Magali Duran

Author directory

Also published as: Magali Sanches Duran

Other people with similar names: Magali Sanches Duran

Unverified author pages with similar names: Magali Sanches Duran


2026

This paper explores the potential of Enhanced and Extended Enhanced Universal Dependencies for narrative analysis in Brazilian Portuguese, focusing on how enhanced dependency relations contribute to clause linkage. The study examines narrative sentences annotated in CoNLL-U format and compares basic and enhanced dependency layers. The results show that enhanced dependencies are particularly useful for recovering implicit subject relations in Portuguese and for enriching clause relations through information from prepositions and conjunctions. These enhancements enable a more explicit representation of participant continuity and event relations in narratives, supporting the transition from syntactic dependency analysis to richer semantic annotation.
Relatamos, neste artigo, o processo de anotação do icônico livro O Pequeno Príncipe segundo o modelo internacional Universal Dependencies, como parte de um esforço de expansão dos dados anotados atualmente disponíveis para o português do Brasil. Em especial, apresentamos o protocolo de anotação e as decisões linguísticas envolvidas, além de uma análise quantitativa do córpus.
This paper presents the first Brazilian Portuguese dataset annotated for Uniform Meaning Representation (UMR). It contains 96 sentences from the Brazilian Portuguese portion of the Parallel Universal Dependencies treebank, parallel to existing UMR annotations in English, Czech, and Italian. The sentences were parsed with PortParser, revised in Arborator-Grew, converted from CoNLL-U into preliminary sentence-level UMR graphs, and manually revised in PENMAN format. The results show that CoNLL-U is a useful starting point, but semantic graph construction requires manual interpretation and language-specific lexical resources. The dataset expands multilingual UMR coverage and supports future Portuguese semantic annotation.