ISO-based annotated multilingual parallel corpus for discourse markers

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Silvano, Maria da Purificação (Autor) FLUP; Damova, Mariana (Autor) Outra; Valunaité, Giedré Oleskeviciené (Autor) Outra; Liebeskind, Chaya (Autor) Outra; Chiarcos, Christian (Autor) Outra; Trajanov, Dimitar (Autor) Outra; Ciprian-Octavian, Truica (Autor) Outra; Apostol, Elena-Simona (Autor) Outra; Baczkowska, Anna (Autor) Outra

Ata de Conferência Internacional

Título: Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022)

Páginas: 2739-2749

LREC 2022: Language Resources and Evaluation Conference, 13

Marseille, 2022

Outras Informações

Abstract (EN): Discourse markers carry information about the discourse structure and organization, and also signal local dependencies or epistemological stance of speaker. They provide instructions on how to interpret the discourse, and their study is paramount to understand the mechanism underlying discourse organization. This paper presents a new language resource, an ISO-based annotated multilingual parallel corpus for discourse markers. The corpus comprises nine languages, Bulgarian, Lithuanian, German, European Portuguese, Hebrew, Romanian, Polish, and Macedonian, with English as a pivot language. In order to represent the meaning of the discourse markers, we propose an annotation scheme of discourse relations from ISO 24617-8 with a plug-in to ISO 24617-2 for communicative functions. We describe an experiment in which we applied the annotation scheme to assess its validity. The results reveal that, although some extensions are required to cover all the multilingual data, it provides a proper representation of discourse markers value. Additionally, we report some relevant contrastive phenomena concerning discourse markers interpretation and role in discourse. This first step will allow us to develop deep learning methods to identify and extract discourse relations and communicative functions, and to represent that information as Linguistic Linked Open Data (LLOD).

Idioma: Inglês

Tipo (Avaliação Docente): Científica