Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020最新文献

{"title":"AriEmozione: Identifying Emotions in Opera Verses","authors":"Francesco Fernicola, Shibingfeng Zhang, F. Garcea, P. Bonora, Alberto Barrón-Cedeño","doi":"10.4000/books.aaccademia.8528","DOIUrl":"https://doi.org/10.4000/books.aaccademia.8528","url":null,"abstract":"We present a new task: the identification of the emotions transmitted in Italian opera arias at the verse level. This is a relevant problem for the organization of the vast repertoire of Italian Opera arias available and to enable further analyses by both musicologists and the lay public. We shape the task as a multi-class supervised problem, considering six emotions: love, joy, admiration, anger, sadness, and fear. In order to address it, we manually-annotated an opera corpus with 2.5k verses —which we release to the research community— and experimented with different classification models and representations. Our best-performing models reach macroaveraged F1 measures of ∼0.45, always considering character 3-grams representations. Such performance reflects the difficulty of the task at hand, partially caused by the size and nature of the corpus, which consists of relatively short verses written in 18thcentury Italian.","PeriodicalId":300279,"journal":{"name":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122620525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Good are Humans at Native Language Identification? A Case Study on Italian L2 writings","authors":"Elisa Di Nuovo, C. Bosco, E. Corino","doi":"10.4000/books.aaccademia.8475","DOIUrl":"https://doi.org/10.4000/books.aaccademia.8475","url":null,"abstract":"In this paper we present a pilot study on human performance for the Native Language Identification task. We performed two tests aimed at exploring the human baseline for the task in which test takers had to identify the writers’ L1 relying only on scripts written in Italian by English, French, German and Spanish native speakers. Then, we conducted an error analysis considering the language background of both test takers and text writers.","PeriodicalId":300279,"journal":{"name":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125986647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grounded and Ungrounded Referring Expressions in Human Dialogues: Language Mirrors Different Grounding Conditions","authors":"Eleonora Gualdoni, R. Bernardi, R. Fernández, Sandro Pezzelle","doi":"10.4000/books.aaccademia.8600","DOIUrl":"https://doi.org/10.4000/books.aaccademia.8600","url":null,"abstract":"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.","PeriodicalId":300279,"journal":{"name":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126403252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Suoidne-varra-bleahkka-mála-bihkka-senet-dielku 'hay-blood-ink-paint-tar-mustard-stain' -Should compounds be lexicalized in NLP?","authors":"Linda Wiechetek, Chiara Argese, Flammie A. Pirinen, Trond Trosterud","doi":"10.4000/books.aaccademia.8979","DOIUrl":"https://doi.org/10.4000/books.aaccademia.8979","url":null,"abstract":"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.","PeriodicalId":300279,"journal":{"name":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126407974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simple Data Augmentation for Multilingual NLU in Task Oriented Dialogue Systems","authors":"Samuel Louvan, B. Magnini","doi":"10.4000/books.aaccademia.8648","DOIUrl":"https://doi.org/10.4000/books.aaccademia.8648","url":null,"abstract":"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.","PeriodicalId":300279,"journal":{"name":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130389302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multifunctional ISO Standard Dialogue Act Tagging in Italian","authors":"G. Roccabruna, Alessandra Cervone, G. Riccardi","doi":"10.4000/books.aaccademia.8860","DOIUrl":"https://doi.org/10.4000/books.aaccademia.8860","url":null,"abstract":"English. 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 ‘state-ment’, followed by a ‘question’). This work focuses on multifunctional DA tagging in Italian. First, we present iLIS-TEN2ISO, a novel resource with multi-functional 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.","PeriodicalId":300279,"journal":{"name":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130731537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building a Treebank in Universal Dependencies for Italian Sign Language","authors":"Gaia Caligiore, C. Bosco, A. Mazzei","doi":"10.4000/books.aaccademia.8320","DOIUrl":"https://doi.org/10.4000/books.aaccademia.8320","url":null,"abstract":"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 CoNLLU format.","PeriodicalId":300279,"journal":{"name":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131145054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Diachronic Italian Corpus based on \"L'Unità\"","authors":"Pierpaolo Basile, A. Caputo, Tommaso Caselli, Pierluigi Cassotti, Rossella Varvara","doi":"10.4000/books.aaccademia.8245","DOIUrl":"https://doi.org/10.4000/books.aaccademia.8245","url":null,"abstract":"English. In this paper, we describe the creation of a diachronic corpus for Italian by exploiting the digital archive of the newspaper “L’Unità”. We automatically clean and annotate the corpus with PoS tags, lemmas, named entities and syntactic dependencies. Moreover, we compute frequency-based time series for tokens, lemmas and entities. We show some interesting corpus statistics taking into account the temporal dimension and describe some examples of usage of time series. 1 Motivation and Background Diachronic linguistics is one of the two major temporal dimensions of language study proposed by de Saussure in his Cours de languistique générale and has a long tradition in Linguistics. Recently, the increasing availability of diachronic corpora as well as the development of new NLP techniques for representing word meanings has boosted the application of computational models to investigate historical language data (Hamilton et al., 2016; Tahmasebi et al., 2018; Tang, 2018). This culminated in SemEval-2020 Unsupervised Lexical Semantic Change Detection (Schlechtweg et al., 2020), the first attempt to systematically evaluate automatic methods for language change detection. Italian is a Romance language which has undergone lots of changes in its history. Its official Copyright c ©2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). adoption as a national language occurred only after the Unification of Italy (1861), having previously been a literary language. Diachronic corpora of Italian are currently available and accessible to the public (e.g., DiaCORIS and MIDIA). Unfortunately, restricted access/distribution of these resources limits their utilisation. This actually prevents the investigation of more recent NLP methods to the diachronic dimensions. To obviate this limit, we collect and make freely available1 a new corpus based on the newspaper “L’Unità”. Founded by Antonio Gramsci on February, 12th 1924, “L’Unità” was the official newspaper of the Italian Communist Party (PCI 2, henceforth). The newspaper had a troubled history: with the dissolution of PCI in 1991, the newspaper continued to live as the official newspaper of the new Democratic Party of the Left (PDS/DS) until July, 31th 2014. After that date, it ceased its publication until June, 30th 2015, and it was definitely closed on June, 3rd 2017. Since 2017, the historical archive of “L’Unità” has been made again visible and available on the Web.3 One of the main issues of this resource is the lack of information about who owns the rights of the original archive. To our knowledge, the online version of the archive was legally obtained by downloading the original archive before the closure of the newspaper. The current archive, available online, does not contain the local editions of the newspaper and the photographic archive. The main contribution of this work lies in the https://github.com/swapUniba/unita/ It is the a","PeriodicalId":300279,"journal":{"name":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131170005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Lexical Semantic Changes in Corpora with the Diachronic Engine","authors":"Pierluigi Cassotti, Pierpaolo Basile, M. Degemmis, G. Semeraro","doi":"10.4000/books.aaccademia.8343","DOIUrl":"https://doi.org/10.4000/books.aaccademia.8343","url":null,"abstract":"English. With the growing availability of digitized diachronic corpora, the need for tools capable of taking into account the diachronic component of corpora becomes ever more pressing. Recent works on diachronic embeddings show that computational approaches to the diachronic analysis of language seem to be promising, but they are not user friendly for people without a technical background. This paper presents the Diachronic Engine, a system for the diachronic analysis of corpora lexical features. Diachronic Engine computes word frequency, concordances and collocations taking into account the temporal dimension. It is also able to compute temporal word embeddings and timeseries that can be exploited for lexical semantic change detection. 1 Motivation and Background Synchronic corpora are widely used in linguistics for deriving a set of abstract rules that govern a particular language under analysis by using statistical approaches. The same methodology can be adopted for analyzing the evolution of word meanings over time in the case of diachronic corpora. However, this process can be very time-consuming. Usually, linguists rely on software tools that can easily explore and clean the corpus, while highlighting the more relevant linguistic features. Sketch Engine1(Kilgarriff et al., 2004; Kilgarriff et al., 2014) is the leading tool in the corpus analysis field. Beyond several interesting features, Sketch Engine includes trends (Kilgarriff et al., 2015), which allow for diachronic Copyright c ©2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). https://www.sketchengine.eu/ analysis based on the frequency distribution of words. Trends rely on merely frequency features, ignoring word usage information. Moreover, the Sketch Engine interface does not provide temporal information about concordances and collocations. NoSketchEngine2 is an open-source version of SketchEngine. It requires technical expertise for the setup and, contrarily to SketchEngine, it does not support word sketches, terminology, thesaurus, n-grams, trends and corpus building. An interesting system is DiaCollo3 (Jurish and der Wissenschaften, 2015), a software tool for the discovery, comparison, and interactive visualization of target word combinations. Combinations can be requested for a particular time period, or for a direct comparison between different time periods. However, DiaCollo is focused exclusively on the extraction and visualization of collocations from diachronic corpora. In recent works about computational diachronic linguistics, techniques based on word embeddings produce promising results. In Semeval Task 1 (Schlechtweg et al., 2020), for instance, type embeddings rich high performances on both subtasks. However, these techniques are not included in any aforementioned linguistic tool. In order to bridge this gap, we try to build a tool that includes approaches for the analysis of diachronic embeddi","PeriodicalId":300279,"journal":{"name":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131259832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributional Semantics: Yesterday, Today, and Tomorrow","authors":"Alessandro Lenci","doi":"10.4000/books.aaccademia.9030","DOIUrl":"https://doi.org/10.4000/books.aaccademia.9030","url":null,"abstract":"Distributional semantics is undoubtedly the mainstream approach to meaning representation in computational linguistics today. It has also become an important paradigm of semantic analysis in cognitive science, and even linguists have started looking at it with growing interest. The popularity of distributional semantics has literally boomed in the era of Deep Learning, when “word embeddings” have become the basic ingredient to “cook” any NLP task. The era of BERT & co. has brought new types of contextualized representations that have often generated hasty claims of incredible breakthroughs in the natural language understanding capability of deep learning models. Unfortunately, these claims are not always supported by the improved semantic abilities of the last generation of embeddings. Models like BERT are still rooted in the principles of distributional learning, but at the same time their goal is more ambitious than generating corpus-based representations of meaning. On the one hand, the embeddings they produce encode much more than lexical meaning, but on the other hand we are still largely uncertain about what semantic properties of natural language they actually capture. Distributional semantics has surely benefited from the successes of the deep learning, but this might even jeopardize the very essence of distributional models of meaning, by making their goals and foundations unclear.","PeriodicalId":300279,"journal":{"name":"Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134520385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}