alemiaschi/clic-it_corpus huggingface dataset:
E pluribus unum.,Representing compounding in a derivational lexicon of Latin
Authors: Silvia Micheli , Eleonora Litta (Università di Pavia, Università Cattolica del Sacro Cuore, 2017)
Topic: Latin Resources

Abstract
This paper describes how compounding is treated in the Word Formation Latin derivational lexicon. Through the analysis of some types of Latin compounds, perspectives and limitations of the resource are highlighted; its contribution to theoretical and computational linguistic issues is also outlined.

Introduction
lexicon Word Formation Latin (WFL, AUTHOR) is a derivational morphology resource for Latin where words are analysed in their formative components and related to each other on the basis of word formation rules (WFRs). It represents a wide lexical resource not only for the study of Latin derivational morphology (i.e. affixal and conversive processes), but also for compounding, which has often been neglected in other most recent resources for other languages. and AUTHOR, the derivational lexicon for German DErivBASE AUTHOR and that for Italian derIvaTario AUTHOR. The lexical basis behind WFL is the same as the morphological analyser and lemmatiser for Latin Lemlat AUTHOR. All lemmas have been collected from three main Classical Latin dictionaries ( AUTHOR; AUTHOR; AUTHOR) plus the Onomasticon of Forcellini’s AUTHOR 5\textsuperscript{th} edition of Lexicon Totius Latinitatis AUTHOR. All those lemmas that share a common (not derived) ancestor belong to the same “morphological family”, AUTHOR represented in the web application () as a tree-graph. The aim of this paper is twofold: on the one hand, it describes how compounding is represented into the WFL derivational lexicon; on the other hand, it aims at highlighting the theoretical and computational contribution of this resource through the analysis of some aspects (i.e. WFRs, input and output lexical categories) of the Latin compounds collected in it.

Conclusion
This paper has provided an overview of how compounding is represented in WFL, a derivational lexicon for Latin. This preliminary study, with its quantitative analysis in the field of LC, shows the potential for raising new questions and issues offered by a resource that for the first time collects all compounds used in Classical Latin. For instance, representing all compounding rules into a network, as it has been already successfully done for the affixal rules listed in WFL, AUTHOR, could lead to further research questions. These could be the investigation on constituent typologies or on the productivity of the different types of compounds. Future developments in WFL should be a way of searching through constituents by original lemma (currently still missing), and implementing a way of marking those PoS that appear differently in the resource's lexical basis. This would also allow for a more precise quantitative investigation on constituent typologies.
PARSEME-It Corpus – An annotated Corpus of Verbal Multiword Expressions in Italian
Authors: Johanna Monti, Maria Pia di Buono, Federico Sangati (Università di Napoli L'Orientale, University of Zagreb, Indipendent Researcher, 2017)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper describes a new language resource annotated with verbal multiword expressions (VMWEs) in Italian. The paper discusses the state of the art in VMWE identification and annotation in Italian, the methodology adopted, the various VMWE categories annotated, the corpus and the annotation process. Finally, the paper ends with results, conclusion and future work.

Introduction
This paper outlines the development of a new language resource for Italian, namely the PARSEME-It VMWE corpus, annotated with Italian MWEs of a particular class: verbal multiword expressions (VMWE). The PARSEME-It VMWE corpus has been developed by the PARSEME-IT research group in the framework of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions AUTHOR, a joint effort, carried out within a European research network, to elaborate universal terminologies and annotation guidelines for verbal multiword expressions in 18 languages, among which also the Italian language is represented. Notably, multiword expressions represent a difficult lexical construction to identify, model and treat by Natural Language Processing (NLP) tools, such as parsers, machine translation engines among others, mainly due to their non-compositional property. In particular, among multiword expressions verbal ones are particularly challenging because they have different syntactic structures (prendere una decisione 'make a decision', decisioni prese precedentemente 'decisions made previously'), may be continuous and discontinuous (andare e venire versus andare in malora in Luigi ha fatto andare la societ\`a in malora), may have a literal and figurative meaning (abboccare all'amo 'bite the hook' or 'be deceived'). In this paper, we describe the state of the art in VMWE annotation and identification for the Italian language (section 2). We then present the methodology (section 3), the Italian VMWE categories taken into account for the annotation task (section 4), the corpus and the annotation process (section 5), and the results (section 6). Finally, we discuss conclusions and future work (section 7).

Conclusion
In this paper, we described a linguist resource of Italian VMWE, developed within the PARSEME Shared Task on Automatic Identification of VMWE. We consider this work an initial contribution for elaborating an Italian universal terminology of VMWE. Future work includes the extension of the current corpus and a fine-grained linguistic analysis of the annotation in order to contribute to the description of these phenomena.
AHyDA: Automatic Hypernym Detection with feature Augmentation
Authors: Ludovica Pannitto, Lavinia Salicchi, Alessandro Lenci (Università di Pisa, 2017)
Topic: Distributional Semantics

Abstract
Several unsupervised methods for hypernym detection have been investigated in distributional semantics. Here we present a new approach based on a smoothed version of the distributional inclusion hypothesis. The new method is able to improve hypernym detection after testing on the \textsc{bless} dataset.

Introduction
Within the Distributional Semantics framework, semantic similarity between words is usually expressed in terms of proximity in a semantic space, where the dimensions of the space represent, at some level of abstraction, the contexts in which the words occur. Our intuitions about the meaning of words allow inferences of the kind expressed in example (entailment), and we expect Distributional Semantic Models (DSMs) to support such inferences: \ex \ex Wilbrand invented TNT \to Wilbrand uncovered TNT \ex A horse ran \to An animal moved The type of relation between semantically similar lexemes may differ significantly, but DSMs only account for a generic notion of semantic relatedness. Furthermore, not all lexical relations are symmetrical (see example (ex2)), while most of the similarity measures defined in distributional semantics are, like the cosine. \ex \ex I saw a dog \rightarrow I saw an animal \ex I saw an animal \nrightarrow I saw a dog % Hypernymy is an asymmetric relation. Automatic hypernym identification is a very well-known task in literature, which has mostly been addressed with semi-supervised, pattern-based approaches AUTHOR. Various unsupervised models have been proposed AUTHOR, based on the notion of Distributional Generality AUTHOR and on the Distributional Inclusion Hypothesis (DIH) AUTHOR which has been derived from it.

Conclusion
The Distributional inclusion hypothesis has proven to be a viable approach to hypernym detection. However, its original formulation rests on an assumption that does not take into consideration the actual usage of hypernyms in texts. In this paper we have shown that, by adding some further pragmatically inspired constraints, a better discrimination can be achieved between co-hyponyms and hypernyms. Our ongoing work focuses on refining the way in which the smoothing is performed, and testing its performance on other datasets of semantic relations.
PARAD-it: Eliciting Italian Paradigmatic Relations with Crowdsourcing
Authors: Irene Sucameli, Alessandro Lenci (Università di Pisa, 2017)
Topic: Lexical and Semantic Resources and Analysis

Abstract
n this paper, we present a new dataset of semantically related Italian word pairs. The dataset consists of nouns, adjectives and verbs together with their synonyms, antonyms and hypernyms. The data have been collected with crowdsourcing from a pool of Italian native speakers. The dataset, the first of its kind, is useful not only to evaluate computational models of Italian semantic relations, but also for linguistic and psycholinguistic investigations of the mental lexicon.

Introduction
The present project aims at providing new data about the internal organization of the Italian lexicon. For this purpose, we present PARAD-it1 a paradigmatic relation dataset elicited from Italian native speakers with crowdsourcing. This dataset consists of a set of target words selected from the Italian section of MultiWordNet paired with relata belonging to different kinds of paradigmatic semantic relations. The data have been collected using the same method adopted by AUTHOR for German and by AUTHOR for English, thereby making the three datasets fully comparable for crosslingual analyses. PARAD-it is a collection of hypernyms, antonyms, and synonyms for a set of Italian nouns, adjectives and verbs.

Conclusion
This project presents PARAD-it, a new collection composed by pairs of Italian nouns, verbs and adjectives related by different types of paradigmatic relations, elicited by native speakers with crowdsourcing. Starting from this new resource, a quantitative analysis was carried out to analyze the mechanisms underlying the Italian language. In particular, the analysis has shown that: i) high frequency values tend to be recorded for nouns and hypernyms while ii) Italian speakers tend to use a more uniform vocabulary to describe the relation of antonymy. This analysis has revelead some interesting differences in the response distribution both with respect to the PoS of the target, and with respect to the semantic relation. Moreover, this study confirms the differential salience of the various paradigmatic relations in organizing the mental lexicon. To the best of our knowledge, PARAD-it is the first, freely available resource of this kind for Italian, paving the way for its use as a test set for computational models of semantic relation identification and classification. For future research, we plan to realize and additional round of crowdsourcing in order to validate the words previously produced, checking also if there is an overlap between these words and the targets from MultiWordNet. Moreover, we plan to carry out a crosslingual comparison with the similar datasets collected for German and for English.
TWITTIRÒ: a Social Media Corpus with a Multi-layered Annotation for Irony
Authors: Alessandra Teresa Cignarella, Cristina Bosco, Viviana Patti (Università di Torino, 2017)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In this paper we describe our work concerning the application of a multi-layered scheme for the fine-grained annotation of irony AUTHOR on a new Italian social media corpus. In applying the annotation on this corpus containing tweets, i.e. \textsc{twittir\`o}, we outlined both strengths and weaknesses of the scheme when applied on Italian, thus giving further clarity on the future directions that can be followed in the multilingual and cross-language perspective.

Introduction
The recognition of irony and the identification of pragmatic and linguistic devices that activate it are known as very challenging tasks to be performed by both humans or automatic tools AUTHOR. Our goal, was to create an annotated Italian corpus through which we could address some issues concerning formalization and automatic detection of irony. This work collocates, therefore, in the context of a multilingual project for studying irony and for developing resources to be exploited in training NLP tools for sentiment analysis. Providing that irony detection is a field that has been growing very fast in the last few years AUTHOR, and also taking into account that generation of irony (whether it is spoken or written) may also depends on the language and culture in which it is expressed, the main aim of this work is that of replying to the following research questions: Is it possible to formally model irony? If so, how? Through the present paper indeed, we aim at contributing to the study of irony not only in Italian, but rather in a multilingual and cross-linguistic perspective. Our hope is that, on the one hand, studying the use of figurative language in Italian social media texts, will help us to better understand the developing of this figure of speech itself -irony- and its relations with humor. On the other hand, the study will lead us to the discovery of features and patterns that can be shared and confronted with similar projects in other languages.

Conclusion
The paper describes our work concerning the application of a fine-grained annotation scheme for pragmatic phenomena. In particular, it has been used to annotate the rhetorical device of irony in texts from Twitter. It confirms how this task is challenging, it contributed to shed some light on linguistic phenomena and to significantly extend the resource in AUTHOR with new Italian annotated data to be exploited in future experiments on irony detection in a multi-lingual perspective . The disagreement in the annotation of irony in the three sub-corpora \textsc{tw-spino}, \textsc{sentipolc} and \textsc{tw-bs}, which are featured by different characteristics, is a further issue to be addressed. In future work, we plan therefore to investigate the differences in the disagreemente detected across the three portions of \textsc{twittirò} providing in-depth analysis of currently available and new linguistic data.
Parsing Italian texts together is better than parsing them alone!
Authors: Oronzo Antonelli, Fabio Tamburini (Università di Bologna, 2018)
Topic: Syntax and Dependency Treebanks

Abstract
In this paper we present a work English. In this paper we present a work aimed at testing the most advanced, stateof-the-art syntactic parsers based on deep neural networks (DNN) on Italian. We made a set of experiments by using the Universal Dependencies benchmarks and propose a new solution based on ensemble systems obtaining very good performances.

Introduction
Syntactic parsing of morphologically rich languages like Italian often poses a number of hard challenges. Various works applied different kinds of freely available parsers on Italian training them using different resources and different methods for comparing their results (Lavelli, 2014; Alicante et al., 2015; Lavelli, 2016) and gather a clear picture of the syntactic parsing task performances for the Italian language. In this direction seems relevant to cite the EVALITA1 periodic campaigns for the evaluation of constituency and dependency parsers devoted to the syntactic analysis of Italian (Bosco and Mazzei, 2011; Bosco et al., 2014). Other studies regarding the syntactic parsing of Italian tried to enhance the parsing performances by building some kind of ensemble systems (Lavelli, 2013; Mazzei, 2015). 1http://www.evalita.it By looking at the cited papers we can observe that they evaluated the state-of-the-art parsers before the “neural net revolution” not including the last improvements proposed by new research studies. The goal of this paper is twofold: ﬁrst, we would like to test the effectiveness of parsers based on the newly-proposed technologies, mainly deep neural networks, on Italian, and, second, we would like to propose an ensemble system able to further improve the neural parsers performances when parsing Italian texts.

Conclusion
We have studied the performances of some neural dependency parsers on generic and social media domain. Using the predictions of each single parser we combined the best outcomes to improve the performance in various ways. The ensemble models are more efﬁcient on corpora built using in-domain data (social media), giving an improvement of ∼1% in UAS and ∼2.5% in LAS.Thanks to the number of parser models adopted in the experiments it has been possible to verify that the performances of the ensemble models increase as the number of parsers grows. The improvement of LAS is, in most cases, at least twice the value of UAS. This could mean that ensemble models catch with better precision the type of dependency relations rather than headdependent relations. All the proposed ensemble strategies, except for distilling, perform more or less in the same way, therefore the choice of which strategy to use is due, in part, to the properties that we want to obtain on the combined dependency tree. Our work is inspired by the work of Mazzei
Integrating Terminology Extraction and Word Embedding for Unsupervised Aspect Based Sentiment Analysis
Authors: Luca Dini, Paolo Curtoni, Elena Melnikova (Innoradiant, 2018)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In this paper we explore the adEnglish. In this paper we explore the advantages that unsupervised terminology extraction can bring to unsupervised Aspect Based Sentiment Analysis methods based on word embedding expansion techniques. We prove that the gain in terms of F-measure is in the order of 3%.

Introduction
The goal of this paper is to bring a contribution on the advantage of exploiting terminology extraction systems coupled with word embedding techniques. The experimentation is based on the corpus of Semeval 2016. In a previous work, summarized in section 4, we reported the results of a system for Aspect Based Sentiment Analysis (ABSA) based on the assumption that in real applications a domain dependent gold standard is systematically absent. We showed that by adopting domain dependent word embedding techniques a reasonable level of quality (i.e. acceptable for a proof of concept) in terms of entity detection could be achieved by providing two seed words for each targeted entity. In this paper we explore the hypothesis that unsupervised terminology extraction approaches could further improve the quality of the results in entity extraction. The paper is organized as follows: In section 2 we enumerate the goal of the research and the industrial background justifying it. In section 3 we provide a state of the art of ABSA particularly focused towards unsupervised ABSA and its relationship to terminology extraction. In section 4 we summarize our previous approach in order to provide a context for our experimentation. In section 5 we prove the benefit of the integration of unsupervised terminology extraction with ABSA, whereas in 6 we provide hints for further investigation.

Conclusion
Many improvements can be conceived to the method presented here, especially concerning the computation of the vector associated to the opinionated windows, both in terms of size, directionality and consideration of finer grained features (e.g. indicators of a switch of topic). However our future investigation will rather be oriented towards full-fledged ABSA, i.e. taking into account not only Entities, but also Attributes. Indeed, if we consider that the 45% F measure is obtained on a corpus where only 66% sentences were correctly classified according to the sentiment and if we put ourselves in a Semeval perspective where entity evaluation is provided with respect to a “gold sentiment standard” we achieve a F-score of 68%, which is fully acceptable for an almost unsupervised system.
Advances in Multiword Expression Identification for the Italian language: The PARSEME shared task edition 1.1
Authors: Johanna Monti, Silvio Ricardo Cordeiro, Carlos Ramisch, Federico Sangati, Agata Savary, Veronika Vincze (Università di Napoli L'Orientale, Aix-Marseille Université, University of Tours, MTA-SZTE Research Group on Artificial Intelligence, 2018)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This contribution describes the results of the second edition of the shared task on automatic identiﬁcation of verbal multiword expressions, organized as part of the LAW-MWE-CxG 2018 workshop, co-located with COLING 2018, concerning both the PARSEME-IT corpus and the systems that took part in the task for the Italian language. The paper will focus on the main advances in comparison to the ﬁrst edition of the task.

Introduction
Multiword expressions (MWEs) are a particularly challenging linguistic phenomenon to be handled by NLP tools. In recent years, there has been a growing interest in MWEs since the possible improvements of their computational treatment may help overcome one of the main shortcomings of many NLP applications, from Text Analytics to Machine Translation. Recent contributions to this topic, such as Mitkov et al. (2018) and Constant et al. (2017) have highlighted the difﬁculties that this complex phenomenon, halfway between lexicon and syntax, characterized by idiosyncrasy on various levels, poses to NLP tasks. This contribution will focus on the advances in the identiﬁcation of verbal multiword expressions (VMWEs) for the Italian language. In Section 2 we discuss related work. In Section 3 we give an overview of the PARSEME shared task. In Section 4 we present the resources developed for the Italian language, namely the guidelines and the corpus. Section 5 is devoted to the annotation process and the inter-annotator agreement. Section 6 brieﬂy describes the thirteen systems that took part in the shared task and the results obtained. Finally, we discuss conclusions and future work (Section 7).

Conclusion
Having presented the results of the PARSEME shared task edition 1.1, the paper described the advances achieved in this last edition in comparison with the previous one, but also highlighted that there is room for further improvements. We are working on some critical areas which emerged during the annotation task in particular with reference to some borderline cases and the reﬁnement of the guidelines. Future work will focus on maintaining and increasing the quality and the size of the corpus but also on extending the shared task to other MWE categories, such as nominal MWEs.