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  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>PARSEME-It Corpus An annotated Corpus of Verbal Multiword Expressions in Italian</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Johanna Monti</string-name>
          <email>jmonti@unior.it</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Maria Pia di Buono</string-name>
          <email>mariapia.dibuono@fer.hr</email>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Federico Sangati</string-name>
          <email>federico.sangati@gmail.com</email>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Dep. of Literary, Linguistic and Comparative Studies “L'Orientale” University of Naples</institution>
          ,
          <country country="IT">Italy</country>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>Indipendent Researcher</institution>
          ,
          <country country="IT">Italy</country>
        </aff>
        <aff id="aff2">
          <label>2</label>
          <institution>TakeLab - University of Zagreb</institution>
          ,
          <country country="HR">Croatia</country>
        </aff>
      </contrib-group>
      <abstract>
        <p>English. This paper describes a new language resource annotated with verbal multiword expressions (VMWEs) in Italian. The paper discusses the state of the art in VMWE identification and annotation in Italian, the methodology adopted, the various VMWE categories annotated, the corpus and the annotation process. Finally, the paper ends with results, conclusion and future work.</p>
      </abstract>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>1 Introduction</title>
      <p>This paper outlines the development of a
new language resource for Italian, namely the</p>
      <sec id="sec-1-1">
        <title>PARSEME-It VMWE corpus, annotated with</title>
        <p>
          Italian MWEs of a particular class: verbal
multiword expressions (VMWE). The
PARSEMEIt VMWE corpus has been developed by the
PARSEME-IT research group1 in the framework
of the PARSEME Shared Task on Automatic
Identification of Verbal Multiword Expressions
          <xref ref-type="bibr" rid="ref14">(Savary et al., 2017)</xref>
          , a joint effort, carried out
1https://www.researchgate.net/project/PARSEME-ITSyntactic-Parsing-and-Multiword-Expressions-in-Italian
within a European research network, to
elaborate universal terminologies and annotation
guidelines for verbal multiword expressions in 18
languages, among which also the Italian language
is represented. Notably, multiword expressions
represent a difficult lexical construction to
identify, model and treat by Natural Language
Processing (NLP) tools, such as parsers, machine
translation engines among others, mainly due to their
non-compositional property. In particular, among
multiword expressions verbal ones are particularly
challenging because they have different syntactic
structures (prendere una decisione ’make a
decision’, decisioni prese precedentemente ’decisions
made previously’), may be continuous and
discontinuous (andare e venire versus andare in malora
in Luigi ha fatto andare la societa` in malora), may
have a literal and figurative meaning (abboccare
all’amo ’bite the hook’ or ’be deceived’). In this
paper, we describe the state of the art in VMWE
annotation and identification for the Italian
language (section 2). We then present the
methodology (section 3), the Italian VMWE categories
taken into account for the annotation task (section
4), the corpus and the annotation process (section
5), and the results (section 6). Finally, we discuss
conclusions and future work (section 7).
2
        </p>
      </sec>
    </sec>
    <sec id="sec-2">
      <title>State of the art in VMWE identification and annotation in Italian</title>
      <p>Several scholars have investigated different kinds
of Italian VMWEs, focusing on both syntactic and
semantic aspects. Among these works, we may
distinguish contrastive and comparative analyses,
and synchronic and diachronic studies.</p>
      <p>
        In the first group, most of the scholars propose a
comparison with Germanic languages
        <xref ref-type="bibr" rid="ref10 ref20">(Mateu and
Rigau, 2010)</xref>
        , mainly for describing verb-particle
constructions, that represent a very common
phenomenon in this family.
      </p>
      <p>
        On the other hand, synchronic and diachronic
studies include analyses of: (i) verb-particle
constructions
        <xref ref-type="bibr" rid="ref11 ref6 ref6 ref9 ref9">(Masini, 2005; Iacobini and Masini,
2005; Quaglia and Trotzke, 2017)</xref>
        , (ii) idiomatic
constructions
        <xref ref-type="bibr" rid="ref15 ref17 ref18 ref19">(Tabossi et al., 2011; Vietri, 2014c)</xref>
        with either ordinary or support verbs
        <xref ref-type="bibr" rid="ref17 ref18 ref19">(Vietri,
2014b)</xref>
        , (iii) support, or light, verbs, which
represent a wider phenomenon and, for this reason,
they have been largely analysed
        <xref ref-type="bibr" rid="ref12 ref3 ref4 ref5 ref7">(La Fauci, 1980;
D’Agostino and Elia, 1998; Cicalese, 1999;
AlbaSalas, 2004; Quochi, 2007; Cicalese et al., 2016)</xref>
        .
Reflexive verbs in Italian have been investigated
as occurrences of non-local anaphora
        <xref ref-type="bibr" rid="ref13">(Reuland,
1990)</xref>
        and considering their syntactic classification
        <xref ref-type="bibr" rid="ref2">(Carstea Romascanu, 1977)</xref>
        .
      </p>
      <p>
        To the best of our knowledge only a limited
number of monolingual language resources with
multiwords for the Italian language have been
developed such as a dictionary for Italian idioms
        <xref ref-type="bibr" rid="ref17 ref18 ref19">(Vietri,
2014a)</xref>
        , a series of example corpora and a database
of MWEs represented around morphosyntactic
patterns
        <xref ref-type="bibr" rid="ref10 ref20">(Zaninello and Nissim, 2010)</xref>
        , or a
corpus annotated with Italian MWEs of a particular
class: verb-noun expressions such as fare
riferimento, dare luogo and prendere atto
        <xref ref-type="bibr" rid="ref16">(Taslimipoor
et al., 2016)</xref>
        . At the time of writing, therefore, the
PARSEME-It VMWE corpus represents the first
sample of a corpus, which includes several types
of VMWEs, specifically developed for NLP
applications.
3
      </p>
    </sec>
    <sec id="sec-3">
      <title>Methodology</title>
      <p>
        The development of the Italian VMWE corpus is
based on the PARSEME annotation guidelines2,
provided for the shared task. The guidelines have
been developed with the aim of delivering
general definitions and prescriptions for the
annotation of VMWEs in 18 languages, but, at the same
time, of allowing language-specific descriptions of
these linguistic phenomena
        <xref ref-type="bibr" rid="ref14">(Savary et al., 2017)</xref>
        .
The annotation guidelines include three main
categories:
1. a universal category, which is common to
all the languages involved in the task and
holds light-verb constructions (LVCs) and
idioms (ID);
2. a quasi-universal category, relevant for
some languages or language families, that
2The guidelines are available at
http://parsemefr.lif.univmrs. fr/guidelines-hypertext/.
contains inherently reflexive verbs (IReflVs)
and verb-particle constructions (VPCs);
3. an other VMWEs category, which is a
residual category for the occurrences not
belonging to any of the previous groups.
      </p>
      <p>In order to ease the identification and
categorisation task of VMWEs, a decision tree method
was devised with generic and language-specific
tests. Generic tests consider general criteria that
are valid for all languages, while language-specific
tests consider structural, lexical, morphological
and syntactic features that are specific for the
individual languages. The decision tree includes three
steps, (i) identification of a VMWE candidate, i.e.,
a combination of a verb with at least one other
word, which is a potential VMWE; (ii)
identification of the lexicalized elements of the
expression, (iii) assignment of the VMWE to one of the
VMWE categories, using general and
languagespecific tests.</p>
    </sec>
    <sec id="sec-4">
      <title>4 Italian VMWEs</title>
      <p>For the Italian VMWE annotation task, according
to PARSEME guidelines, multiword expressions
are understood as (continuous or discontinuous)
sequences of words with the following
compulsory properties:</p>
      <p>Their component words include a head word
and at least one other syntactically related
word. Most often the relation they maintain
is a syntactic (direct or indirect) dependency
but it can also be e.g., a coordination.</p>
      <p>They show some degree of orthographic,
morphological, syntactic or semantic
idiosyncrasy with respect to what is considered
general grammar rules of a language.</p>
      <p>At least two components of such a word
sequence have to be lexicalized.</p>
      <p>In this task we only annotate the lexicalized
components and ignore open slots. Collocations, i.e.,
word co-occurrences whose idiosyncrasy is of
statistical nature only (e.g., the graphic shows,
drastically drop, etc.), are excluded from the scope of
this study. The VMWE which have been
annotated for the Italian language are:
1. Light verb constructions (LVC), which
typically consist of a verb and a noun or
prepositional phrase, e.g., fare una domanda (’to
make a question’), fare una passeggiata (’to
have a walk’). The verb has a purely
syntactic operator function (performing an activity
or being in a state), whereas the noun is
predicative, often referring to an event (e.g.,
decision, visit) or a state (e.g., fear, courage);
2. Idioms (ID), which have at least two
lexicalized components including a head verb and at
least one of its arguments, e.g., tirare le cuoia
(’kick the bucket’), piovere a catinelle (’rain
cats and dogs’);
3. Inherently reflexive verbs (IReflV), which
are those reflexive verbal constructions which
(a) never occur without the clitic e.g.,
suicidarsi (’suicide’), or when (b) the REFLV and
non-reflexive versions have clearly
different senses or subcategorization frames e.g.,
riferirsi (’refer’);
4. Verb particle combinations (VPC), which
are formed by a lexicalized head verb
and a lexicalized particle dependent on the
verb. The meaning of the VPC is
noncompositional. Notably, the change in the
meaning of the verb goes significantly
beyond adding the meaning of the particle, e.g.,
buttare giu` (’swallow’). This type of
construction is very frequent in English, German,
Swedish, Hungarian, but we can find them
also in Italian;
5. Other Verbal MWEs (OTH), which gather
the types not belonging to any of the
categories above, e.g., corto-circuitare
(’shortcircuit’).</p>
    </sec>
    <sec id="sec-5">
      <title>Corpus and annotation task</title>
      <p>CoNLL format, i.e. lemmatized, POS-tagged and
annotated with syntactic dependencies. For our
annotation task, we selected a sub-corpus formed
by 17,000 sentences (corresponding to 421,848
tokens) randomly taken from blogs, Wikipedia and
Wikinews. The corpus was kept in its original state
and therefore no errors or inconsistencies were
corrected. The pre-annotation of the PAISA´ was
kept in order to ease the annotation work with
reference to the identification of verbal MWEs but
we asked annotators not to overestimate the
system’s performances, and to review the whole text,
not only the pre-annotated candidates proposed by
the system. A dedicated tag in FLAT was defined
for this purpose. The objective was to have a
final corpus of at least 3,500 annotated VMWEs per
language. Since the density of VMWEs highly
depend on the particular language, as well as text
choice and genre, we were not able to make any
reliable estimation of the corpus size needed to reach
this goal from the beginning of the task.
5.2</p>
      <sec id="sec-5-1">
        <title>Annotation environment</title>
        <p>The annotation environment used for the
PARSEME-It VMWE corpus is FLAT, a
webbased linguistic annotation environment3 based
around the FoLiA format4 a rich XML-based
format for linguistic annotation. FLAT allows
users to view annotated FoLiA documents and
enrich these documents with new annotations
(Figure 1), a wide variety of linguistic annotation
types is supported through the FoLiA paradigm. It
is a document-centric tool that fully preserves and
visualises document structure. It is open source
software developed at the Centre of Language
and Speech Technology, Radboud University
Nijmegen and is licensed under the GNU Public
License v3.
5.3</p>
      </sec>
      <sec id="sec-5-2">
        <title>Annotation task</title>
        <p>The annotation task for the Italian language was
performed in five different stages.</p>
        <p>1. The PARSEME Annotation guidelines were
agreed on5 and examples for the Italian
language were added in order to ease the
annotation task by the Italian annotators. To this
end, a two-phase pilot annotation in Italian</p>
        <sec id="sec-5-2-1">
          <title>3http://flat.readthedocs.io/en/latest/</title>
          <p>4http://proycon.github.io/folia
5http://parsemefr.lif.univ-mrs.fr/parseme-stguidelines/1.0/?page=home
5
5.1</p>
        </sec>
      </sec>
      <sec id="sec-5-3">
        <title>PARSEME Italian VMWE corpus</title>
        <p>
          The PARSEME-It VMWE corpus is based on a
selection of texts taken from the PAISA´ corpus of
Italian web texts
          <xref ref-type="bibr" rid="ref8">(Lyding et al., 2014)</xref>
          . We chose
this corpus because it contains documents (i) from
different web sources, e.g., Wikibooks, Wikinews,
Wikiversity, and several blog services from
different websites, collected in 2010 by means of
a Creative Commons-focused web crawling, and
a targeted collection of documents from specific
websites, (ii) dedicated to no specific technical
domain, free from copyright issues, so as to be
compatible with an open license (iii) annotated in
was carried out. This step was useful in
identifying the Italian VMWE categories to be
annotated, but also to promote cross-language
convergences with the other languages
foreseen in the shared task. Each pilot annotation
phase provided feedback from annotators and
was followed by enhancements of the
guidelines, corpus format and processing tools.
2. A pre-processing step of the PAISA´ corpus
was needed: a ’no space’ column was added
to the files in order to add the ’nsp’ tag if a
token should have been appended to the
previous one without a space.
(ii) the category attribution concerning for
instance the fare + N VMWE type, since in
some cases the category is LVC such as in
fare rumore and in some others is ID such as
in fare schifo, (iii) the identification of nested
VMWEs like in Mi guardo bene where the
annotator has to decide if in the ID guardarsi
bene there is also a IReflV guardarsi or not.
4. A few files were double-annotated to evaluate
the inter-annotator agreement (IAA).
Measuring IAA is not a trivial task because of the
challenges posed by VMWEs and described
in the Introduction. The available IAA
results organized per-VMWE F-score (Funit),
estimated Cohens K (Kunit) and finally
standard K(Kcat)
          <xref ref-type="bibr" rid="ref14">(Savary et al., 2017)</xref>
          scores are
presented in Table 1.
5. Further 1,000 sentences were used as test-set
during the shared task. The VMWE
annotations were automatically annotated by the
systems that took part in the shared task and
performed according to the same guidelines.
        </p>
        <p>#S #T #A1 #A2
IT 2000 52639 336 316
Funit
0.417</p>
        <p>Kunit
0.331</p>
        <p>Kcat
0.78
The PARSEME-It VMWE corpus is composed of
2,454 entries (Table 2), and it is freely available6,
released under Creative Commons licenses.</p>
        <p>The data have been annotated using the official
parseme-tsv format7 (Figure 3), adapted from the
CoNLL format.</p>
        <sec id="sec-5-3-1">
          <title>6http://hdl.handle.net/11372/LRT-2282</title>
          <p>7http://typo.uni-konstanz.
de/parseme/index.php/2general/
184-parseme-shared-task-format-of-the-finalannotation.</p>
        </sec>
      </sec>
      <sec id="sec-5-4">
        <title>Category</title>
        <p>ID
IReflV
LVC
VPC
OTH</p>
      </sec>
      <sec id="sec-5-5">
        <title>Total</title>
        <p>In the official parseme-tsv format, as described
in Savary et al. (2017), the information about each
token are represented by 4 tab-separated columns
featuring (i) the position of the token in the
sentence or a range of positions (e.g., 1-2) in case of
multiword tokens such as contractions, (ii) the
token surface form, (iii) an optional flag indicating
that the current token is adjacent to the next one,
and (iv) an optional VMWE code composed of the
VMWEs consecutive number in the sentence and
for the initial token in a VMWE its category (e.g.,
2:ID if a token starts an idiom which is the
second VMWE in the current sentence). In case of
nested, coordinated or overlapping VMWEs
multiple codes are separated with a semicolon.
Furthermore, in order to provide data usable as
features in the shared task systems, also companion
files in a format close to CoNLL-U8 have been
re</p>
        <sec id="sec-5-5-1">
          <title>8http://universaldependencies.org/format.htm</title>
          <p>leased. These companion files contain extra
linguistic information, i.e., lemmas, POS-tags,
morphological features, and syntactic dependencies.
7</p>
        </sec>
      </sec>
    </sec>
    <sec id="sec-6">
      <title>Conclusion and Future Work</title>
      <p>In this paper, we described a linguist resource of
Italian VMWE, developed within the PARSEME
Shared Task on Automatic Identification of
VMWE. We consider this work an initial
contribution for elaborating an Italian universal
terminology of VMWE. Future work includes the
extension of the current corpus and a fine-grained
linguistic analysis of the annotation in order to
contribute to the description of these phenomena.</p>
    </sec>
    <sec id="sec-7">
      <title>Acknowledgments</title>
      <p>The work described in this paper has been
supported by the IC1207 PARSEME COST action.
The annotation work was carried out also thanks
to the help of Maarten van Gompel who adapted
the FLAT annotation platform to the needs of the
community.</p>
      <p>Our thanks go also to the Italian annotators,
Valeria Caruso, Manuela Cherchi, Anna De Santis,
Annalisa Raffone, for their contributions.
Autorship contribution is as follows: Johanna
Monti is author of Sections 1, 3, 4 and 5.3; Maria
Pia di Buono of Sections 2 and 6 and 7; and
Federico Sangati of Sections 5.1 and 5.2.</p>
    </sec>
  </body>
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