Ely Edison Matos

Also published as: Ely Edison Matos


Modeling Construction Grammar’s Way into NLP: Insights from negative results in automatically identifying schematic clausal constructions in Brazilian Portuguese
Arthur Lorenzi | Vânia Gomes de Almeida | Ely Edison Matos | Tiago Timponi Torrent
Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)

This paper reports on negative results in a task of automatic identification of schematic clausal constructions and their elements in Brazilian Portuguese. The experiment was set up so as to test whether form and meaning properties of constructions, modeled in terms of Universal Dependencies and FrameNet Frames in a Constructicon, would improve the performance of transformer models in the task. Qualitative analysis of the results indicate that alternatives to the linearization of those properties, dataset size and a post-processing module should be explored in the future as a means to make use of information in Constructicons for NLP tasks.


Lutma: A Frame-Making Tool for Collaborative FrameNet Development
Tiago Timponi Torrent | Arthur Lorenzi | Ely Edison Matos | Frederico Belcavello | Marcelo Viridiano | Maucha Andrade Gamonal
Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022

This paper presents Lutma, a collaborative, semi-constrained, tutorial-based tool for contributing frames and lexical units to the Global FrameNet initiative. The tool parameterizes the process of frame creation, avoiding consistency violations and promoting the integration of frames contributed by the community with existing frames. Lutma is structured in a wizard-like fashion so as to provide users with text and video tutorials relevant for each step in the frame creation process. We argue that this tool will allow for a sensible expansion of FrameNet coverage in terms of both languages and cultural perspectives encoded by them, positioning frames as a viable alternative for representing perspective in language models.