LexicO: un lessico computazionale italiano derivato da Parole-Simple-Clips
10.6092/issn.2532-8816/15176
Parole-Simple-Clips (PSC) is a computational lexicon of the Italian language, developed from 1996 to 2003 by the Institute of Computational Linguistics of the Italian National Research Council (ILC-CNR) in the context of national and European projects. The PSC resource is strongly structured and rich of data, and may provide an edge over existing linguistic resources if used in the support of NLP and text retrieval related tasks, such as, as currently being experimented, for full-text search. However, the lexicon still appears incomplete and contains some redundant, erroneous and missing data. This paper documents the first steps undertaken for the creation of LexicO, an Italian computational lexicon built upon PSC starting from an in depth analysis of its four linguistic layers (semantic, syntactic, morphological and phonological) in which it is structured. As a result of this work, LexicO has been released and made freely available for download.
application/pdf
2532-8816
15176
15
en
Simone Marchi
http://creativecommons.org/licenses/by/4.0
Umanistica Digitale
LexicO
LexicO: an Italian Computational Lexicon derived from Parole-Simple-Clips
Text.Serial.Journal
https://umanisticadigitale.unibo.it/
Articles
2023-07-17
2022-08-01
2023-07-17
2023-07-17
169-193
Um. Dig.
Simone Marchi
2023/07/17
10.6092/issn.2532-8816/15176
169
Istituto di Linguistica Computazionale “A. Zampolli”, CNR, Pisa, Italia
2532-8816
15
LexicO
en
193
LexicO: an Italian Computational Lexicon derived from Parole-Simple-Clips
https://umanisticadigitale.unibo.it/article/download/15176/16496
     Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon
DOI: http://doi.org/10.6092/issn.2532-8816/15176
		
    LexicO: an Italian Computational Lexicon derived from
                      Parole-Simple-Clips

                                            Flavia Sciolette

                Istituto di Linguistica Computazionale “A. Zampolli”, CNR, Pisa, Italia
                                  flavia.sciolette@ilc.cnr.it




                                           Simone Marchi

                Istituto di Linguistica Computazionale “A. Zampolli”, CNR, Pisa, Italia
                                    simone.marchi@ilc.cnr.it




                                        Emiliano Giovannetti

                Istituto di Linguistica Computazionale “A. Zampolli”, CNR, Pisa, Italia
                               emiliano.giovannetti@ilc.cnr.it




        Abstract

Parole-Simple-Clips (PSC) is a computational lexicon of the Italian language, developed from
1996 to 2003 by the Institute of Computational Linguistics of the Italian National Research
Council (ILC-CNR) in the context of national and European projects. The PSC resource is
strongly structured, rich of data, and, for its features, may provide an edge if used in the support
of text retrieval related tasks, such as full-text search. However, the lexicon still appears incomplete
and presents some redundant, erroneous and missing data. This paper documents the first steps
undertaken for the creation of LexicO, an Italian computational lexicon built upon PSC starting
from an in depth analysis of its four linguistic layers (semantic, syntactic, morphological, and
phonological) in which it is structured. As a result of this work, LexicO has been released and
made freely available.
Keyword: Computational Lexicon; Parole-Simple-Clips; Linguistic Resources; Full-text Search;
LexicO
Parole-Simple-Clips (PSC) è un lessico computazionale per l’Italiano, sviluppato tra il 1996 e il 2003
presso l’Istituto di Linguistica Computazionale del Consiglio Nazionale delle Ricerche (ILC-CNR)
nell’ambito di progetti nazionali ed Europei. PSC è una risorsa ricca, fortemente strutturata e, per
le sue caratteristiche, può fornire un vantaggio in task di text retrieval come la ricerca full-text. PSC
appare tuttavia incompleta in alcune sue sezioni e presenta dati ridondanti, erronei o mancanti.

                                                  169
       Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023



Questo contributo descrive i primi passi compiuti per la creazione di LexicO, un lessico computazionale
italiano costruito sulla base di PSC a partire da un’analisi approfondita dei suoi quattro livelli
linguistici (semantico, sintattico, morfologico e fonologico). A seguito di questi interventi, LexicO è
stato rilasciato e reso liberamente disponibile.
Parole chiave: Lessico Computazionale; Parole-Simple-Clips; Risorse Linguistiche; Ricerca sul
testo; LexicO



       Introduction

Despite the availability of several linguistic models and projects focussing on the systematisation
of linguistic data (see Related works), just a few computational lexical resources seems to be
available specifically for the Italian language1. This is understandable, if we consider how the
construction of a large-scale lexical dataset, especially if structured by following a full-fledged
linguistic model, can be extremely time consuming and require high-level linguistic skills. The
resources most employed in different computational tasks are mainly the multilingual Wordnets;
to the best of our knowledge, other lexicons appear under-used2 for computational processes, in
comparison with them. Due for their complexity, however, they could be profitably exploited to
provide support in different tasks related to text and linguistic processing, such as information
retrieval, word sense disambiguation, named entity recognition, full text search and, more in
general, any task that may take advantage from the availability of structured linguistic information.
This work documents the analysis of a portion of a fundamental linguistic resource of Italian: the
computational lexicon known as “PAROLE-SIMPLE-CLIPS” (PSC). The critical issues found
during the analysis have led to the creation of LexicO, a new Italian computational lexicon
conceived to gather data coming from PSC as they will be analysed and revised over time. PSC
was built from 1996 to 2003 at the Institute of Computational Linguistics of the Italian National
Research Council (ILC-CNR) in the context of national and European projects [25].
Thanks to its depth, richness, and the rigorous linguistic model it is built upon, PSC is a mine of
information in itself, but it may also be productively exploited for many tasks, both of scholarly


    1     In the repository of Italian node of CLARIN (Common Language Resources and Technology
    Infrastructure) - the research infrastructure ensuring availability and accessibility of language resour-
    ces and tools - the search for “lexicalConceptualResource” as “type” and “Italian” as “language” pro-
    vides 12 results. At the time of writing, the main detected resources are Parole-Simple-Clips (PSC)
    and IWN (ItalWordNet), both described in Related works; the other results include i) five resources
    derived from the first two; ii) five domain terminologies (https://dspace-clarin-it.ilc.cnr.it/repository/
    xmlui/). Expanding the search to all the Virtual Language Observatory, we can find other two resour-
    ces (see Related works) of further public 20 in the repository. These 20 resources include multilingual
    domain terminologies and four previous stages of other resources (https://vlo.clarin.eu/?4)
    2     For the Italian linguistic domain, an original use of resource for a computational task is re-
    presented in [28] as quantitative methods to detect polysemy, with the employ of PSC. The uses of
    domain terminologies for recognition and automatic extraction of terms are more represented (for
    example in the Automatic Misogyny Identification, task within Evalita 2018, see [8] for an overview
    of the methods and use of lexicons).In every case, this kind of studies appears less-represented in the
    main repositories (Wos, Scopus, Zenodo, Google Scholar).
                                                    170
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




and computational nature, such as, as in the example described in The use of a computational
lexicon in an application of full-text search: the case of querying of the babylonian Talmud, for
advanced full-text search. In particular, we here describe the analysis process and the consequent
discovery of a set of redundant,3 erroneous and missing data in PSC. This work appeared
necessary after the experiments of full-text search described in [11] (see for example the case of
missing synonyms reported in The use of a computational lexicon in an application of full-text
search: the case of querying of the babylonian Talmud). The paper is organised as follows: the
Related works is dedicated to related works about lexical resources for Italian with references
to works involving the application of linguistic resources in information retrieval contexts; the
The use of a computational lexicon in an application of full-text search: the case of querying
of the babylonian Talmud introduces the case-study with which we started to experiment the
use of the resource; the The PSC resource describes the underlying model and the current data
stored in PSC; the Discovery and processing of redundant, erroneous and missing data shows
the approach and outcome of the discovery and processing of redundant, erroneous, and missing
data; the Conclusions and perspectives introduces the next steps planned for further updates of
LexicO.



        Related works

Italian linguistics has always shown an interest for lexical data [27], an inclination that has been
progressively encouraged by the increasing availability of computational technologies and the
development of many “corpus-based” resources. Many projects involving corpora (monolingual,
parallel, domain-specific) have flourished and both digitised traditional dictionaries and
computational dictionaries have taken advantage of them (e.g. to calculate the frequency of
lemmas, the adherence of the use, or the coverage) [5], as well as for applications in different
tasks, like “machine translation, computer-aided translation, human-aided machine translation”
[6], full-text search, topic modelling or information retrieval, as discussed in the following.
Besides PSC, just a few digital resources including lexical information are available for Italian.
In the following we list those that met the following three criteria: i) free availability; ii) presence
of structured linguistic information, also specifically for Italian (thus excluding terminologies of
specific domains), and iii) structuring in a machine-readable format.
The first set of resources we took into account are those based on WordNet [7]. An important
linguistic resource for Italian is ItalWordNet4 (IWN) [24], firstly developed for EuroWordNet5
and successively extended.
Successively, we cite MultiWordNet,6 developed by the Bruno Kessler Center in Information
and Communication Technology (FBK-ICT Irst Center); the alignment of the synsets belonging
to the different languages populating the resource (including Italian) is strictly based on the


    3      The term “redundant”, in this work, indicates any kind of data in excess that needs to be remo-
    ved.
    4      http://www.ilc.cnr.it/iwndb/iwndb_php/
    5      http://projects.illc.uva.nl/EuroWordNet/
    6      http://multiwordnet.fbk.eu/english/home.php
                                                   171
       Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023



semantic relations available in Princeton WordNet and the enrichment of the resource has been
carried out with the help of semi-automatic techniques [21].
Amongst the lexicalized ontologies integrating WordNet, we here mention Babelnet,7 a
multilingual resource that includes Italian [20]. Babelnet is characterised by a huge amount of
data, created by linking Wikipedia with Princeton WordNet and different many other resources:
similarly to WordNet, Babelnet is organised into sets of synonyms, called “Babel synsets”.
Amongst the derived resource from Babelnet, we cite SyntagNet, a recent example of lexical-
semantic combination database of syntagmatic relations, developed by Sapienza University of
Rome in the context of ERC-Project MOUSSE8. This resource contains manually disambiguated
78.000 noun-verb and noun-noun lexical combinations of five languages: English, German,
French, Spanish, and Italian. The 88.019 semantic combinations are linked with 20.626
WordNet synsets for 14.004 lemmas.
Another resource for Italian is Senso Comune,9 a lexical knowledge base developed by University
of Sapienza and CNR. The “core” of the resource is composed of 2.000 lexical units which have
been manually associated with the concepts belonging to a foundational ontology of reference.
This core of entries is freely available, while the complete resource is protected by copyright.
Though built more as support tools for NLP than as lexical resources, we also mention Italian
Content Words [12] and Italian Function Words [13]. Both resources are conceived to help
in pos-tagging and parsing, and - at the time of writing - they are distributed as UniMorph-
annotated dictionaries10 [18]. Regarding resources based on FrameNet,11 it is worth mentioning
the project of an Italian FrameNet, still under development [2], with 7.000 validated lexical
units.
The growing attention to the creation of lexical resources is also reflected in the development
of standards for the encoding of computational lexicons. At present, the de facto standard is
OntoLex-Lemon12, a model for lexical resources developed in the framework of Linked Open
Data (LOD).
The effective use of lexical resources is basically limited to WordNet, though for different tasks.
In the field of Information Retrieval, we cite SIRWWO (Semantic Information Retrieval using
Wikipedia, WordNet, and domain Ontologies), a recent approach that combines multiple
knowledge sources [14]; in [15] the authors describe the use of WordNet for text categorization
of multi-label documents. With a specific focus on the task of Word Sense Disambiguation, we
highlight the work of NLP Lab of Sapienza [3].
All the resources introduced in this section present different features, especially considering the
model they adopt to describe linguistic information. Furthermore, some of them (such as Senso
Comune) are not available in their entirety and thus they cannot be fully accessed and assessed.
For these reasons, it is very difficult to do an in-depth and effective comparison among them.


    7     https://babelnet.org/
    8     http://syntagnet.org/
    9     http://www.sensocomune.it/ [no longer working, 7/11/2022]
    10 Universal Morphology (UniMorph) is a project that provides a universal schema for the annota-
    tion of morphology (https://unimorph.github.io/).
    11 https://framenet.icsi.berkeley.edu/fndrupal/
    12 https://www.w3.org/2016/05/ontolex/
                                               172
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




However, to better substantiate our choice of exploiting PSC with respect to the other considered
resources, in PSC compared to other resources for Italian language we will compare them on
the basis of some selected portions of their data. Furthermore, as it will be shown in the The
use of a computational lexicon in an application of full-text search: the case of querying of the
babylonian Talmud, PSC presents features that can make its use particularly effective in text
querying contexts. At the same time, however, this resource has some problems that need to be
corrected before its employment, as highlighted in the experimentation described in the The
use of a computational lexicon in an application of full-text search: the case of querying of the
babylonian Talmud.



         The use of a computational lexicon in an application of full-text sear-
         ch: the case of querying of the babylonian Talmud

In this section, we introduce a case study where PSC has been exploited to query the text of
the Italian translation of the babylonian Talmud, the primary source of Jewish religious law
and theology. The translation is being carried out by domain-expert translators working in the
context of the Babylonian Talmud Translation Project13 using Traduco, a computer-assisted
translation tool developed at ILC-CNR [10]. At the time of this writing, seven treatises of the
Talmud have been published, and more than 1 million of Hebrew/Aramaic words have been
translated. It is therefore a corpus of considerable dimensions covering a wide range of topics, the
querying of which, for example by keyword, can prove to be extremely difficult. As a matter of
fact, translators and editors of the Talmud often need to examine and compare different parts of
the text in order to publish a coherent and homogeneous translation.
Traduco already includes two search functions, a standard keyword based search and a search
based on the presence of manually inserted semantic annotation of different kinds, among which
talmudic concepts, linguistic expressions, names of Rabbis, etc.
However, scholars working at the translation of the Talmud would often need to query the text
combining different kinds of linguistic information - for example to find words belonging to
a specific semantic field and having specific morphological constraints - a task that a simple
keyword-based search cannot easily fulfil. For this reason, we decided to develop an advanced
full-text search interface to the Talmud integrating a query expansion technique exploiting the
PSC as a support lexicon. This interface adds to Traduco two new querying modalities: i) a search
by form or lemma with constraints on morphological traits and the possibility of extending the
search with semantic relations; ii) a search based on semantic traits.
The first kind of search is shown in Figure 1. A user can insert a word and indicate if it has to be
interpreted as an inflected form or a lemma: in this second case, the system searches for all the
relative forms.




    13    https://www.talmud.it/
                                               173
        Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023




  Figure 1: The form/lemma section of the interface for full-text search supported by PSC; the talmudic
treatises, constituting the corpus, are shown on the left side, the query interface is placed at the centre and
                           the results of the search are shown in the rightmost part.

The underlying system accesses the lexicon to retrieve all the lemmas, the relative inflected forms,
and the lexical senses14 corresponding to the indicated word. Queries can be enriched with filters on
morphological features (e.g. on pos, gender, number, tense and mood for verbs) and extended with
semantic relations (at the moment limited to hypernymy, hyponymy, and synonymy) to be applied
to the selected senses. The right side of the interface shows, on top, the forms retrieved from the
lexicon and used to expand the query, while, below, the list of occurrences of the searched words in
their context. In the following example, as reported in Figure 1, we describe a case of propagation of
morphological features to related senses. We consider the case of word dedurre, with the sense of “to
presume”: the richness of linguistic information in PSC appears particularly productive when the
query involves verbal entries, since the search can be focussed on forms with specific moods, tenses,
etc. With the insertion of the morphological constraints “indicative” and “subjunctive”, a user can
search for all the forms with these finite moods. In addition, with the expansion of the search to
synonyms, the forms of desumere (a synonym of dedurre present in PSC) are added to the query.
During the exploitation of the resource, it has been observed how the results of some queries
were incorrect, for example when searches were extended with synonymy. If a user searches for
synonyms of word desumere, the results do not include the occurrences of dedurre. As a matter of
fact, as shown in Figure 2, PSC includes a synonymic relation between dedurre and desumere (the
solid arrow) but does not include the symmetric relation holding between desumere and dedurre
(the dotted arrow). This is an example of missing data that motivated the update of the resource,
as detailed in Discovery and processing of redundant, erroneous and missing data.



    14 In this work, we use the term “sense” to define the meaning described in the resource as Seman-
    tic Units (SemUs, cfr. The PSC resource), according to the documentation of PSC: “Following the
    terminology of the GENELEX Model, word senses are encoded as Semantic Units or SemUs” [17].
    From a theoretical point of view, in the documentation, the concept of “meaning” is associated with
    the Qualia Structure; the meaning of a word is the result of an interaction among different compo-
    nents; these components of meaning can coexist in the same concept (e.g. “apple” is an object from a
    formal point of view and it is a food, from a telic perspective).
                                                     174
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




 Figure 2: An example of missing data in PSC: the shown sense of verb “dedurre” is linked to the sense of
 “desumere” through a relation of synonymy, but the symmetric relation is missing (the dotted arrow); a
              similar case of asymmetry is present between verbs “desumere” and “arguire”.

The second kind of search, involving semantic traits, is shown in Figure 3. In this example, the user
selects the template15 “Vegetal Entity”, which is classified as a subclass of “Living Entity”. Once the
template is chosen, the system retrieves from the lexicon all the relative senses and shows them in a
window to the right of the template hierarchy. It is then possible to select all the available 639 senses
or just some of them. Finally, the user can run the search: the system composes the expanded query
and retrieves 2.500 textual segments of the corpus containing words (both as lemmas and inflected
forms) with senses referring to the semantic field of “Vegetal Entity” such as frutti (fruits), pino
(pine), alberi (trees), and so on. The user may then further filter the result of the query by selecting
just a “leaf” of the subtree under “Vegetal Entity”, for example the template “Fruit”.




 Figure 3: The semantic traits section of the interface, conceived for a more explorative search of the cor-
 pus. In the example, the user searched for occurrences of words the senses of which refer to the template
                                              “Vegetal Entity”.


    15    See The underlying linguistic model.
                                                    175
       Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023



In this case, the system retrieves 63 senses from the lexicon for 398 occurrences on the corpus,
such as cedro (citron), dattero (date), and melagrana (pomegranate). The results of this query do
not give only purely quantitative data, but also allow qualitative considerations to be made: in
this case we can note how the results contain just fruits widespread in the Mediterranean basin.
More information on the search tool can be found in the already cited [11].



       The PSC resource

The Parole-Simple-Clips resource, as the name suggests, was developed in distinct projects:
LE-PAROLE (“Preparatory Action for Linguistic Resources Organisation for Language
Engineering”) for the morphological and syntactic layers; LE-SIMPLE (“Semantic Information
for Multifunctional Plurilingual Lexica”) for the core of the semantic lexicon; CLIPS (“Corpora
e Lessici dell’Italiano Parlato e Scritto”16) for the enrichment of the syntactic layers and the
development of the phonological layer [19];[4]. Further works involved the analysis, enrichment
and processing of PSC, among which we cite: i) the enrichment of the semantic representation
with relational information between events and participants [26], ii) the conversion of the
resource in a MySQL database17, iii) the first phases of the conversion in a Linked Open Data
(LOD)-compliant format [9];[16], iv) the mapping and the creation of a Sense Inventory
[22]. In this long span of time, the resource has grown from a quantitative and qualitative
point of view, and appears as one of the richest linguistic resources freely available for Italian.
At the time we writing, PSC is available on CLARIN repository as a MySQL database,
 in which are represented all the four levels of linguistic information. There are no (both user
and programming) interfaces. Despite the considerable investment made in the context of the
European projects where its development has been carried out, this resource, at present, appears
difficult to use and needs some work of renovation. The following sections present a preliminary
analysis of model and data included in the database. As we will see, the entries include partially
incomplete and redundant, missing, and erroneous data. These considerations justify subsequent
interventions on the resource, for its better exploitation.


       The underlying linguistic model
The lexical model of PSC is the common architecture adopted for the development of the
multilevel standardised lexicon of 12 European languages (Catalan, Danish, Dutch, Finnish,
French, German, Greek, English, Portuguese, Spanish, Swedish, and Italian).
The theoretical framework of the model is the Generative Lexicon by James Pustejovsky [23],
based on the Qualia (“roles”), constituents of the lexical information as “generative devices”.
A lexeme is expressed according to a compositional approach, although the meaning cannot
be considered as a mere sum of a set of certain properties. The Qualia Structure (QS) includes
four roles: formal, constitutive, telic, and agentive. The formal role provides the information


    16 The speech corpus CLIPS (http://www.clips.unina.it/it/index.jsp) was developed by the CI-
    RASS - Centro interdipartimentale di ricerca per l’Analisi e la Sintesi dei Segnali [1].
    17 The dump is available on CLARIN: https://dspace-clarin-it.ilc.cnr.it/repository/xmlui/hand-
    le/20.500.11752/ILC-88
                                                176
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




that distinguishes an individual within a larger set; the constitutive role expresses the relations
concerning the composition of an entity or event; the telic role describes the functions or the
scopes of an entity; finally, the agentive role formalises the origin of an entity.
The original QS was expanded in the “Extended Qualia Structure”18 (EQS). In the EQS, subtypes
are associated with the four roles of the original QS (e.g. for the telic role: object_of_the_activity,
used_for, used_as, used_by), corresponding to the relations between two semantic units. Every
relation is expressed by a prototypical or optional value. The Qualia relations can be combined to
describe semantic types of different degrees of complexity. This system of semantic types is defined
as the SIMPLE Ontology [17], composed of 157 concepts (as semantic types are also called in
that context), the top-level of which have been modelled on the same concepts of EuroWordNet.
The concepts of the ontology are divided into mono-dimensional types, organised through
hypernymy relations (e.g. EARTH_ANIMAL is a sub-type of ANIMAL, that is a subtype of
LIVING_ENTITY, and so on) and unified types (Multi-dimensional), represented using a
combination of hypernymy relations and different dimensions of meaning (e.g. CHANGE_
OF_LOCATION is a unified type, with embedded properties of his super-type CHANGE, but
having also agentive information).
The ontology can be expressed with different levels of granularity: it includes a Core Ontology,
the highest-level part with the most shared common concepts among the languages, and a
Recommended Ontology, with specific types that represent the lower nodes in the hierarchy and
which provide a more granular organisation of the word-senses [17].
An important element used to describe the semantic layer of PSC is the template, a predefined
schema conceived to provide information to a given semantic type and, consequently, to a sense
with which the template is associated. In this way, every word sense associated with a specific type
is automatically equipped with a set of well-structured information, relative to different semantic
features. The template can be considered as the interface between the lexicon and the ontology;
the adoption of common templates is a solution to guarantee the correctness and the uniformity
of the data among different lexicons.19
The PSC resource contains four layers of linguistic information: semantics, morphology, syntax,
and phonology. These layers are separated but the single units are connected to each other through
different relations: e.g. a semantic unit is connected to one or more syntactical units in order to
link semantic properties (for example, the arguments) with syntactic features (for example the
elements of a syntactic construction. See The available data).


         The available data
The structure of the relational database currently representing PSC reflects the multilayered
nature of the model; we here provide a brief description of the schema and of the available data.
The database is a MySQL conversion of the original MS Access database. A schematization of the
main tables populating the database and their associations is shown in Figure 4.




    18    http://www.ilc.cnr.it/AZ_bibliography/Z176.PDF
    19    An example of a template can be found in [17].
                                                 177
       Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023




Figure 4: The main tables constituting the database that have been analysed in this work for the discovery
                                of redundant, erroneous and missing data

We first introduce the phonological layer, composed of 387.125 phonological units (PhoUs).
Each entry20 is accompanied by its phonetic representation (described with “phono” and “sampa”
attributes in the database) with hyphenation (syllables).
In particular, PhoUs represent the phonological description of words and they are connected
with Morphological Units (MorUs), which constitute the lemmas of the lexicon. MorUs
can be classified on the basis of their part-of-speech (pos), as it is illustrated in Table 1. The
morphological layer is made up of the following information: the lemma, the pos, and a set
of inflectional rules, described as “ginp” (“Combination of Morphological Features”).21 These
features contain removal/addition rules for the derivation of words, and their morphological
features (e.g. gender, number).




    20 In this paper, we use the term “entry” to indicate a distinct unit, encoded in one of the available
    linguistic layers (phonological, morphological, syntactic and semantic) of the resource; an association
    is the representation of a relationship between two entries. More specifically, in this paper, we descri-
    be the associations between a PhoU and a MorU, between a SynU and a SemU, and between two
    SemUs (representing semantic relations).
    21 http://www.tagmatica.fr/doc/parole_IT.pdf, 2.
                                                   178
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




                 MorUs

       pos       N             A           V            ADV       NP         OTHER         Tot

       #         48.735        11.830      6.521        3.311     1.069      535           72.001
       %         67.69%        16.43%      9.06 %       4.60%     1.48%      0.74%         100%

  Table 1: The distribution of the available 72001 MorUs (lemmas) wrt pos; OTHER gathers the POSs
                          relative to the different pronouns and determinatives.

Every MorU is associated with a certain number of PhoUs, derived by the rules of the
corresponding ginp, with pos and a morphological feature (morphFeat) that represents the
description of inflected forms.
In Table 2, the MorU “chiesa” (church) is connected with two PhoUs: “chiesa” with morphFeat
“FS” (feminine singular) and “chiese” with morphFeat “FP” (feminine plural).

                           MorU                PhoU        pos     morphFeat
                           chiesa              chiesa      N       FS
                           chiesa              chiese      N       FP

                               Table 2: The word-forms of chiesa (church).

Syntactical units (SynUs) are the entries populating the syntactic layer. SynUs are associated with
a MorU as corresponding lemma and with a short description of the syntactic behaviour (e.g.
the construction of transitive with an auxiliary verb). Optionally, a SynU can be described with
a correlated description (e.g. the intransitive structure) and a frameset, a representation of the
connection between the short description and the correlated description.22 Table 3 illustrates, in
percentage, the presence of the different features describing SynUs.




    22 The descriptions of SynUs are represented in the resource according to a specific and concise
    annotation. For example, a value for a description is “t-xa”: “t” stands for “transitive”, while the “x”
    introduces the lexical information of the entry, in this case, the value “a” with the meaning of the au-
    xiliary “avere” (to have). http://www.tagmatica.fr/doc/parole_IT.pdf, 6.
                                                   179
          Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023




                SynUs

     Attr.      pos       MorUs     SemUs      comment      example     description     descriptionL      framsetL
     #          65.565    65.565    51.070     16.737       42.796      65.565          1.481             1.478
     %          100%      100%      77.89%     25.53%       65.27%      100%            0.22%             0.22%

Table 3: Presence of the different features describing SynUs (e.g. just 65.27% of SynUs have an example)

The semantic layer of PSC is expressed with semantic units (SemUs) and represents the richest
among the linguistic layers of the resource from the point of view of encoded information. SemUs
are codified with pos, lemma and definition. In addition, each SemU is associated with one
template, a set of semantic traits and it’s related to one or more other SemUs through semantic
relations. For example, the SemU of word “piadina” (a kind of flatbread) is associated with the
template “Artifact_Food”. The relation IsA connects “piadina” as a hyponym of the semantic
unit “pane” (bread). The description of the sense, expressed with a template, is further enriched
by semantic traits; in the case of SemU “piadina”, they are Concrete-Entity, Artifact (Agentive),
Food (Telic), and the type of domain (Food). Regarding the argumental structure, while the SynU
describes the syntactic behaviour of the word it represents, the type of arguments is associated
with the relative SemU. For example, the Arg0 (ARG0offendere#1) of the verb “offendere” (to
insult) is codified as “Role_ProtoAgent” (semantic role), while “ARG1offendere#1” is the Arg1
of the same verb, with the semantic role “Role_ProtoPatient”.
In PSC, predicates are represented by entities associated to SemUs and to which it is possible
to provide an argumentative structure, for example, a sense of word “fumatore” (smoker) is
associated to the predicate “PREDfumare#1” (to smoke).
Predicates are associated with relative arguments and the appropriate semantic role; for example,
“PREDabile#1” (skilled) is associated to the arguments “ARG0abile#1” and “ARG1abile#1”.
These arguments are connected with the semantic roles; in the case of “ARG0abile#1”, the relative
semantic role is “Role_ProtoAgent” while “ARG1abile#1” is associated to “Role_SOA_ARG”
(State Of Affairs Argument). The correspondence between arguments of the argumental structure
- the semantic frame - and the syntactic structure is defined according to values expressed with the
attribute idCorresp. Table 4 shows the coverage of the different features characterising SemUs.

             SemUs

 Attr.       definition   example     comment      SynU        traits        template       predicate23      relations
 #           47.937       24.865      16.513       57.100      54.437        54.437         16.317           48.198
 %           83.85%       43.49%      28.88%       99.87%      95.22%        95.22%         28.54%           84.30%

                 Table 4: Presence of features describing SemUs, shown in percentage numbers




     23       Predicates are encoded for verbs, nouns, and adjectives.
                                                        180
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




       PSC compared to other resources for Italian language
As anticipated, a comparison between PSC and the other lexical resources introduced in Related
works appears difficult, especially since they are grounded on different linguistic models.
However, they share some data that may be used, at least, to provide some hints about their
different features and coverage.
We start from a purely quantitative point of view in the analysis of the following resources:
PSC, ItalWordNet (IWN), MultiWordNet (MWN), BabelNet, and Italian Function/Content
Words (IF/CW). We have decided to exclude FrameNet, Senso Comune, and BabelNet from
this comparison for different reasons. Firstly, FrameNet is still under construction while Senso
Comune is freely available just for a small “core” portion. BabelNet deserves a separate mention;
this resource currently represents the most important knowledge graph, with highest number of
senses (9.260.58724), synsets (5.923.356), and istances of relations available for Italian. However,
as a knowledge graph and encyclopaedic dictionary, BabelNet consists of linking amongst
different resources, one of them considered in our comparison (IWN), and includes named
entities that do not pertain strictly to a lexicon of a language. Given its importance in the field
of semantic resources and for its application in tasks as the word sense disambiguation, it was
necessary to mention in the state of the art (see Related works), but it is not possible to compare
BabelNet with the other considered resources.
We summarise the chosen data in the Table 5.


             Lemmas      Senses     Forms            Syntactic    Synsets    Relation    Number of
                                                     Units                   instances   relations
    PSC      72.001      57.172     469.746          64.565                  83.415      138


    IWN      48.41625    68.241                                   49.350     138.385     83


    MWN      41.491      57.934                                   32.673     45.593      14


    I F /    90.192                 2.345.63126
    CW


Table 5: Presence of the different features describing SynUs (e.g. just 65.27% of SynUs have an example)

While all the resources mentioned take into account a single layer of information and they
do not always permit connections among the entries, PSC presents a complex inner structure,


    24 Distribution: 9.219.129 nouns, 20.146 verbs, 16.662 adjectives, 4.650 adverbs, 2.651.799
    concepts, and 3.271.557 named entities.
    25 Distribution of pos: 3.918 proper nouns, 29.527 nouns, 8.015 verbs, 5.808 adjectives, and
    1.090 adverbs.
    26 The resource was probably created with an algorithmic generation of entries using morpholog-
    ical rules, which may have led to an hypergeneration of forms, for example for adverbs. Furthermore,
    there are duplicated lemmas in both resources; furthermore, the group of Function Words includes
    multiword expressions, thus making it further complex to estimate the number of headwords.
                                                  181
       Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023



based on four levels of linguistic descriptions: morphology, phonology, semantics and syntax.
Furthermore, it presents a varied set of relations among the entries that permits a fine-grained
description of senses.
We believe that the uses of a multilevel linguistic resource have not yet been sufficiently explored
in the context of NLP processes. In this sense, PSC can constitute a useful tool for further
development and improvement in the context of human language technologies.
Furthermore PSC has the advantage of being conceived according to a consistent model for
all levels of information and with lexicons of other languages; this feature makes it particularly
suitable for its conversion into a compliant LOD format as it is already conceived to favor
interoperability among different resources.



       Discovery and processing of redundant, erroneous and missing data

This section describes the process of discovery of PSC’s redundant, erroneous, and missing data
and which included PhoUs, MorUs, SemUs, SynUs, and the relative associations. As a result
of this process, the revised data were conveyed in LexicO, which has been made available for
download on CLARIN-IT repository.27
The first kind of data we analysed was related to the presence of redundant entries, some of which
had to be removed while others represented potential redundant entries that will require a human
verification. To isolate and point out candidate redundant entries some ad hoc algorithms and
queries were developed to browse the various tables of the database; depending on the nature of
the entry being examined (semantic unit, syntactic unit, morphological unit, or phonetic unit),
we were able to discover those entries and mark them appropriately to be manually examined ex
post on LexicO or not to be considered in case of sure redundant data.
To support this task the database representing the lexicon has been extended with a set of support
tables, each of which consisting of three fields: i) the id of the candidate redundant entry, ii)
the id of the redounded entry, and iii) a number representing a status, used to indicate the type
of redundancy. For the sake of simplicity, from now on just two statuses will be considered for
redundant entries: “sure redundant” (hereinafter referred to as redundants) and “to be verified”
(that include possible redundant and erroneous or incomplete data).
A finer-grained classification of redundancy has been introduced and made available through an
ad hoc developed web interface28 where also the algorithms used to find out redundant, erroneous
and missing data are shown and the results of which can be evaluated.29
Table 6 summarises the number of entries and associations (see Note 20) before and after the
analysis (respectively, columns “PSC” and “LexicO”), and including the number of affected
entities, summarising the extent of the intervention and containing the values relative to all
inserted, not considered and modified entries of a certain kind in LexicO; finally, the column

    27 http://hdl.handle.net/20.500.11752/ILC-977
    28 https://klab.ilc.cnr.it/PSC-critical-entries/
    29 The code of the evaluation interface is available at: https://github.com/klab-ilc-cnr/PSC-criti-
    cal-entries
                                                 182
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




“to be verified” concerns all the data that cannot be modified with an automatic procedure, but
require an additional check by lexicographers.

                                                      PSC         LexicO     affected30   to be verified
    Semantic Units                                    57.172      56.870     41           365

    Syntactic Units                                   64.565      64.561     1.172        222

    Morphological Units                               72.001      71.021     980          0
    Phonological Units                                387.125     387.036    89           0
    Associations MorUs - PhoUs                        469.746     469.708    221          0
    Associations SynUs - SemUs                        59.089      59.048     729          0
    Semantic rels (synonymy, holonymy, meronymy)      8.766       14.667     5.927        5.922

 Table 6: The number of entries, associations and semantic relation instances in PSC and in LexicO (i.e.
 before and after the analysis), the affected (inserted, modified, deleted) entities and those marked to be
manually verified; relations instances are, at the moment, limited to synonymy, holonymy and meronymy.

Each of the developed algorithms makes a preselection of candidate redundant entries by looking for
equivalent values on a first set of features. Then, additional data are taken into account: they are added
to the comparison and used by the algorithm to narrow the search by introducing more restrictions.


        Redundant phonological units
PhoUs populate the phonological layer of the model. The adopted procedural approach allows to
discover and remove redundant PhoUs by isolating those having the same naming and phonetic
representations, described by the “phono” and “sampa” attributes. The algorithm discovered a
total of 89 redundant PhoUs, which, consequently, have not been exported to the new lexicon.
In some cases, the removal of a PhoU involved also the update of the relative association (when
present) with the MorU, as shown in the examples below.
The process of discovering candidate redundant entries distinguishes, for Phonological units,
four cases. In the first case, 11 PhoUs were found as duplicate of other entries, since they had
same naming, phono, morphFeat and they were linked to the same MorU; in the second case,
the removal of the restriction on the equivalence in the morphFeat brought to 6 candidate
redundants (to be manually reviewed). The third type of redundancy is relative to 9 “isolated”
entries, i.e. PhoUs missing an association with any MorU. Finally, the algorithm found 63 PhoUs
having the same naming and phono of other entries but that were referred to by different MorUs,
indicating another kind of redundancy.
We first introduce an example belonging to the first case of redundancy, and relative to the
two PhoUs representing the pronunciation of “liquefà” (liquefy, liquefies), which share the same
phonetic information. The algorithm identified a case of redundancy and carried on the discovery
process by taking into account their association with the MorU and the morphological features.


    30 The number of affected entities does not include the other updated data referring to them,
    such as arguments, predicates, templates, traits, linked to SemUs.
                                                   183
       Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023



As is evident from Table 7, associations 1 and 3 represent equal values in correspondence of
MorU, pos, and morphological features. For this reason, PhoU “liquefa2” and its association
with MorU “liquefare” were not transferred into LexicO.


                    MorU               pos       morphFeat                 PhoU
           1        liquefare          V         S2MP                      liquefa
           2        liquefare          V         S3IP                      liquefa
           3        liquefare          V         S2MP                      liquefa2

 Table 7: The association between the two PhoUs “liquefà” and MorU “liquefare” with the relative mor-
 phological features: “S2MP” stands for “singular second-person imperative present” while “S3IP” stands
                              for “singular third-person indicative present”.

Another type of case involved two phonological entries relative to “nocciòli” (hazelnut trees).
One of them (“noccioli2”) appeared “isolated” since it was not associated with any MorU; the
algorithm proceeded in the same way as for entry “liquefa2”. The last example is about the entry
“lèggi” ([you] read), which was present as two distinct PhoUs. These two entries (identified by
“leggi2” and “leggi3”) appeared associated with the same MorU “leggere”, but with distinct
morphological features. In this case, the algorithm removed the redundant PhoU “leggi3” and
modified the two forms of “leggere” in order to refer to “leggi2”. In any case, if the two considered
PhoUs had been associated with distinct MorUs, the algorithm would have acted in the same
way, since two distinct PhoUs representing the same word pronunciation are not allowed to be
present at the same time in the resource.


       Redundant semantic units
SemUs constituting the PSC resource are by far the richest in information among the four
available types of entries. For this reason, a simple query to the database was not sufficient to
identify candidate redundant SemUs. The algorithm for the discovery of redundant SemUs can
be found in the “Redundant Entries” section of the web interface. The interface also shows the
list of the discovered entries. Basically, the algorithm,31 as illustrated in Figure 5, considers a
SemU as a candidate redundant if it has a set of base characteristics of another SemU, namely,
same naming, pos, semantic traits, template, predicates, and semantic relations.




    31 The code implementing the algorithm, written in PERL, is available at https://github.com/
    klab-ilc-cnr/LexicO-scripts.
                                                 184
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




   Figure 5: The pseudocode of the algorithm for the discovery of redundant SemUs. Cases marked with
 status “15” are relative to sure redundant entries, which have not been included in LexicO. All the other
             cases have been included but will have to be manually checked by lexicographers.

A total of 41 SemUs have been discovered as (sure) redundant, si nce they shared the same
base characteristics plus identical example, definition, comment and associated SynU of another
SemU.
This step-by-step example documents the finding of a redundant SemU. In this case, the
discovered entry is a sure duplicate of another one and, consequently, it has not been exported
to LexicO. However, there are cases where all the attributes of two entries correspond except for
some: each case, as aforementioned, have been marked appropriately as a candidate redundant
to be manually verified.
The PSC database contained four SemUs (i.e. senses) relative to the word “abate” (abbot) and
having the same naming and pos. It standed out, on the basis of the presence of repeated values
in the definitions, that these SemUs could hide some redundant information.
The next step involved the comparison of the semantic relations attributed to each SemU: as
depicted in Table 8, senses 2 and 4 of “abate” had identical semantic relations.




                                                   185
       Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023



                source     definition                               relation         target
        1       abate      titolo onorifico di ecclesiastici ...    isa              uomo
                                                                    memberof         chiesa
        2       abate      superiore di una abbazia                 isa              uomo
                                                                    memberof         abbazia
        3       abate      superiore di una abbazia                 NULL             NULL
        4       abate      superiore di una abbazia                 isa              uomo
                                                                    memberof         abbazia

             Table 8: The semantic relations attributed to each SemU of the word “abate”

In the following step the algorithm found out that the two detected SemUs also shared the same
semantic traits, templates and predicates.
The last four characteristics considered by the algorithm were: i) the SynUs associated with the
analysed SemUs, ii) the definition, iii) the example, and iv) the comment. If two SemUs share
all these data they can be considered as identical, and one of them must be removed accordingly.
This was the case of the example here described: SemUs 2 and 4 were related to the same SynU
(i.e. “SYNUabateN”) and shared identical example, definition, and comment: as a consequence,
one of them was considered redundant and, thus, it was not exported to LexicO.


       Redundant syntactic units
To consider a SynU as a candidate redundant entry we required it to have, at least, the same
naming, pos, description, descriptionL, and framesetL equal to those of another SynU. In
addition, as we did for SemUs, on the basis of the sharing of other information the algorithm
considers the entries as candidate redundants to be subsequently verified. Four SynUs sharing
all data (i.e. in addition to the aforementioned base set of features, also comment, example,
associated MorU and SemU) of another SynU, have been removed as redundant.
The first example of redundant SynU discovery is relative to the entry “centesimo” (hundredth) with
pos “number”, represented by two distinct SynUs in the resource. The two SynUs shared the same
data, including the comment and SemU of reference: on the basis of the criteria followed by the
algorithm the entry “centesimo2”, as a sure redundant entry, was not exported from PSC to LexicO.
Another example is relative to the entry “querceto” (oak forest). In this case, the two analysed
SynUs, namely “querceto” and “querceto2”, differed on the associated SemUs: the algorithm thus
assigned to “querceto2” a status of candidate redundant to be verified, meaning that this entry
should be double-checked by a lexicographer.


       Redundant morphological units
The last case of automatic discovery of possible redundant entries is relative to MorUs. An in
depth analysis of these elements and their relations revealed two kinds of redundancy. The first
case concerns MorUs with the same naming, pos, ginp, and PhoUs of reference; the algorithm

                                                 186
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




found a total of 36 entries of this kind.
An example is relative to the entry “maltese”, which appeared, as a noun, in three distinct MorUs.
The algorithm focussed on the two of them which shared the same ginp, then it looked for the
PhoUs they were associated to. The two MorUs referred to the same PhoUs: for this reason one
of them was excluded from LexicO.
The second case is relative to a critical case of “disconnection” between SynUs and PhoUs, in
which two MorUs share all data but one of them has no ginp. An example of disconnection,
related to the entry “longevità” (longevity), is shown in Figure 6: the MorU with ginp was linked
to a PhoU, but it was not referenced by any SynU; on the other hand, the MorU without ginp
was linked to a SynU, but to no PhoUs.




 Figure 6: An example of “disconnection” between the syntactic and phonological layer caused by the pre-
sence of redundant morphological information related to lemma “longevità”; the figure represents the data
                                      before and after the update.

In this case, the algorithm proceeded with the removal of the MorU without ginp and with the
update of the SynU to refer to the MorU with ginp.


        Redundant and missing associations
In addition to the introduced cases of redundant entries, a thorough analysis of the resource
showed also the presence of i) redundant associations between MorUs and PhoUs (denoting
inflected forms), ii) between SemUs and SynUs, and iii) redundant and missing associations
between SemUs (semantic relations). Regarding the first and second type of associations, 24
pairs MorU/PhoU and 710 pairs SemU/SynU were recognized as redundant and, thus, not
transferred into LexicO. As regards the third type of associations, the algorithm detected 10
redundant relations that, therefore, were not included in LexicO. The management of erroneous
and missing relations related to synonymy, meronymy, and holonymy will be detailed in the
following two subsections.


        Synonymy
The analysis of synonymy showed two distinct situations, which we faced and solved in the
following order:
i) reflexive synonyms: 9 cases of redundant synonymic relations were found that matched the
pattern “A is a synonym of A” (e.g. “USem75081buttare” synonymous of itself ); these relation
instances have been removed;
ii) asymmetric synonymy: 1645 relation instances were found as missing, since in the face of a
                                                  187
       Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023



pattern of “A is a synonym of B” the resource lacked the inverse relation “B is a synonym of A”;
these missing instances were added to LexicO.
We also found some cases of erroneous synonymic data, such as between senses of words
“solito” (usual) and “insolito” (unusual). However, the fixing of these cases will need the manual
intervention of a lexicographer.


       Meronymy and holonymy
Meronymy and holonymy are represented, in PSC, with four different relations; in fact,
meronymy is distinguished in two values, as “Is a part of ”, to indicate a part of a set (e.g. “leaf ”
as part of a “crown of a tree”) and “Is a member of ”, to indicate the individual in a group (e.g.
“senator” as part of the “senate”). In the same way, the resource describes two kinds of holonymy:
“Has as part”, as the inverse relation of “Is a part of ” and “Has a member”, as inverse of “Is a
member of ”. The analysis of these relations presented situations similar to synonymy. Given
the pattern “A is the meronym of B”, the resource does not always present the inverse relation,
represented by the pattern “B is a holonym of A”.
The column “PSC” of Table 9 contains the number of synonymic, meronymic and holonymic
relations. Regarding meronymy, 1699 instances of relation “Is a part of ” and 531 instances of
“Is a member of ” lacked the corresponding holonymic associations (for a total of 2230 missing
associations). To bring an example, “riccio” (bur) was indicated as a meronym of “castagna”
(chestnut), but this latter did not appear as a holonym of “riccio”.
In the same way, 1365 instances of “Has as part” and 678 instances of “Has a member” did
not present the corresponding meronymic relation (for a total of 2043 missing associations).
For example, “monastero” (monastery) was present as a holonym of “monaco” (monk), but the
inverse meronymic relation was not included in PSC. All these missing instances have been
added to LexicO: the column “LexicO” of Table 9 indicates the final number of relations, while
the rightmost column shows the number of affected instances.32




    32 The DB table describing semantic relations also includes, for each association, a reference to
    the template of the source SemU and the indication of a “semantic weight” with two possible values:
    prototypical and essential. For the addition of missing inverse relations into LexicO the intervention
    of a lexicographer is required to define the semantic weight, and thus the relative field was, during
    the migration into the new resource, filled with a “NULL” value: the attribution of the correct values
    will be part of future versions of the resource. Furthermore, 43 semantic relations did not present
    the information of the source SemU’s template: for the above mentioned reason, these relations have
    been moved in LexicO with a “NULL” value for the template, that will be added in future updates.
                                                  188
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




                                                      PSC        LexicO        affected

        synonymy                                      4062       5697          1655

        meronymy               Is a part of           1803       3167          1364

                               Is a member of         642        1318          679

        holonymy               Has a part             1468       3167          1700

                               Has a member           789        1318          529

Table 9: The number of synonymic and meronymic relations in PSC and in LexicO, i.e. before and after
               the analysis and fixing process, and the number of affected associations.




       Conclusions and perspectives

This paper describes the analysis of a portion of the Italian computational lexicon PSC and the
consequent fixing of a set of data. From these revised data we started the construction of a new
computational lexicon, grounded on PSC, and called LexicO. After an introduction to related
works, some examples of use of PSC in a context of full-text search, a description of its underlying
model and a quantification of current data, the paper has focussed on the analytical steps taken to
unearth redundancies, erroneous and missing data, with the strategies adopted to fix them. While
a number of duplicate and missing entries has been already detected and processed accordingly,
another set of potential redundant entries has been marked for further manual revisions.
To assess the analysis carried out on PSC an ad hoc web interface has been developed through
which it is possible to execute pre-compiled queries and analyse both the coverage and all the
entries affected by the described algorithms.
On the basis of the conducted analysis it has been possible to make some considerations on
current strengths and weaknesses of PSC. First of all, this resource represents the richest and
most structured freely available computational lexicon for the Italian language: its multilayered
nature, the huge number of relations forming a strongly interconnected network among all the
different levels, and the already available linguistic data, make PSC a unique resource that is
worth updating, enriching, and sharing.
Regarding issues, though we considered just a few of the available semantic relations, this kind of
data appears as one of the most problematic: as shown in 9, for example, we had to double the
number of meronymic relations in LexicO by adding those that were missing. On the other hand,
concerning entries, just a few semantic and phonological units were found as clearly redundant,
while morphological and syntactic units would need more attention.
Focussing on a more specific content and considering data with which each entry is described,
the analysis highlighted some interesting facts. For example, 16.15% of SemUs do not have
a definition (which may cause interpretation difficulties to users browsing the lexicon) while

                                                189
       Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023



15.7% of them have no semantic relation (which, in turn, may pose problems to NLP algorithms
trying to take advantage from the semantic layer of the resource). We plan to continue the
development of LexicO by starting from the following lines of intervention.
Firstly, all the candidate redundant entries will have to be manually examined, and, if needed,
corrected, by lexicographers; moreover, we will experiment further algorithmic strategies to
discover other critical data. Furthermore, the lexicon, currently stored in a relational database, will
be converted into an RDF graph database, which may better represent the network-like nature
of the resource and also ease and speed up its querying. At the same time, the lexicon will be
made adherent to the specifications of OntoLex-Lemon model, which is natively compliant with
the principles of Linguistic Linked Open Data (LLOD); we expect that in order to preserve the
peculiarities and specific phenomena of the linguistic model inherited from PSC the OntoLex-
Lemon model will have to be extended. This new resource will be made available on CLARIN
repository as well. We also plan to link the new lexicon with other resources, starting from
BabelNet. Finally, to share the resource in the community of lexicographers, and enable them to
collaboratively work in the correction and enrichment of the lexicon, we intend to arrange and
start up a dedicated project involving the most authoritative institutions in the field of Italian
lexicography.
The availability of a renewed and updated Italian computational lexicon, especially in an easily
shareable LLOD compliant format, could bring considerable benefits in terms of impact in all
digital contexts involving the linguistic and semantic based access to texts and in the support of
NLP tasks, such as word sense disambiguation. More in general, despite its current redundancies
and gaps, especially if further fixed and enriched, LexicO may represent a computational lexicon
of reference for all communities working with resources for the Italian language.



       Data Availability Statement

All data involved in this work are openly available at the following locations: i) the PSC resource
in ILC4CLARIN repository at http://hdl.handle.net/20.500.11752/ILC-88, ii) the LexicO
resource in ILC4CLARIN repository at http://hdl.handle.net/20.500.11752/ILC-977, iii) the
web interface for the evaluation of PSC’s critical entries on KLAB group website at https://klab.
ilc.cnr.it/PSC-critical-entries, iv) the code of the interface in KLAB group GitHub repository
at https://github.com/klab-ilc-cnr/PSC-critical-entries and, v) the scripts and the queries
implementing the algorithms in KLAB group GitHub repository at https://github.com/klab-ilc-
cnr/LexicO-scripts.



       Funding

This work was supported by the TALMUD project and carried out within the scientific
collaboration between S.c.a r.l. PTTB and CNR-ILC (09/11/2017).



                                                 190
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




       References

       [1]. Leoni, Federico Albano. 2007. “Un frammento di storia recente della ricerca (lin-
       guistica) italiana. Il corpus CLIPS.” Bollettino d’Italianistica, n.s., IV, 2: 122-130. https://
       doi.org/10.7367/71826.
       [2]. Basili, Roberto, Brambilla, Silvia, Croce, Danilo, and Tamburini, Fabio. 2017. “De-
       veloping a Large Scale FrameNet for Italian: the IFrameNet Experience.” In Proceedings of
       the Fourth Italian Conference on Computational Linguistics (CLiC-it 2017), AAccademia
       University Press. https://doi.org/10.4000/books.aaccademia.2364.
       [3]. Bevilacqua, Michele, and Navigli, Roberto. 2020. “Breaking Through the 80%
       Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorpora-
       ting Knowledge Graph Information.” In Proceedings of the 58th Annual Meeting of the As-
       sociation for Computational Linguistics, 2854–2864. https://doi.org/10.18653/v1/2020.
       acl-main.255.
       [4]. Calzolari, Nicoletta. 2003. “Risorse Linguistiche per la lingua italiana scritta.” in
       Conferenza TIPI-Tecnologie Informatiche nella Promozione della Lingua Italiana, Roma.
       [5]. Chiari, Isabella. 2012a. “Il dato empirico in lessicografia: dizionari tradizio-
       nali e collaborativi a confronto.” Bollettino di italianistica, 2: 94-125. https://doi.
       org/10.7367/72622.
       [6]. Chiari, Isabella. 2012b. “Linguistic resources and machine translation trends for the
       Italian language: overview and perspectives” In Language Translation Automation Confe-
       rence (LTAC), edited by Cannavina, Valeria and Fellet, Anna, The Big Wave, 105-123.
       [7]. Fellbaum, Christiane. 1998. WordNet: An Electronic Lexical Database. Cambridge
       (Mass.):MIT Press. https://doi.org/10.7551/mitpress/7287.001.0001.
       [8]. Fersini, Elisabetta, Anzovino, and Rosso. 2018. “Overview of the task on automatic
       misogyny identification at ibereval. In Proceedings of the Third Workshop on Evaluation
       of Human Language Technologies for Iberian Languages (IberEval 2018)”. CEUR Wor-
       kshop Proceedings. Seville: CEUR-WS. org.
       [9]. Del Gratta, Riccardo, Frontini, Francesca, Khan, Fahad, and Monachini, Monica.
       2015. “Converting the PAROLE SIMPLE CLIPS lexicon into RDF with lemon.” Se-
       mantic Web, 6, 4: 387–392. https://doi.org/10.3233/SW-140168.
       [10]. Giovannetti, Emiliano, Albanesi, Davide, Bellandi, Andrea, and Benotto, Giulia.
       2017. “Traduco: A collaborative web-based CAT environment for the interpretation and
       translation of texts.” Digital Scholarship in the Humanities, 32, suppl_1: 47-62. https://
       doi.org/10.1093/llc/fqw054.
       [11]. Giovannetti, Emiliano, Albanesi, Davide, Bellandi, Andrea, Marchi, Simone,
       Papini, Mafalda, and Sciolette, Flavia. 2022. “The role of a computational lexicon for
       query expansion in full-text search.” In Proceedings of CLiC-it 2021: Italian Conference
       on Computational Linguistics, CEUR workshop proceedings. https://doi.org/10.4000/
       books.aaccademia.10638.
       [12]. Grella, Matteo. 2018. Italian Content Words v3, LINDAT/CLARIAH-CZ digital
       library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathemati-
       cs and Physics, Charles University. http://hdl.handle.net/11372/LRT-2894.
       [13]. Grella, Matteo, 2018, Italian Function Words v3, LINDAT/CLARIAH-CZ digi-
       tal library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathe-
                                                191
Umanistica Digitale – ISSN: 2532-8816 – n. 15, 2023



matics and Physics, Charles University, http://hdl.handle.net/11372/LRT-2893.
[14]. Jiang, Yuncheng. 2020. “Semantically-enhanced information retrieval using mul-
tiple knowledge sources.” Cluster Computing 23, 4: 2925–2944. https://doi.org/10.1007/
s10586-020-03057-7.
[15]. Jindal, Rajni and Shweta, Taneja. 2018. “A Novel Method for Efficient Multi-La-
bel Text Categorization of research articles”. In International Conference on Computing,
Power and Communication Technologies (GUCON), 333-336. https://doi.org/10.1109/
GUCON.2018.8674985.
[16]. Khan, Fahad, Bellandi, Andrea, Frontini, Francesca, and Monica Monachini.
2018. “One Language to rule them all: Modelling Morphological Patterns in a Large
Scale Italian Lexicon with SWRL.” In Proceedings of the 11th International Conference on
Language Resources and Evaluation - LREC2018, Miyazaki, Japan. https://aclanthology.
org/L18-1694.
[17]. Lenci, Alessandro, Bel, Nuria, Busa, Federica, Calzolari, Nicoletta, Gola, Elisa-
betta, Monachini, Monica, Ogonowski, Antoine, Peters, Ivonne, Peters, Wim, Ruimy,
Nilda, Villegas, Marta, and Zampolli, Antonio. 2000. “SIMPLE: A General Framework
for the Development of Multilingual Lexicons”. International Journal of Lexicography 13,
4: 249–263. https://doi.org/10.1093/ijl/13.4.249.
[18]. McCarthy, Arya D., Kirov, Christo, Grella, Matteo, Nidhi, Amrit, Xia, Patrick,
Gorman, Kyle Vylomova, Ekaterina, Mielke, Sabrina J., Nicolai, Garrett, Silfverberg,
Miikka, Arkhangelskiy, Timofey, Krizhanovsky, Nataly, Krizhanovsky, Andrew, Klya-
chko, Elena, Sorokin, Alexey, Mansfield, John, Ernštreits, Valts, Pinter, Yuval, L. Jacobs,
Cassandra, et al. 2020. “UniMorph 3.0: Universal Morphology.” In Proceedings of the
Twelfth Language Resources and Evaluation Conference, Marseille, 3922–3931. https://doi.
org/10.3929/ethz-b-000462327.
[19]. Monachini, Monica, Calzolari, Federico, Mammini Michele, Rossi Sergio, and
Ulivieri, Marisa. 2004. “Unifying Lexicons in view of a Phonological and Morphological
Lexical DB” In Proceedings of the Fourth International Conference on Language Resources
and Evaluation (LREC04), Lisbona, European Language Resources Association (ELRA),
1107-1110.
[20]. Navigli, Roberto, and Pozzetto, Simone Paolo. 2012. “BabelNet: The auto-
matic construction, evaluation and application of a wide-coverage multilingual se-
mantic network.” Artificial Intelligence, 193: 217-250. https://doi.org/10.1016/j.ar-
tint.2012.07.001.
[21]. Pianta, Emanuele, Bentivogli, Luisa, and Girardi, Christian. 2002. “MultiWord-
Net: developing an aligned multilingual database.” In Proceedings of the First International
Conference on Global WordNet, 293-302.
[22]. Poli, Francesca. 2021. Italian Sense Inventory. ILC-CNR for CLARIN-IT reposi-
tory hosted at Institute for Computational Linguistics “A. Zampolli”, National Research
Council. http://hdl.handle.net/20.500.11752/OPEN-5
[23]. Pustejovsky, James. 1995. The Generative Lexicon. Cambridge, MA: MIT Press.
https://doi.org/10.7551/mitpress/3225.001.0001.
[24]. Roventini, Adriana, Alonge, Antonietta, Bertagna, Francesca, Calzolari, Nicoletta,
Cancila, Jean, Girardi, Magnini, Bernardo, Marinelli, Rita, Speranza, Manuela, and Zam-
polli, Antonio. 2003. “ItalWordNet: building a large semantic database for the automatic

                                        192
F. Sciolette, S. Marchi, E. Giovannetti– LexicO: an Italian Computational Lexicon




       treatment of Italian.” In Computational Linguistics in Pisa. Edited by Zampolli Antonio,
       Calzolari, Nicoletta L. Cignoni. Linguistica Computazionale, Special Issue, 745-791.
       [25]. Ruimy, Nilda, Monachini, Monica, Distante, Raffaella, Guazzini, Elisabetta, Mo-
       lino, Stefano, Ulivieri Marisa, Calzolari, Nicoletta, and Zampolli, Antonio. 2002. “Clips,
       a multi-level italian computational lexicon: A glimpse to data.” In Proceedings of the Third
       International Conference on Language Resources and Evaluation (LREC02).
       [26]. Ruimy, Nilda. 2010. “Simple_PLUS: a network of lexical semantic rela-
       tions.” Procesamiento del Lenguaje Natural, 44: 99-106. https://www.redalyc.org/
       pdf/5157/515751744018.pdf.
       [27]. Sabatini, Francesco. 2011. “La storia dell’italiano nella prospettiva della corpus
       linguistics.” in I dialetti, il latino, modelli teorici, la Crusca, l’Europa. Saggi dal 1968 al
       2009, edited by; Coletti, V., Coluccia, R., D’Achille, P., De Blasi, N., & Proietti,, Napoli:
       Liguori editore, II: 223-232.
       [28]. Vieu, Laure, Jezek, Elisabetta and van de Cruys, Tim. 2015. “Quantitative
       methods for identifying systematic polysemy classes.” In 6th Conference on Quantitative
       Investigations in Theoretical Linguistics (QITL 2015), Tübingen, 1-5.




                                                193
