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Questo contributo si propone di presentare il progetto TrAVaSI (Trattamento Automatico di Varietà Storiche di Italiano), il cui obiettivo è la creazione di risorse per il trattamento automatico di varietà storiche della lingua italiana, in particolare lessici diacronici e corpora arricchiti con annotazione linguistica da utilizzare per lo sviluppo e/o la specializzazione di strumenti di annotazione. Il contributo illustra gli obiettivi, i primi risultati conseguiti e le prospettive di sviluppo. The paper is aimed at illustrating the TrAVaSI project, whose aim is the design and development of language resources for the automatic processing of historical varieties of the Italian language, in particular diachronic lexicons and corpora enriched with linguistic annotation to be used for the development and/or specialization of annotation tools. The results achieved so far are reported together with current and future directions of research.

We present the European Clinical Case Corpus (E3C) project, aimed at collecting and annotating a large corpus of clinical cases in five European languages (Italian, English, French, Spanish, and Basque). Project results include: (i) a freely available collection of multilingual clinical cases; and (ii) a two-level annotation scheme based on temporal relations (derived from THYME), whose purpose is to allow the construction of clinical timelines, and taxonomy relations based on medical taxonomies, to be used for semantic reasoning over clinical cases.

Il contributo presenta una valutazione delle prestazioni di DeepL nella traduzione di testi specialistici dall’inglese all’italiano. La valutazione è stata condotta a livello di frase, su un campione di 108 frasi tratte da testi relativi ad ambiente, energia, biomedicina e discipline del farmaco, e le traduzioni prodotte sono state valutate da traduttori in formazione dotati di competenze disciplinari. La traduzione di DeepL ha ottenuto una valutazione statisticamente pari a quella della traduzione umana per quanto riguarda l’adeguatezza e leggermente inferiore per quanto riguarda la scorrevolezza. La traduzione automatica dei testi ha inoltre ricevuto un punteggio superiore a quello ottenuto, con modalità simili, dalla traduzione automatica di testi giornalistici. The paper presents an evaluation of the performance of DeepL in the translation of specialized texts from English to Italian. The evaluation was carried out at sentence level, on a sample of 108 sentences taken from texts relating to the environment, energy, bio-medicine and drug science, and the translations produced were evaluated by translators in training, with disciplinary skills. The translation by DeepL was statistically rated at the same level of human translation in terms of adequacy and slightly lower in terms of fluency. Machine translation of the texts also received a higher score than that obtained in another analysis, carried out in a similar way, by machine translation of journalistic texts.

Existing research on Authorship Attribution (AA) focuses on texts for which a lot of data is available (e.g novels), mainly in English. We approach AA via Authorship Verification on short Italian texts in two novel datasets, and analyze the interaction between genre, topic, gender and length. Results show that AV is feasible even with little data, but more evidence helps. Gender and topic can be indicative clues, and if not controlled for, they might overtake more specific aspects of personal style.

While text-only datasets are widely produced and used for research purposes, limitations set by image-based social media platforms like Instagram make it difficult for researchers to experiment with multimodal data. We therefore developed CREENDER, an annotation tool to create multimodal datasets with images associated with semantic tags and comments, which we make freely available under Apache 2.0 license. The software has been extensively tested with school classes, allowing us to improve the tool and add useful features not planned in the first development phase. Mentre i dataset testuali sono ampiamenti creati e usati per scopi di ricerca, le limitazioni imposte dai social media basati sulle immagini (come Instagram) rendono difficile per i ricercatori sperimentare con dati multimodali. Abbiamo quindi sviluppato CREENDER, un tool di annotazione per la creazione di dataset multimodali in cui immagini vengono associate a etichette semantiche e commenti, e che abbiamo reso disponibile gratuitamente con la licenza Apache 2.0. Il software è stato testato in un laboratorio con alcune classi scolastiche, permettendoci di ottimizzare alcune procedure e di aggiungere feature non previste nella prima release.

Modern personal assistants require to access unstructured information in order to successfully fulfill user requests. In this paper, we have studied the use of two machine learning components to design personal assistants: intent classification, to understand the user request, and answer sentence selection, to carry out question answering from unstructured text. The evaluation results derived on five different real-world datasets, associated with different companies, show high accuracy for both tasks. This suggests that modern QA and dialog technology is effective for real-world tasks.

We present a new dataset of sentences extracted from the movie Forrest Gump, annotated with the emotions perceived by a group of subjects while watching the movie. We run experiments to predict these emotions using two classifiers, one based on a Support Vector Machine with linguistic and lexical features, the other based on BERT. The experiments showed that contextual embeddings are effective in predicting human-perceived emotions.

We explore linguistic features that contribute to sarcasm detection. The linguistic features that we investigate are a combination of text and word complexity, stylistic and psychological features. We experiment with sarcastic tweets with and without context. The results of our experiments indicate that contextual information is crucial for sarcasm prediction. One important observation is that sarcastic tweets are typically incongruent with their context in terms of sentiment or emotional load.

This paper presents the results of an evaluation of Google Translate, DeepL and Bing Microsoft Translator with reference to natural gender translation and provides statistics about the frequency of female, male and neutral forms in the translations of a list of personality adjectives, and nouns referring to professions and bigender nouns. The evaluation is carried out for EnglishSpanish, EnglishItalian and EnglishFrench.

We study how language use differs between dialogue partners in a visually grounded reference task when a referent is mutually identifiable by both interlocutors vs. when it is only available to one of them. In the latter case, the addressee needs to disconfirm a proposed description – a skill largely neglected by both the theoretical and the computational linguistics communities. We consider a number of linguistic features that we expect to vary across conditions. We then analyze their effectiveness in distinguishing among the two conditions by means of statistical tests and a feature-based classifier. Overall, we show that language mirrors different grounding conditions, paving the way to future deeper investigation of referential disconfirmation.

Ostracism is a community-level phenomenon, shared by most social animals, including humans. Its detection plays a crucial role for the individual, with possible evolutionary consequences for the species. Considering (1) its bound with communication and (2) its social nature, we hypothesise the combination of (a) linguistic and (b) community-level features to have a positive impact on the automatic recognition of ostracism in human online communities. We model an English linguistic community through Reddit data and we analyse the performance of simple classification algorithms. We show how models based on the combination of (a) and (b) generally outperform the same architectures when fed by (a) or (b) in isolation.

The difficulty in finding useful dialogic data to train a conversational agent is an open issue even nowadays, when chatbots and spoken dialogue systems are widely used. For this reason we decided to build JILDA, a novel data collection of chat-based dialogues, produced by Italian native speakers and related to the job-offer domain. JILDA is the first dialogue collection related to this domain for the Italian language. Because of its collection modalities, we believe that JILDA can be a useful resource not only for the Italian research community, but also for the international one. Negli ultimi anni l’utilizzo di chatbot e sistemi dialogici è diventato sempre più comune; tuttavia, il reperimento di dati di apprendimento adeguati per addestrare agenti conversazionali costituisce ancora una questione irrisolta. Per questo motivo abbiamo deciso di produrre JILDA, un nuovo dataset di dialoghi relativi al dominio della ricerca del lavoro e realizzati via chat da parlanti nativi italiani. JILDA costituisce la prima collezione di dialoghi relativi a questo dominio, in lingua italiana. Per gli aspetti metodologici e la modalità di raccolta dei dati, riteniamo che una simile risorsa possa essere utile ed interessante non solo per la comunità di ricerca italiana ma anche per quella internazionale.

The present paper aims to investigate the nature and the extent of cross-linguistic phonosemantic correspondences within a computational framework. An LSTM-based Recurrent Neural Network is trained to associate the phonetic representation of a word, encoded as a sequence of feature vectors, to its corresponding semantic representation in a multilingual vector space. The processing network is tested, without further training, in a language that does not appear in the training set. The performance of the multilingual model is compared with a monolingual upper bound and a randomized baseline. After the quantitative evaluation of its performance, a qualitative analysis is carried out on the network’s most effective predictions, showing an inhomogeneous distribution of phonosemantic information in the lexicon, influenced by semantic, syntactic, and pragmatic factors.

In this paper, we present a novel dataset composed of images and comments in Italian, created with teenagers in classes using a simulated scenario to raise awareness on cyberbullying phenomena. Potentially offensive comments have been collected for more than 1,000 images and manually assigned to a semantic category. Our analysis shows that the presence of human subjects, as well as the gender of the people present in the pictures trigger different types of comment, and provides novel insight into the connection between images posted on social media and offensive messages. We also compare our corpus with a similar one obtained with WhatsApp, showing that comments to images show different characteristics compared to text-only interactions.

This paper presents a new topic modelling framework inspired by game theoretic principles. It is formulated as a normal form game in which words are represented as players and topics as strategies that the players select. The strategies of each player are modelled with a probability distribution guided by a utility function that the players try to maximize. This function induces players to select strategies similar to those selected by similar players and to choice strategies not shared with those selected by dissimilar players. The proposed framework is compared with state-of-the-art models demonstrating good performances on standard benchmarks. Questo articolo presenta un approccio di modellazione dei topic ispirato alla teoria dei giochi. La modellazione dei topic è vista come un gioco in forma normale in cui le parole rappresentano i giocatori e i topic le strategie che i giocatori possono scegliere. Ogni giocatore sceglie le strategie da impiegare tramite una distribuzione di probabilità che viene influenzata da una funzione di utilità che i giocatori cercano di massimizzare. Questa funzione incentiva i giocatori a scegliere strategie simili a quelle impiegate da giocatori simili e disincentiva la scelta di strategie condivise con giocatori dissimili. Il confronto con modelli allo stato dell’arte dismostra buone prestazioni su diversi dataset di valutazione.

In theatrical pieces, written language is the primary medium for establishing antagonisms. As one of the most important figures of renaissance, Shakespeare wrote characters which express themselves clearly. Thus, the emotional landscape of the plays can be revealed from the texts. It is important to analyze such landscapes for further demonstrating these structures.We use word-emotion association lexicon with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive). By using this lexicon, the emotional state of each character is represented in 10 dimensional space and mapped onto a plane. This principle axes planes position each character relatively. Additionally, tempora-emotional evaluation of each play is graphed.We conclude that the protagonist and the antagonist have different emotional states from the rest and these two emotionally oppose each other. Temporal-Emotional timeline of the plays are meaningful to have a better insight into the tragedies.

This paper presents the results of research carried out on the UNIOR Eye corpus, a corpus which has been built by downloading tweets related to environmental crimes. The corpus is made up of 228,412 tweets organized into four different subsections, each one concerning a specific environmental crime. For the current study we focused on the subsection of waste crimes, composed of 86,206 tweets which were tagged according to the two labels alert and no alert. The aim is to build a model able to detect which class a tweet belongs to.

Transfer learning has been proven to be effective, especially when data for the target domain/task is scarce. Sometimes data for a similar task is only available in another language because it may be very specific. In this paper, we explore the use of machine-translated data to transfer models on a related domain. Specifically, we transfer models from the question duplication task (QDT) to similar FAQ selection tasks. The source domain is the well-known English Quora dataset, while the target domain is a collection of small Italian datasets for real case scenarios consisting of FAQ groups retrieved by pivoting on common answers. Our results show great improvements in the zero-shot learning setting and modest improvements using the standard transfer approach for direct in-domain adaptation.

The Italian Sign Language (LIS) is the natural language used by the Italian Deaf community. This paper discusses the application of the Universal Dependencies (UD) format to the syntactic annotation of a LIS corpus. This investigation aims in particular at contributing to sign language research by addressing the challenges that the visual-manual modality of LIS creates generally in linguistic annotation and specifically in segmentation and syntactic analysis. We addressed two case studies from the storytelling domain first segmented on the ELAN platform, and second syntactically annotated using CoNLL-U format.

In this paper, we present our approach to the task of binary sentiment classification for Italian reviews in healthcare domain. We first collected a new dataset for such domain. Then, we compared the results obtained by two different systems, one including a Support Vector Machine and one with BERT. For the first one, we linguistic pre–processed the dataset to extract hand-crafted features exploited by the classifier. For the second one, we over-sampled the dataset to achieve better results. Our results show that the SVM-based system, without the worry of having to oversample, has better performance than the BERT-based one, achieving an F1-score of 91.21%.

In this paper, we present a method based on Linear Discriminant Analysis for legal text classification of extremely noisy data, such as duplicated documents classified in different classes. The results show that Linear Discriminant Analysis obtains very good performances both in clean and noisy conditions, if used as classifier in ensemble learning and in multi-label text classification.

The aim of this research was to create the first Italian corpus of free conversations between healthcare professionals and people with dementia, in order to investigate specific linguistic phenomena from a computational point of view. Most of the previous researches on speech disorders of people with dementia have been based on qualitative analysis, or on the study of a few dozen cases executed in laboratory conditions, and not in spontaneous speech (in particular for the Italian language). The creation of the Corpus Anchise 320 aims to investigate Dementia language by providing a broader number of dialogues collected in ecological conditions. Automatic linguistic analysis can help healthcare professionals to understand some characteristics of the language used by patients and to implement effective dialogue strategies.

In this paper we discuss the role of natural language generation (NLG) in modern dialogue systems (DSs). In particular, we will study the role that a linguistically sound NLG architecture can have in a DS. Using real examples from a new corpus of dialogue in customer-care domain, we will study how the non-linguistic contextual data can be exploited by using NLG.

In this paper we define the creativity embedding of a text based on four self-assessment creativity metrics, namely diversity, novelty, serendipity and magnitude, knowledge graphs, and neural networks. We use as basic unit the notion of triple (head, relation, tail). We investigate if additional information about creativity improves natural language processing tasks. In this work, we focus on triple plausibility task, exploiting BERT model and a WordNet11 dataset sample. Contrary to our hypothesis, we do not detect increase in the performance.

In this paper we address the main issues and results of a research thesis (Romani, 2020) dedicated to the annotation of metonymies in T-PAS, a corpus-based digital repository of Italian verbal patterns (Ježek et al., 2014). The annotation was performed on the corpus instances of a selected list of 30 verbs and was aimed at both implementing the resource with metonymic patterns and identifying and creating a map of the metonymic relations that occur in the verbal patterns. The annotated corpus data (consisting of 1218 corpus instances), the patterns, and the relations can be useful for NLP tasks such as metonymy recognition.

The recent introduction of Transformers language representation models allowed great improvements in many natural language processing (NLP) tasks. However, if on one hand the performances achieved by this kind of architectures are surprising, on the other their usability is limited by the high number of parameters which constitute their network, resulting in high computational and memory demands. In this work we present BERTino, a DistilBERT model which proposes to be the first lightweight alternative to the BERT architecture specific for the Italian language. We evaluated BERTino on the Italian ISDT, Italian ParTUT, Italian WikiNER and multiclass classification tasks, obtaining F1 scores comparable to those obtained by a BERTBASE with a remarkable improvement in training and inference speed. La recente introduzione dei Transformers come modelli di rappresentazione del linguaggio naturale ha permesso grandi avanzamenti sullo stato dell’arte in molte applicazioni di Natural Language Processing (NLP). Tuttavia, se da una parte i risultati raggiunti da queste architetture sono sorprendenti, dall’altra la loro fruibilità è limitata dall’elevato numero di parametri che costituiscono la loro architettura, con conseguenti elevate esigenze computazionali e di memoria. In questo lavoro presentiamo BERTino, un modello DistilBERT che è la prima alternativa leggera all’architettura BERT specifica per la lingua italiana. Abbiamo valutato BERTino sui task ISDT italiano, ParTUT italiano, WikiNER italiano e classificazione multiclasse, ottenendo punteggi F1 paragonabili a quelli ottenuti da un modello BERTBASE con un notevole miglioramento nella velocità di addestramento e inferenza.

This paper presents a new linguistic resource for Italian, called MultiEmotions-It, containing comments to music videos and advertisements posted on YouTube and Facebook. These comments are manually annotated according to four different dimensions: i.e., relatedness, opinion polarity, emotions and sarcasm. For the annotation of emotions we adopted the Plutchik’s model taking into account both basic and complex emotions, i.e. dyads.

Computational research on mental health disorders from written texts covers an interdisciplinary area between natural language processing and psychology. A crucial aspect of this problem is prevention and early diagnosis, as suicide resulted from depression being the second leading cause of death for young adults. In this work, we focus on methods for detecting the early onset of depression from social media texts, in particular from Reddit. To that end, we explore the eRisk 2018 dataset and achieve good results with regard to the state of the art by leveraging topic analysis and learned confidence scores to guide the decision process.

This research investigates Instagram users’ sentiment narrated during the lockdown period in Italy, caused by the COVID-19 pandemic. The study is based on the analysis of all the posts published on Instagram under the hashtag #andràtuttobene on May 4, May 18 and June 3, 2020. Our research carried out a view on a national, regional and provincial scale. We analyzed all the different languages and forms (i.e. captions, hashtags, emojis and images) that constitute the posts. The aim of this research is to provide a set of procedures revealing the different polarity trends for each kind of expression and to propose a single comprehensive measure.

In the last few years, pre-trained neural architectures have provided impressive improvements across several NLP tasks. Still, generative language models are available mainly for English. We develop GePpeTto, the first generative language model for Italian, built using the GPT-2 architecture. We provide a thorough analysis of GePpeTto's quality by means of both an automatic and a human-based evaluation. The automatic assessment consists in (i) calculating perplexity across different genres and (ii) a profiling analysis over GePpeTto's writing characteristics. We find that GePpeTto's production is a sort of bonsai version of human production, with shorter but yet complex sentences. Human evaluation is performed over a sentence completion task, where GePpeTto's output is judged as natural more often than not, and much closer to the original human texts than to a simpler language model which we take as baseline.

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges.

This paper presents a new corpus for the Italian language representative of the fanfiction genre. It comprises about 55k user-generated stories inspired to the original fantasy saga “Harry Potter” and published on a popular website. The corpus is large enough to support data-driven investigations in many directions, from more traditional studies on language variation aimed at characterizing this genre with respect to more traditional ones, to emerging topics in computational social science such as the identification of factors involved in the success of a story. The latter is the focus of the presented case-study, in which a wide set of multi-level linguistic features has been automatically extracted from a subset of the corpus and analysed in order to detect the ones which significantly discriminate successful from unsuccessful stories

Lexicalizing compounds, in addition to treating them dynamically, is a key element in giving us idiomatic translations and detecting compound errors. We present and evaluate an e-dictionary (NDS) and a grammar checker (GramDivvun) for North Sámi. We achieve a coverage of 98% for NDS-queries and of 96% for compound error detection in GramDivvun. La lessicalizzazione delle parole composte, in aggiunta a trattarle in maniera dinamica, è un elemento chiave per ottenere traduzioni idiomatiche e rilevare errori nelle stesse. Presentiamo e valutiamo un e-dizionario (NDS) e un correttore grammaticale (GramDivvun) per il Sami del Nord. Otteniamo una copertura del 98% per le ricerche in NDS e del 96% per il rilevamento di errori nelle parole composte in GramDivvun.

Data augmentation has shown potential in alleviating data scarcity for Natural Language Understanding (e.g. slot filling and intent classification) in task-oriented dialogue systems. As prior work has been mostly experimented on English datasets, we focus on five different languages, and consider a setting where limited data are available. We investigate the effectiveness of non-gradient based augmentation methods, involving simple text span substitutions and syntactic manipulations. Our experiments show that (i) augmentation is effective in all cases, particularly for slot filling; and (ii) it is beneficial for a joint intent-slot model based on multilingual BERT, both for limited data settings and when full training data is used.

As highlighted by Pustejovsky (1995, 2002), the semantics of each verb is determined by the totality of its complementation patterns. Arguments play in fact a fundamental role in verb meaning and verbal polysemy, thanks to the sense co-composition principle between verb and argument. For this reason, clustering of lexical items filling the Object slot of a verb is believed to bring to surface relevant information about verbal meaning and the verb-Objects relation. The paper presents the results of an experiment comparing the automatic clustering of direct Objects operated by the agglomerative hierarchical algorithm of the Sketch Engine corpus tool with the manual clustering of direct Objects carried out in the T-PAS resource. Cluster analysis is here used to improve the semantic quality of automatic clusters against expert human intuition and as an investigation tool of phenomena intrinsic to semantic selection of verbs and the construction of verb senses in context.

The use of contextualised word embeddings allowed for a relevant performance increase for almost all Natural Language Processing (NLP) applications. Recently some new models especially developed for Italian became available to scholars. This work aims at evaluating the impact of these models in enhancing application performance for Italian establishing the new state-of-the-art for some fundamental NLP tasks.

Polarity imbalance is an asymmetric situation that occurs while using parametric threshold values in lexicon-based Sentiment-Analysis (SA). The variation across the thresholds may have an opposite impact on the prediction of negative and positive polarity. We hypothesize that this may be due to asymmetries in the data or in the lexicon, or both. We carry out therefore experiments for evaluating the effect of lexicon and of the topics addressed in the data. Our experiments are based on a weighted version of the Italian linguistic resource MAL (Morphologically-inflected Affective Lexicon) by using as weighting corpus TWITA, a large-scale corpus of messages from Twitter in Italian. The novel Weighted-MAL (W-MAL), presented for the first time int this paper, achieved better polarity classification results especially for negative tweets, along with alleviating the aforementioned polarity imbalance. Lo sbilanciamento della polarità è una situazione di asimmetria che si viene a creare quando si impiegano valori soglia parametrici nella Sentiment Analysis (SA) basata su dizionario. La variazione dei valori soglia può avere un impatto opposto rispetto alla predizione di polarità negativa e positiva. Si ipotizza che questo effetto sia dovuto ad asimmetrie nei dati o nel dizionario, o in entrambi. Abbiamo condotto esperimenti per misurare l’effetto del lessico e degli argomenti trattati nel nostro dataset. I nostri esperimenti sono basati su una versione ponderata della risorsa per l’italiano MAL (Morphologically-inflected Affective Lexicon), usando come corpus per la ponderazione TWITA, un corpus di larga scala di messaggi da Twitter in italiano. La nuova risorsa Weighted-MAL (W-MAL), presentata per la prima volta in questo articolo, ottiene migliori risultati nella classificazione della polarità specialmente, per i messaggi negativi, oltre ad alleviare il problema sopracitato di sbilanciamento della polarità.

The movement of a child’s index finger that points to a printed text while (s)he is reading may provide a proxy for the child’s eye movements and attention focus. We validated this correlation by showing a quantitative analysis of patterns of “finger-tracking” of Italian early graders engaged in reading a text displayed on a tablet. A web application interfaced with the tablet monitors the reading behaviour by modelling the way the child points to the text while reading. The analysis found significant developmental trends in reading strategies, marking an interesting contrast between typically developing and atypically developing readers.

In this paper we investigate the applicability of automatic methods for frame induction to improve the coverage of IFrameNet, a novel lexical resource based on Frame Semantics in Italian. The experimental evaluations show that the adopted methods based on neural word embeddings pave the way for the assisted development of a large scale lexical resource for our language.

It is widely held that children implicitly learn the structure of their writing system through statistical learning of spelling-tosound mappings. Yet an unresolved question is how to sequence reading experience so that children can ‘pick up’ the structure optimally. We tackle this question here using a computational model of encoding and decoding. The order of presentation of words was manipulated so that they exhibited two distinct progressions of granularity of spelling-to-sound mappings. We found that under a training regime that introduced written words progressively from small-to-large granularity, the network exhibited an early advantage in reading acquisition as compared to a regime introducing written words from large-to-small granularity. Our results thus provide support for the grain size theory (Ziegler and Goswami, 2005) and demonstrate that the order of learning can influence learning trajectories of literacy skills. Nanyang Technological University Published version Support comes from the Education Research Funding Programme of the National Institute of Education (NIE), Nanyang Technological University, Singapore, grant #OER0417OBA.

In this paper we present LeFFI, an inflected lexicon of Italian listing all the available wordforms of 2,053 verbs. We then use this resource to perform an entropy-based analysis of the mutual predictability of wordforms within Italian verb paradigms, and compare our findings to the ones of previous work on stem predictability in Italian verb inflection.

This paper explores the possibility to annotate engagement as an extra-linguistic information in a multimodal corpus of guided tours in cultural sites. Engagement has been annotated in terms of gain or loss of perceived attention from the audience, and this information has been aligned to the transcription of the speech from the guide. A preliminary analysis suggests that the level of engagement correlates with some specific linguistic features, opening up to possible future exploitation.

The task of Dialogue Act (DA) tagging, a crucial component in many conversational agents, is often addressed assuming a single DA per speaker turn in the conversation. However, speakers’ turns are often multifunctional, that is they can contain more than one DA (i.e. “I’m Alex. Have we met before?” contains a ‘statement’, followed by a ‘question’). This work focuses on multifunctional DA tagging in Italian. First, we present iLISTEN2ISO, a novel resource with multifunctional DA annotation in Italian, created by annotating the iLISTEN corpus with the ISO standard. We provide an analysis of the corpus showing the importance of multifunctionality for DA tagging. Additionally, we train DA taggers for Italian on iLISTEN (achieving State of the Art results) and iLISTEN2ISO. Our findings indicate the importance of using a multifunctional approach for DA tagging.

Aerest is a reading assessment protocol for the concurrent evaluation of a child’s decoding and comprehension skills. Reading data complying with the Aerest protocol were automatically collected and structured with the ReadLet web-based platform in a pilot study, to form the Aerest Reading Database. The content, structure and potential of the database are described here, together with the main directions of current and future developments.

This paper presents the early stages of the development of a new treebank containing all of Dante Alighieri’s Latin works. In particular, it describes the conversion of the original tei-xml files to CoNLL-U, the creation of a gold standard, the process of training four annotators and the evaluation of the syntactic annotation in terms of inter-annotator agreement and la, uas and las. The aim is to release a new resource, in view of the celebrations for the 700th anniversary of Dante’s death, which can support the development of the Vocabolario Dantesco.

This paper describes the compilation and annotation of ItaGLAM, a corpus of tweets written by Italian Galleries, Li-breries, Archives and Museums (GLAMs) during the lockdown period in Italy due to the COVID-19 pandemic ItaGLAM has been annotated with a set of labels which may be useful to identify different types of communication Furthermore, the collected data have been used to train a set of classifiers The results are analyzed to evaluate the information flow between GLAM and users and to analyze cultural communication on the Web Copyright © 2020 for this paper by its authors

Per un’analisi dell’italiano scritto degli studenti universitari, un contesto privilegiato si trova in quei canali social di condivisione tra pari noti con la titolatura di “Spotted”. Attivi particolarmente su Instagram, si tratta di canali di interazione pubblica informale che forniscono elementi significativi all’indagine sulle scritture spontanee dei giovani in rete. Questa ricerca presenta un corpus di oltre trentamila testi da Spotted universitari italiani. Concentrandoci sulle fasi COVID (e sul periodo immediatamente precedente), intendiamo illustrare come il corpus Spotted-Ita appaia funzionale a un’indagine riferita alla comunicazione linguistica nell’occasione di un’esperienza unica e straordinaria vissuta dagli studenti universitari. “Spotted" posts represent one of the most popular forms of Computer-mediated Communication (CMC) among university students in Italy, and as such, they represent a privileged context to analyze the Italian language used by students on the Web. This kind of informal communication channels is active especially on Instagram, and provides relevant insights on the spontaneous writing of young people. Based on Spotted-ITA Corpus, a corpus of over 30,000 posts retrieved from Italian Spotted accounts on Instagram, this paper presents a focus on the period of lockdown in Italy due to COVID pandemics and its immediately preceding period, showing how this corpus can be useful in analyzing the way Italian university students communicated during this unique and extraordinary experience.

L’articolo si propone di investigare l’impatto delle comunicazioni istituzionali durante la crisi sanitaria causata dal Covid-19 in Italia, attraverso l’analisi computazionale dell’attività di micro-blogging su Twitter. A partire dal corpus TWITA, abbiamo realizzato una Sentiment Analysis per osservare eventuali cambiamenti nella polarità delle scritture degli utenti, in reazione a undici discorsi istituzionali tenuti durante l’emergenza sanitaria. Variazioni sono state osservate in reazione a tre discorsi tenuti dal Presidente del Consiglio rispettivamente il 4 marzo, il 9 marzo e il 26 aprile 2020. This paper aims at investigating the impact of institutional communications during the health crisis due to Covid-19 pandemic in Italy, through the analysis of micro-blogging activities on Twitter by means of NLP techniques. We performed a Sentiment Analysis on the TWITA corpus, to pinpoint potential correlations between opinion polarity (positive or negative) of the users and public speeches during the outbreak. Our findings show changes in sentiment polarity related to three institutional speeches delivered by the Italian Prime Minister Giuseppe Conte on March, 4th, March, 9th, and April, 26th 2020.