TWITTIRÒ: an Italian Twitter Corpus with a Multi-layered Annotation for Irony
Provided the difficulties that still affect a correct identification of irony within the context of Sentiment Analysis tasks, in this paper we describe the main issues emerged during the development of a novel resource for Italian annotated for irony. The project mainly consists in the application o...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Accademia University Press
2018
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/abce63f8e2e048babae28facf8e9e684 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Provided the difficulties that still affect a correct identification of irony within the context of Sentiment Analysis tasks, in this paper we describe the main issues emerged during the development of a novel resource for Italian annotated for irony. The project mainly consists in the application on the Twitter corpus TWITTIRÒ of a multi-layered scheme for the fine-grained annotation of irony, as proposed in a multilingual setting and previously applied also on French and English datasets (Karoui et al. 2017). In applying the annotation on this corpus, we outline and discuss the issues and peculiarities emerged about the exploitation of the semantic scheme for Twitter textual messages in Italian, thus shedding some lights on the future directions that can be followed in the multilingual and cross-language perspective too. We present, in particular, an analysis of the annotation process and distribution of the labels of each layer involved in the scheme. This is supported by a discussion of the outcome of the annotation carried on by native Italian speakers in the development of the corpus. In particular, an in-depth discussion of the inter-annotator agreement and of the sources of disagreement is included. The result is a novel gold standard corpus for irony detection in Italian, which enriches the scenario of multilingual datasets available for this challenging task and is ready to be used as a benchmark in automatic irony detection experiments and evaluation campaigns. |
|---|