distributional properties can provide useful insights on how general abstract constructions influence the resolution of coercion effects. However, complete understanding of the processing and recognition of coercion requires to take into consideration the complex intertwining of lexical verb and abstract constructions.</p>
      </abstract>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>1 Introduction</title>
      <p>
        In Construction Grammar
        <xref ref-type="bibr" rid="ref14">(Goldberg, 2006)</xref>
        , the
basic units of linguistic analysis are called
constructions (Cxns), form-meaning pairings
associated with autonomous, non-compositional abstract
meanings, independently from the lexical items
occurring in them. Examples of Cxns range from
morphemes (e.g., pre-, -ing), to filled or
partiallyfilled complex words (e.g., daredevil) to idioms
(e.g., give the devil his dues) to more abstract
patterns like the Ditransitive [Subj V Obj1 Obj2]
(e.g., he gave her a book)
        <xref ref-type="bibr" rid="ref14">(Goldberg, 2006)</xref>
        .
      </p>
      <p>Cxns appear at any level of linguistic analysis,
but the level at which the notion of constructional
meaning represents a radical departure from other
theories of grammar is argument structure. These
Cxns, such as the English Ditransitive, are claimed
to be associated with an abstract semantic content.
In this case, constructional meaning can be
paraphrased as X CAUSES Y TO RECEIVE Z. One of
the main supporting arguments in favour of
constructions as independent and primitive objects of
grammar is the flexibility with which argument
Cxns and verbs interact with each other, as in
example (1) in which the original intransitive sense
of “to sneeze” is overridden by the Caused Motion
Cxn, and thus takes a transitive sense of “making
something move by sneezing”.</p>
      <p>
        (1) John sneezed the napkin off the table
This flexibility in combining Cxns and verbs
is known as valency coercion
        <xref ref-type="bibr" rid="ref20 ref24 ref27 ref6 ref7 ref7">(Michaelis, 2004;
Boas, 2011; Lauwers and Willems, 2011; Perek
and Hilpert, 2014)</xref>
        .
      </p>
      <p>This phenomenon, although vastly addressed
for English, has not yet received a systematic
investigation in other languages. For notable
exceptions, see Boas and Gonzálvez-García (2014). In
particular – to the best of our knowledge – no
previous attempt to carry out an empirical
investigation of valency coercion exists for Italian.
However, even a simple corpus query reveals that the
phenomenon is present in Italian, though it is not
as pervasive as in English:
(2) Tossì una risata leggera tra i suoi capelli
(He coughed a light laugh in her hair)
[ItWac]
This paper presents an analysis of Italian
constructional flexibility that combines psycholinguistic
and computational evidence: first, we present the
results of a behavioral experiment on valency
coercion. Then, we model Cxns with distributional
semantics to investigate whether the semantic shape
of Italian argument Cxns can affect the
interpretation and processing of coerced sentences.</p>
    </sec>
    <sec id="sec-2">
      <title>Studying valency coercion: an acceptability rating task</title>
    </sec>
    <sec id="sec-3">
      <title>MATERIALS AND SUBJECTS: The offline</title>
      <p>
        psycholinguistic experiment targets 9 Italian Cxns
(see Table 1) that were selected using existing
resources: LexIt
        <xref ref-type="bibr" rid="ref22">(Lenci et al., 2012)</xref>
        and
ValPal
        <xref ref-type="bibr" rid="ref11 ref16">(Cennamo and Fabrizio, 2013)</xref>
        . The resultant
Cxns are of varying abstractness and schematicity
levels (Barðdal, 2008).
      </p>
      <p>Cxn
CAUSED MOTION (CM)
CAUSED MOTION + via (CMvia)
DATIVE (DT)
INTRANSITIVE MOTION (IM)
PASSIVE (PASS)
PREDICATIVE (PRED)
VERBA DICENDI explicit
(sentential) (VDE)
VERBA DICENDI implicit
(sentential) (VDI)
frames
NPj-V-NP -PPlocation</p>
      <p>NPs-V-NPobj
NPs-V-NPj-PPrecipient</p>
      <p>NPs-V–PPlocation</p>
      <p>NPs-V-PP
NPs-V–AdjPpredicate</p>
      <p>NPs-V-cheVP</p>
      <p>NP-V-diVP</p>
      <p>For each Cxn, we built 21 sentences, which
were subdivided into 3 experimental conditions:
GRAMMATICAL (3a), COERCION (3b),
IMPOSSIBLE (3c) (7 sentences per condition). The total
number of stimuli amounts to 189 sentences. The
structure of the test was inspired by Perek and
Hilpert (2014). Between conditions, sentences
differ only for their main verb, to have as little
variation as possible.</p>
      <p>
        (3)
a. Gianni ha detto che verrà domani
(Gianni said that he will come tomorrow)
b. Gianni ha fischiettato che verrà
domani (Gianni whistled that he will
come tomorrow)
c. Gianni ha cucinato che verrà domani
(Gianni cooked that he will come
tomorrow)
The coercion condition consists of verbs that
display a partial semantic incompatibility with the
constructional environment they are embedded in.
They were selected by means of both native
intuition and corpus query, selecting and refining cases
that were either hapax or rare occurrences in the
Italian corpus ItWac
        <xref ref-type="bibr" rid="ref4">(Baroni et al., 2009)</xref>
        .
      </p>
      <p>
        120 Italian native speakers were tested: 39
adolescents (12-14 years old), 40 young adults
(1835 years old), and 41 adults (over 40). We tested
subjects of different ages following extensive
sociolinguistic literature that has shown that
language use changes with age
        <xref ref-type="bibr" rid="ref12 ref19 ref30">(Eckert, 2017; Labov,
2001; Wagner, 2012)</xref>
        . Thus, it could be the case
that grammaticality judgments on creative,
nonstandard sentences are also affected by age.
Including different age groups in our analysis allows
us to investigate a more representative sample of
the population. To control for the possible
influencing factor of education level, we only tested
adult speakers either in possess of (at least) a
bachelor degree or enrolled in a University course.
Table 2 summarizes number, age groups and
distribution of tested subjects.
      </p>
      <p>Age group</p>
      <p>Age range distribution Gender
Tot
Adolescents</p>
      <p>12-14
Young Adults 18-39
Adults</p>
      <p>Over 40
mean: 12.9 24 m (61,5%) 39
sd:0.63 15 f (38,4%)
mean:27.3 15 m (37,5%) 41
sd:2.94 25 f (62,5%)
mean: 56.7 18 m (43,9%) 40
sd:9.48 23 f (56,1%)</p>
      <p>A within-subject design was used, in which
each subject sees all stimuli. Participants were
asked to judge the acceptability of the
(randomized) stimuli on a Likert scale from 1 -
“completely unnatural” - to 7 - “perfectly natural”.
Presentation of the data varied across age groups:
adolescents were given the test directly in their
class. Young adults’ judgments were collected
through the online platform Figure Eight. Older
adults, instead, were presented with a simple
Microsoft Word document, in order to include
participants who did not have familiarity with online
data gathering.</p>
      <p>
        RESULTS: We assessed statistical significance
via linear mixed effect modelling, with by-subject
and by-item intercepts.1 Results show that
coercion sentences (purple boxplot in Figure 1) are
recognized as an intermediate condition between
complete grammaticality and total
ungrammaticality.2 We consider this result to support the
claim that coercion effects include a degree of
semantic incompatibility that is nonetheless
resolved in the interpretation process. Consistently
1model selection performed automatically via LRT with
the R package afex. Models were performed with the R
package lmerTest and R2 values were calculated with the MuMIn
package
        <xref ref-type="bibr" rid="ref18 ref5">(Singmann et al., 2016; Kuznetsova et al., 2017;
Barton´, 2013)</xref>
        2p &lt; 0.0001, R2c 0.61
with the main tenets of Construction Grammar, we
argue that the resolution of such incompatibility
is driven by a dynamic interaction between the
main verb and the constructional context
        <xref ref-type="bibr" rid="ref11 ref16 ref17 ref31">(Kemmer, 2008; Kemmer and Yoon, 2013; Yoon, 2016)</xref>
        .
In a second analysis, we wanted to assess the effect
of Cxn types on acceptability ratings. We used
linear mixed effect modelling, adding an interaction
between Cxn type and experimental condition.3
Results indicate high variability in Cxn
‘coercibility’ (see Figure 2 and table 3). That is, some Cxns
in our dataset were consistently judged as more
natural by speakers in the coercion condition.
      </p>
      <p>In particular, it appears that IM, VDE and VDI
Cxns result to be more natural, while DT, PASS
and (marginally) CO are the least naturally
perceived ones in coercion sentences. Since
coercion effects are said to be resolved by the
general Cxn semantics overriding the lexical
meaning of the verb, we hypothesize that the different
flexibility degrees of the Cxns in the first
experiment could be at least partially explained by
distributional properties, such as type and token
frequency, and semantic density of the Cxns in our
dataset, the latter again estimated with
distributional semantics.</p>
      <p>
        Different degrees of flexibility could derive
either from cognitive processes that reflect on
language use, or emerge from repeated exposure and
thus entrench in speakers’ grammar. Both possible
directions of this causal circle, however, ultimately
allow us to fruitfully investigate construction
flexibility using distributional semantics models. In
other words, the higher ‘coercibility’ of novel
instances of some Cxns could be due to speakers’
sensitivity to distributional semantic features of
the constructions
        <xref ref-type="bibr" rid="ref1 ref18 ref21 ref26 ref29 ref3 ref32 ref9">(Barddal, 2006; Bybee, 2013;
Zeschel, 2012; Perek and Goldberg, 2017)</xref>
        .
3
      </p>
    </sec>
    <sec id="sec-4">
      <title>A Distributional Semantic Model for argument constructions</title>
      <p>
        PROCEDURE: Perek (2016) has shown that
distributional semantics
        <xref ref-type="bibr" rid="ref23">(Lenci, 2018)</xref>
        can be
fruitfully used to model the semantic space covered by
a Cxn. It has been argued in the literature that
constructional meanings for argument Cxns arise from
the meaning of high frequency verbs that co-occur
with them
        <xref ref-type="bibr" rid="ref1 ref10 ref13 ref18 ref21 ref26 ref29">(Goldberg, 1999; Casenhiser and
Goldberg, 2005; Barak and Goldberg, 2017)</xref>
        .
Therefore, we modelled the semantic content of Cxns
with the semantics of their most typical verb, each
represented as a distributional vector.
      </p>
      <p>
        We used the UDLex Pipeline4
        <xref ref-type="bibr" rid="ref29">(Rambelli et al.,
2017)</xref>
        to obtain a mapping between the Cxns of
our dataset and the most frequent verbs that occur
in them (these were selected considering verbs that
appear at least 5 times in the relevant
subcatego4The UDLex Italian dataset consist of 409,127 tokens.
rization frames). Table 4 summarizes the number
of verbs considered for each of the eight Cxns.5
Then, we built a Distributional Semantic Model
(DSM) from the italian corpus itWac
        <xref ref-type="bibr" rid="ref4">(Baroni et
al., 2009)</xref>
        in order to represent verb meaning of the
verbs obtained with UDLex. The 300-dimensional
vectors (i.e., the embeddings) were created with
the SGNS algorithm
        <xref ref-type="bibr" rid="ref25">(Mikolov et al., 2013)</xref>
        , using
the most frequent 30,000 words as context, with a
minimum frequency of 100.
      </p>
      <p>Cxn
CM
CO
DT
IM
PASS
PRED
VD_E
VD_I</p>
      <p>Following Lebani and Lenci (2017), we
represented each Cxn as the weighted centroid vector
of its typical verbs, as follows:</p>
      <p>CX!N = 1
jV j</p>
      <p>X v 2 V f rel(v; Cxn) ~v
(1)
where V the set of the top-associated verbs v with
Cxn and f rel(v; Cxn) is the co-occurrence
frequency of a verb in a Cxn.</p>
      <p>We measured the pairwise cosine similarity
among the weighted Cxn vectors: as shown in
Figure 3, the distributional behaviour of the Cxn
vectors suggests that some Cxns in our dataset show
similar distributional behaviour.
5the Cxn CMvia was excluded due to the absence of
corresponding subcategorization frames</p>
      <p>Following Perek (2016), the semantic density of
a Cxn is computed as the mean value of pairwise
cosines between the verbs occurring in Cxn.
Figure 4 plots the semantic densities of our Cxns.</p>
      <p>Finally, to assess the effect of distributional
properties on Cxns flexibility, we used semantic
density, type frequency and token frequency (cf.
Table 4) as predictors in linear mixed effect
modelling. As dependent variable, we used the
difference gramm coer and coer imp. We
performed two separate analyses for type and token
frequencies without interactions to avoid
multicollinearity effects. Predictors values were
centered.</p>
      <p>RESULTS: The estimates are reported in Tables
5 and 6 below. In the first two models frequency
does not yield any effect. In the second models,
instead, frequency appears to have an effect on the
data. Hence, it appears that type and token
frequency help discerning impossible from coercion
instances of a Cxn, whereas only semantic
density affects the higher naturalness of coercion
phenomena. The more a Cxn is observed with
semantically similar verbs (i.e., verbs that belong
to the same classes or subclasses, which
therefore increase the Cxn semantic density), the more
the constructional meaning is easily coerced into
novel instances.
4</p>
    </sec>
    <sec id="sec-5">
      <title>Discussion</title>
      <p>
        These findings support our claim that coercion
effects are resolved by a dynamic interrelation
between verb and Cxn
        <xref ref-type="bibr" rid="ref11 ref16 ref17">(Kemmer, 2008; Kemmer
and Yoon, 2013)</xref>
        . Even though frequency
effects are shown to affect Cxns extensibility to new
items
        <xref ref-type="bibr" rid="ref8">(Bybee, 2006)</xref>
        , our results suggest that type
and token frequency only facilitate the
distinc(Gramm - coer) sem. dens + type freq.
      </p>
      <p>estimate st. error t value
(Intercept) 2.71*** 0.11 25.02
Sem. density -0.34. 0.16 -2.217
Type freq. -0.13 0.16 -0.848
(Gramm - coer) sem. dens + tok freq.</p>
      <p>estimate st. error t value
(Intercept) 2.71*** 0.11 25.02
Sem. density -0.35. 0.16 -2.23
Token freq. -0.13 0.16 -0.89
tion between semantically incompatible and
partially compatible formulations, whereas higher
coercibility is only affected by semantic density.</p>
      <p>
        We interpret this finding in light of the upward
strengthening hypothesis
        <xref ref-type="bibr" rid="ref15">(Hilpert, 2015)</xref>
        ,
according to which a novel occurrence of a linguistic unit
strengthens a superior node (i.e., the abstract Cxn)
only if the former is categorized ‘as an instance
of a more abstract Cxn. If this categorization is
not performed, or only superficially so, no
upward strengthening will take place’
        <xref ref-type="bibr" rid="ref15">(Hilpert, 2015,
p.38)</xref>
        . Higher coercibility is hence not affected by
frequency of the Cxn because of the
‘intermediate’ grammaticality level of coercion, which does
not allow unambiguous categorization. Coercion
sentences result more natural if the target Cxn is
observed with verbs belonging to similar
semantic classes or subclasses, which increases Cxn
semantic density. We could therefore assume that
coercion effects in Italian elicit a partial
categorization. The effect of semantic density, however,
only explains part of the data. In fact, visual
inspection of the relation between semantic density
and the estimates of table 3 reveals that this effect
does not explain the high coercibility of IM, or the
low values of CO Cxns (see Figure 5).
All things considered, semantic properties
(modelled with distributional vectors) of Cxns
(e.g., its density) are only one of the factors
influencing speakers processing and recognition of
coercion effects. In fact, it has been argued that
Romance languages are more valency driven than
English (and Germanic languages in general)
        <xref ref-type="bibr" rid="ref27 ref6">(Perek
and Hilpert, 2014)</xref>
        . The results of both
experiments provide substantial evidence for an
integrated account of Italian coercion effects, which
should consider not only the properties of the
general abstract Cxn, but rather the interaction of the
mismatching verb with Cxn meaning.
      </p>
      <p>These result also have interesting implications
to understand the cognitive mechanisms
underlying Cxn flexibility and productivity. In fact, these
findings support the idea that Cxn meaning is
abstracted from the semantics of prototypically
occurring verbs. As we saw, several studies have
argued in favour of this hypothesis for English,
but the fact that we were able to adapt it to Italian
suggests that the factors driving the acquisition of
Cxns are - at least partially - not language-specific
but rather general cognitive processes.</p>
    </sec>
    <sec id="sec-6">
      <title>Acknowledgments:</title>
      <p>The authors thank Lucia Passaro and Florent
Perek for their help and valuable suggestions.</p>
    </sec>
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