Coreference resolution is a crucial task in natural language processing (NLP) that aims to identify and link expressions in a text that refer to the same entity. However, the lack of annotated data for coreference resolution in Portuguese has hindered the development of robust and accurate systems for this language. In this paper, we present an assessment of coreference annotation utilizing large language models (LLMs) for Portuguese: LLM-PREF is proposed to annotate coreference in Portuguese texts. It was evaluated and compared to a system previously proposed in the literature. The results show that although the model’s world knowledge and inference capacity are quite rich - allowing it to recognize complex coreference patterns, including the pronominal anaphora phenomenon - it does not excel the previously developed rule based system.

This paper describes Bruna, a data-centric smart voice assistant powered by multiple Large Language Models designed to support Stilingue and Blip products. Our architecture provides an enriched conversational experience, delivering strategic insights in real-time.

This paper presents the adaptation of an Entity Centric Model for Portuguese coreference resolution, considering 10 named entity categories. The model was evaluated on named e using the HAREM Portuguese corpus and the results are 81.0% of precision and 58.3% of recall overall, the resulting system is freely available

This paper presents Summ-it++, an enriched version the Summ-it corpus. In this new version, the corpus has received new semantic layers, named entity categories and relations between named entities, adding to the previous coreference annotation. In addition, we change the original Summ-it format to SemEval

This paper describes an experiment to compare four tools to recognize named entities in Portuguese texts. The experiment was made over the HAREM corpora, a golden standard for named entities recognition in Portuguese. The tools experimented are based on natural language processing techniques and also machine learning. Specifically, one of the tools is based on Conditional random fields, an unsupervised machine learning model that has being used to named entities recognition in several languages, while the other tools follow more traditional natural language approaches. The comparison results indicate advantages for different tools according to the different classes of named entities. Despite of such balance among tools, we conclude pointing out foreseeable advantages to the machine learning based tool.