Marking Irony Activators in a Universal Dependencies Treebank: The Case of an Italian Twitter Corpus

Alessandra Teresa Cignarella, Manuela Sanguinetti, Cristina Bosco, Paolo Rosso


Abstract
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.
Anthology ID:
2020.lrec-1.627
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5098–5105
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.627
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.627.pdf