For two decades, the HAREM corpus has served as the foundational benchmark for Portuguese Named Entity Recognition (NER), establishing its evaluation paradigm. Virtually all major progress has been measured against its fixed train/test split. This paper presents the first systematic audit of this split, revealing 153 overlapping (contaminated) sentences. We re-evaluate 13 NER models (ranging from CRFs to Transformers) on both the original and a new, decontaminated version of the corpus. Our statistical analysis reveals that decontamination has a significant (p < 0.05) and positive impact on the majority of models. We find that performance gains are most pronounced in the F1_textmacro score (up to +4 points), demonstrating that the contamination primarily harmed generalization on rare entity types. Furthermore, our audit reveals clear evidence of overfitting in some models that benefited from data leakage. We conclude that even minor contamination can distort performance metrics and mask true model generalization. We release our decontaminated benchmark to ensure more reliable future evaluations.

The PROPOR conference has been the main venue for Portuguese language Natural Language Processing (NLP) research for over two decades. This paper presents a longitudinal bibliometric analysis of PROPOR from 2003 to 2024, examining thematic evolution, community structure, and scientific impact. We identify a shift from speech-oriented research toward text-based tasks, alongside the sustained importance of resources and linguistic theory. The community exhibits a stable structure, with complementary leadership models centered on institutional hubs and brokerage roles. Scientific impact is highly concentrated, following a long tail distribution, and distinguishes between cumulative productivity-driven impact and rapidly accelerating citation uptake in recent editions. These findings characterize PROPOR as a resilient regional linguistic ecosystem evolving in dialogue with broader NLP paradigms.

In recent decades, multimodal models such as CLIP have achieved significant advances in associating images and texts. However, most of these advances stem from models trained almost exclusively in English, which limits their effectiveness in other languages. This challenge is particularly relevant for Brazilian Portuguese, a language that still lacks dedicated multimodal resources and relies predominantly on automatic translations. This work investigates the performance of CLIP-based multimodal models in the task of associating images and descriptions written in Brazilian Portuguese. The analysis begins with a zero-shot scenario, in which different CLIP variants are directly evaluated on the FM30k dataset, composed of images and captions originally written in Portuguese. An additional experiment with automatic translations is also conducted to examine the impact of language on cross-modal retrieval tasks. Subsequently, fine-tuning is performed on the textual encoder of the ViT-B/32 model, keeping the visual encoder frozen, with the goal of adapting the model to the target language. The results show that models originally trained in English perform worse in Portuguese, while linguistically adapted variants, either multilingual or Portuguese-specific, achieve superior performance. The proposed fine-tuning approach was able to reduce this performance gap, leading to notable improvements. In the image-to-text scenario, the model achieved an absolute increase of 27.65 percentage points in the Accuracy@1 metric, representing a 209% relative gain over the original CLIP ViT-B/32. In the text-to-image scenario, the gain was 15.47 percentage points, amounting to an even higher 385% relative improvement, contributing to a more balanced association between images and captions.