Alessandra Teresa Cignarella


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Marking Irony Activators in a Universal Dependencies Treebank: The Case of an Italian Twitter Corpus
Alessandra Teresa Cignarella | Manuela Sanguinetti | Cristina Bosco | Paolo Rosso
Proceedings of the 12th Language Resources and Evaluation Conference

The recognition of irony is a challenging task in the domain of Sentiment Analysis, and the availability of annotated corpora may be crucial for its automatic processing. In this paper we describe a fine-grained annotation scheme centered on irony, in which we highlight the tokens that are responsible for its activation, (irony activators) and their morpho-syntactic features. As our case study we therefore introduce a recently released Universal Dependencies treebank for Italian which includes ironic tweets: TWITTIRÒ-UD. For the purposes of this study, we enriched the existing annotation in the treebank, with a further level that includes irony activators. A description and discussion of the annotation scheme is provided with a definition of irony activators and the guidelines for their annotation. This qualitative study on the different layers of annotation applied on the same dataset can shed some light on the process of human annotation, and irony annotation in particular, and on the usefulness of this representation for developing computational models of irony to be used for training purposes.

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Treebanking User-Generated Content: A Proposal for a Unified Representation in Universal Dependencies
Manuela Sanguinetti | Cristina Bosco | Lauren Cassidy | Özlem Çetinoğlu | Alessandra Teresa Cignarella | Teresa Lynn | Ines Rehbein | Josef Ruppenhofer | Djamé Seddah | Amir Zeldes
Proceedings of the 12th Language Resources and Evaluation Conference

The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this paper is twofold: (1) to provide a short, though comprehensive, overview of such treebanks - based on available literature - along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The main goal of this paper is to provide a common framework for those teams interested in developing similar resources in UD, thus enabling cross-linguistic consistency, which is a principle that has always been in the spirit of UD.

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Multilingual Irony Detection with Dependency Syntax and Neural Models
Alessandra Teresa Cignarella | Valerio Basile | Manuela Sanguinetti | Cristina Bosco | Paolo Rosso | Farah Benamara
Proceedings of the 28th International Conference on Computational Linguistics

This paper presents an in-depth investigation of the effectiveness of dependency-based syntactic features on the irony detection task in a multilingual perspective (English, Spanish, French and Italian). It focuses on the contribution from syntactic knowledge, exploiting linguistic resources where syntax is annotated according to the Universal Dependencies scheme. Three distinct experimental settings are provided. In the first, a variety of syntactic dependency-based features combined with classical machine learning classifiers are explored. In the second scenario, two well-known types of word embeddings are trained on parsed data and tested against gold standard datasets. In the third setting, dependency-based syntactic features are combined into the Multilingual BERT architecture. The results suggest that fine-grained dependency-based syntactic information is informative for the detection of irony.


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Presenting TWITTIRÒ-UD: An Italian Twitter Treebank in Universal Dependencies
Alessandra Teresa Cignarella | Cristina Bosco | Paolo Rosso
Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)

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UPV-28-UNITO at SemEval-2019 Task 7: Exploiting Post’s Nesting and Syntax Information for Rumor Stance Classification
Bilal Ghanem | Alessandra Teresa Cignarella | Cristina Bosco | Paolo Rosso | Francisco Manuel Rangel Pardo
Proceedings of the 13th International Workshop on Semantic Evaluation

In the present paper we describe the UPV-28-UNITO system’s submission to the RumorEval 2019 shared task. The approach we applied for addressing both the subtasks of the contest exploits both classical machine learning algorithms and word embeddings, and it is based on diverse groups of features: stylistic, lexical, emotional, sentiment, meta-structural and Twitter-based. A novel set of features that take advantage of the syntactic information in texts is moreover introduced in the paper.


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Application and Analysis of a Multi-layered Scheme for Irony on the Italian Twitter Corpus TWITTIRÒ
Alessandra Teresa Cignarella | Cristina Bosco | Viviana Patti | Mirko Lai
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)