Portuguese Texts Research Articles

Pyrostories: New Historical Insights into Portuguese Burning Landscapes

This paper examines Portuguese literary texts in which fire is explicitly included in the narrative. These texts include descriptions of various occurrences and uses of fire and their intertwined social and ecological effects. They shed light on the origins, actors, practices, and impacts of fire, and they reveal past perceptions of fire, namely the role fire played in social processes and in the making of landscapes. It becomes evident that in literary texts, fire is not merely a physical element but also a powerful symbolic force of life, death, and transformation. Furthermore, the literary landscapes of different regions describe fire in distinct ways, reflecting particular geographical, social, and political contexts.

DESENVOLVIMENTO DA PSICOLOGIA ESCOLAR NO BRASIL: UMA REVISÃO SISTEMÁTICA INTEGRATIVA

ABSTRACT – The main objective of this study was to carry out a survey on the evolution of school psychology in Brazil. The methodological approach adopted consisted of an integrative systematic review of the literature. The search for articles was conducted in three digital databases: Lilacs, Nursing Database and SciELO, using the search terms: Development, Teaching and Learning, History and School Psychology. The inclusion criteria covered texts in Portuguese and English that were available in their entirety and that discussed the development of school psychology in Brazil, in addition to exploring the main opportunities for progress in consolidating this area, considering the Brazilian reality. In contrast, articles that did not directly address the topic or that did not clearly present the contribution of the work of the school psychologist were excluded. The research faced some limitations due to the scarcity of specific publications on the development of school psychology. However, all articles analyzed highlighted the importance of the role of the psychologist in the educational context and the improvement of pedagogical practices. The inclusion of this professional in educational institutions is seen as crucial for the application of psychological knowledge in the formulation of proposals that prioritize education and humanization. The ten articles reviewed emphasize the relevance of the school psychologist and record that since 1930, several initiatives have been carried out to ensure the inclusion of this professional in schools, with the aim of improving the quality of teaching and supporting students with learning difficulties.

Evaluation of deep neural network architectures for authorship obfuscation of Portuguese texts

Preserving authorship anonymity is paramount to protect activists, freedom of expression, and critical journalism. Although there are several mechanisms to provide anonymity on the Internet, one can still identify anonymous authors through their writing style. With the advances in neural network and natural language processing research, the success of a classifier when identifying the author of a text is growing. On the other hand, new approaches that use recurrent neural networks for automatic generation of obfuscated texts have also arisen to fight anonymity adversaries. In this work, we evaluate two approaches that use neural networks to generate obfuscated texts. The first approach uses Generative Adversarial Networks to train an encoder–decoder to transform sentences from an input style into a target style. The second one trains an auto encoder with Gradient Reversal Layer to learn invariant representations. In our experiments, we compared the efficiency of both techniques when removing the stylistic attributes of a text and preserving its original semantics. Our evaluation on real texts clarifies each technique’s trade-offs for Portuguese texts and provides guidance on practical deployment.

A multimodal contrastive analysis of regulations and instructions during the COVID-19 lockdown in the context of the Island of Madeira and the United Kingdom

Abstract The paper analyses aspects of multilingual and multimodal narratives employed during the COVID-19 lockdown, attempting to identify how they were used in professional and public environments. More specifically, the paper looks at editing choices, photography, design, drawing, colour, and writing to get a better understanding of multimodal communication, semiotics, image analysis, and their correlation with the written text and the way it has been perceived and interpreted, be it in online or print-based contexts. Methodologically, this is a contrastively inspired research project, looking specifically at Portuguese-speaking contexts of the Island of Madeira and English-speaking contexts of the United Kingdom, taking into account instructions about the COVID-19 pandemic. These are mainly bilingual English–Portuguese and multilingual texts, multimodal manifestations, and communicative and semiotic effectiveness.

TeenyTinyLlama: Open-source tiny language models trained in Brazilian Portuguese

Large language models (LLMs) have significantly advanced natural language processing, but their progress has yet to be equal across languages. While most LLMs are trained in high-resource languages like English, multilingual models generally underperform monolingual ones. Additionally, aspects of their multilingual foundation sometimes restrict the byproducts they produce, like computational demands and licensing regimes. In this study, we document the development of open-foundation models tailored for use in low-resource settings, their limitations, and their benefits. This is the TeenyTinyLlama pair: two compact models for Brazilian Portuguese text generation. We release them under the permissive Apache 2.0 license on GitHub and Hugging Face for community use and further development.

A Natural Language Processing approach to Complexity Assessment of 18th-century health literature

In this paper, we present an experiment for complexity-level analysis of Portuguese texts from the 18th century using NLP tools. The 18th century was the time for the realization of a new world that had been built since the Renaissance, it was the period of consolidation of many of the current sciences. One of its characteristics is the presentation of scientific written records in national languages, rather than Latin, and the expressed wishes that the specialized texts could be more understandable to people of lesser erudition. As such, we intend to collaborate to identify if and how these wishes were fulfilled. To achieve this goal, we resort to an NLP supporting methodology to detect degrees of complexity of a medical work of this time period and compare it with two other works that have hypothesized lesser and greater complexities. By using NILC-Metrix, we intend to identify features of a continuum of complexity in this kind of document.

"What Do We Know about Hope in Nursing Care?": A Synthesis of Concept Analysis Studies.

Hope is a central concept within the nursing literature, which is crucial towards the development of nursing knowledge. Nursing teams play a crucial role in exploring the meaning of hope and promoting hope among patients and their families. This study aims to synthesize concept analysis studies of hope in the context of nursing care and to propose an evidence-based update of the definition of hope in the International Classification for Nursing Practice (ICNP®). Method: This is a literature review, involving the synthesis of studies concerning the concept analysis of hope in nursing practice. Peer-reviewed articles with fully accessible Portuguese or English text were considered. As we aimed to include a historical perspective of the concept, no restriction upon the time of publication was set. Articles were selected in March 2022 and updated in July 2023 using the Medline, CINAHL, and Scopus databases. The search terms used were "hope" AND "concept" AND "analysis" AND "nurs*". Only articles written in English or Portuguese were included. Two reviewers conducted the research synthesis and report independently to minimize the risk of bias in the included studies. This paper adheres to the PRISMA checklist. To clarify the concept of hope as perceived by patients and develop hope as an evidence-based nursing concept, 13 studies were reviewed. The concept of hope, its attributes, antecedents, and consequences, as well as similar concepts, were studied by nurses and synthesized into a definition. The identified antecedents include pivotal life events, stressful stimuli, and experiencing satisfaction with life. The analysis of its attributes, antecedents, and consequences has contributed to understanding its relevance in nursing care and provided a proposed update of hope in the ICNP®. This review provides conceptual clarity on how hope is defined and used in nursing practice and the potential factors that may impact the promotion of hope to provide opportunities for future nursing research.

The Primary Gate of the Melaka Sultanate

Based on the historical Malay, China and Arab textual account, there was a primary gate that marked the entrance to the royal compound of Melaka Sultanate before 1511. The primary gate was arguably among the most iconic construct and strategic to the Sultanate city, at security, socio-cultural, symbolic and aesthetic levels. However, no critical study has been conducted in tracing the form, position, and its relation to the municipality of the Sultanate Melaka City. This study employs narrative analysis framework to carefully investigate descriptive clues to provide the big picture on the primary gate of Melaka Sultanate in view of historical texts from Malay, China, and Arab; all of which were written within or close to the Sultanate period. The findings of the narrative analysis were compared to a collection of municipal plans from the Portuguese and Dutch Melaka eras. This reconstruction study also took precedence of an ancient Malay sultanate gate for the design process which can be seen carrying the tradition of Sultanate Melaka gate. The finding of the study suggests the form, position and orientation of the sultanate Melaka gate. The study also discusses and proposes the route which could possibly accommodate another 6 gates before reaching the sultanate Melaka palace. The study has limitations since it relies on English translation in dealing with ancient Portuguese texts. The long-term goal of this reconstruction study is to promote historical Melaka identity tourism, which is in line with SDGs 8.9 and 11.4.

CachacaNER: a dataset for named entity recognition in texts about the cachaça beverage

Named Entity Recognition (NER) is the task of identifying and classifying tokens in texts corresponding to a set of pre-defined categories, such as names of people, organizations and locations. Datasets labeled for this task are essential for training supervised machine learning models. Although there are many datasets labeled with texts for English, in the Portuguese language they are scarcer. This work contributes to the creation and evaluation of a manually labeled dataset for the NER task, with texts in Brazilian Portuguese, in the specific domain of the beverage called Cachaça. This is a popular drink in Brazil, and of great economic importance. This is the first NER dataset in the beverage domain, and can be useful for other types of beverages with similar entity categories, such as wine and beer. We describe the process of data collection, creation of the dataset and its experimental evaluation. As a result, we created a dataset containing over 180,000 tokens labeled in 17 entity categories. The labeling obtained an agreement coefficient of 0.857 among the labelers, according to the Fleiss’ Kappa metric, which is considered almost perfect. In our experimental evaluation, we obtained a micro-F1 value equal to 0.933 in the test set. The size of the dataset, as well as the result of its experimental evaluation, are comparable to other datasets in the Portuguese language, even though ours has a greater number of entity categories.

The production of mRNA vaccines in the COVID-19 pandemic: advent of new applications in health treatments

Modern vaccines developed using mRNA technology are highly accurate formulations designed to stimulate the production of a variety of antigenic properties in the user in order to activate adaptive immunity. This enables the use of such technology in the research of vaccines against several infectious diseases, such as hepatitis C, Acquired Immunodeficiency Syndrome (AIDS), malaria, tuberculosis, in addition to chronic diseases, such as some types of cancer. Such vaccines were an efficient alternative in the fight against COVID-19 and have remarkable characteristics, such as the versatility of their applications and the ability to encode and express virtually any type of protein. Like this, this work is justified by the need to know the potential applications of this technology in clinical practice and in the search for effective alternative solutions in the fight against infectious and chronic diseases. A systematic review of the literature was carried out, including texts in Portuguese or English from 2019 to the present day and original texts published before 2019, searched in databases such as Science Direct, US National Library of Medicine, Scielo, Pubmed and Instituto Nacional de Industrial Property (INPI).

Memoria, ideologia e identità nazionale nelle rivisitazioni sette-ottocentesche dell’elogio della lingua portoghese

"Numerous Portuguese texts and paratexts from the era of the dual monarchy (1580-1640) are characterised by eulogies of the Lusitanian language that fulfill the task of defending its prestige over Spanish. Between the end of the 18th century and the first half of the 19th century, when the production of translations, grammars, dictionaries and other glottodidactic tools of various natures increased, the memory of the ancient praises was relied upon for purposes that were (justifying the study of Portuguese as a foreign language), politico- cultural (defending the dignity of Portuguese within the neo-Latin stock; making space for the Lusitanian literary tradition in the European cultural space), and commercial (in the context of the modern publishing industry, creating a market for Lusophone texts and manuals)."

OLID-BR: offensive language identification dataset for Brazilian Portuguese

Social media has revolutionized the manner in which our society is interconnected. While this extensive connectivity offers numerous benefits, it is also accompanied by significant drawbacks, particularly in terms of the proliferation of fake news and the vast dissemination of hate speech. Identifying offensive comments is a critical task for ensuring the safety of users, which is why industry and academia have been working on developing solutions to this problem. Prior research on hate speech detection has predominantly focused on the English language, with few studies devoted to other languages such as Portuguese. This paper introduces the Offensive Language Identification Dataset for Brazilian Portuguese (OLID-BR), a high-quality NLP dataset for offensive language detection, which we make publicly available. The dataset contains 6,354 (extendable to 13,538) comments labeled using a fine-grained three-layer annotation schema compatible with datasets in other languages, which allows the training of multilingual/cross-lingual models. The five NLP tasks available in OLID-BR allow the detection of offensive comments, the classification of the types of offenses such as racism, LGBTQphobia, sexism, xenophobia, and so on, the identification of the type and the target of offensive comments, and the extraction of toxic spans of offensive comments. All those tasks can enhance the capabilities of content moderation systems by providing deep contextual analysis or highlighting the spans that make a text toxic. We further experiment with and evaluate the dataset using state-of-the-art BERT-based and NER models, which demonstrates the usefulness of OLID-BR for the development of toxicity detection systems for Portuguese texts.

Evaluation of the Brazilian Portuguese version of linguistic inquiry and word count 2015 (BP-LIWC2015)

Text psycholinguistic features are a valuable source for various research topics since they are used to obtain psychological, social, and linguistic aspects from written texts using dictionary files. These files are structured in categories, which are defined as groups of dictionary words that tap a particular domain (e.g., negative emotion words). The Linguistic Inquiry Word Count (LIWC) is a vastly used and versatile computer-based language analysis tool designed for text psycholinguistic analysis. The most recent version of the default English dictionary is LIWC2015, as it was released with the 2015 version of the LIWC software. The literature has recently introduced the latest Brazilian Portuguese LIWC dictionary (BP-LIWC2015), developed with the same categories as the LIWC 2015 English dictionary. However, the literature has also reported the need to evaluate BP-LIWC2015. In this scenario, this work investigates three questions: (i) Since LIWC2015 shows consistent improvements over the English dictionary developed in 2007 (LIWC2007), does BP-LIWC2015 achieves better text classification results than the older Brazilian Portuguese dictionary (BP-LIWC2007)? (ii) What is the equivalence between BP-LIWC2015 and BP-LIWC2007 with LIWC2015? (iii) Are there significant differences between Brazilian Portuguese dictionaries? To answer these questions, we conducted text classification experiments with four datasets and seven classification algorithms to compare the two Brazilian Portuguese LIWC dictionaries reported in the literature (i.e., 2007 and 2015). Second, we used a bilingual Portuguese-English scientific news collection to analyze the correlation between LIWC2015 and Brazilian Portuguese LIWC dictionaries. The results indicate that BP-LIWC2015 outperforms the older version in Brazilian Portuguese text classification. Finally, we found a more significant correlation between BP-LIWC2015 and the original English dictionary than the older version.

Lúpus Eritematoso Sistêmico Juvenil: atualização acerca das manifestações clínicas, diagnóstico e tratamento

OBJECTIVES: To carry out a bibliographic review of the literature regarding the main concerns of Juvenile Systemic Lupus Erythematosus (JSLE). DATA SOURCES: The research was carried out using articles in the PubMed and Scielo databases, with texts in English and Portuguese only. The search was carried out using the descriptors: lupus, pediatric, childhood, diagnosis. The selected articles were published from the year 2016 onwards. Articles that did not address the pediatric population at any time in the text were excluded. Thus, 12 articles were selected that contemplated the objective of the study. The collection period of the articles within the databases was from October to November 2022. DATA SYNTHESIS: Introduction and aspects of general methods, diagnosis and differential diagnoses, laboratory investigation, manifestations and treatment. CONCLUSION: Lupus affects both the adult pediatric population and a more in-depth study of this comorbidity in the disease of the pediatric population is necessary for a better understanding of the nuances that the disease involves.

Almeida Bible—Keeping a Heritage Alive: The Historical Path and Current Challenges of a Seventeenth-Century Translation

Until the seventeenth century, no Portuguese person or any other individual had either planned or accomplished a full translation of the Bible into Portuguese. As research has shown, there is a significant quantity of partial Bible texts in Portuguese during the late medieval period, but it was João Ferreira de Almeida (ca. 1628–1691) who undertook the “first common translation … of the Sacred Books.” Since the mid-eighteenth century, the “Almeida Bible” has been published in different versions, first in the Far East—Java and India—and, from the early nineteenth century, in Europe and America. This paper focuses on the historical context in which the Bible translation undertaken by João Ferreira de Almeida emerged and the main characteristics of the translation. It also provides a historical analysis of the revisions that the initial texts underwent in their different chronological and geographical environments, and ends with suggestions for a new revision of the European Portuguese version of the Almeida Bible.

THE KNOWLEDGE AND IMPACT OF NURSING CARE FOR PATIENTS WITH END OF LIFE KENNEDY’S END ULCER

Introduction: Kennedy Terminal Ulcer (UTK) is a disease that affects patients at the end of life. It occurs due to physiological changes that damage the body’s homeostatic mechanisms, even with adequate interventions or a high standard of well-being. UTK has a sudden and rapid onset and tissue deterioration evolves over the course of a few hours. In this sense, identification, diagnosis, treatment and adequate management are important for the nursing team in directing and comforting the patient at the end of life. Objective: To recognize, through a bibliographical review, the knowledge and impact of nursing care for patients with Terminal Kennedy Ulcers in the final stage of life. Methodology: This is a descriptive bibliographic review research, built from materials published between 2016 and 2022. To select the texts, an online search was carried out in the VHL Database (Virtual Health Library). We considered 07 publications that met the theme of the study, published in full, with full texts available, in Portuguese, Spanish and English. The keywords investigated were: Kennedy Terminal Ulcer, Terminality, End of life. Results: Regarding nursing knowledge, the absence of early diagnosis or correct diagnosis from the identification of the Ulcer and the lack of knowledge about the characteristics of its development was evidenced. As for the impact of nursing care, it was clear that with the correct diagnosis, there is the preparation of the nursing team and family members for the palliative approach, bringing comfort to the affected person, better instruction for family members, and the team’s assertive direction of nursing for a death without suffering. Final considerations: UTK is a great challenge for nursing, as its commitment goes far beyond the biological scope, it interferes in the way of life of the affected people, their families and social groups. There is still a shortage of scientific studies by the national community, in particular by nurses, demonstrating a deficit of knowledge related to care, and with erroneous classification and identification of the UTK.

Words Similarities on Personalities: A Language-Based Generalization Approach for Personality Factors Recognition

The evaluation of personality traits allows the study of human behavior in different environments, but it is not a trivial task. In this sense, the Five-Factor Model (FFM) allows, in a global way, the assessment of personality traits of individuals using textual data. However, there is a scarcity of lexical resources for languages other than English, which generated the main research question of this work: "Can models trained to predict FFM personality traits using English textual data show satisfactory results when applied to textual data in other languages?". Therefore, this work aims to answer: (i) Whether Word Embeddings techniques could be used to solve low resources languages problems in FFM personality traits prediction; and (ii) Whether is feasible to train a traditional Machine Learning algorithm with English language textual data and evaluate its performance with Brazilian Portuguese language textual data for FFM personality traits prediction. Thus, the work aims to present an approach in which the models can be used to learn the highest level of abstraction. As results, we observed that the difference in performance between the models trained for personality recognition in English is minimal when used to predict FFM personality traits in Brazilian Portuguese texts. In this task, the Stochastic Gradient Descent model presents the best average results among the FFM personality traits of the models analyzed.

Embedding generation for text classification of Brazilian Portuguese user reviews: from bag-of-words to transformers

Text classification is a natural language processing (NLP) task relevant to many commercial applications, like e-commerce and customer service. Naturally, classifying such excerpts accurately often represents a challenge, due to intrinsic language aspects, like irony and nuance. To accomplish this task, one must provide a robust numerical representation for documents, a process known as embedding. Embedding represents a key NLP field nowadays, having faced a significant advance in the last decade, especially after the introduction of the word-to-vector concept and the popularization of Deep Learning models for solving NLP tasks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based Language Models (TLMs). Despite the impressive achievements in this field, the literature coverage regarding generating embeddings for Brazilian Portuguese texts is scarce, especially when considering commercial user reviews. Therefore, this work aims to provide a comprehensive experimental study of embedding approaches targeting a binary sentiment classification of user reviews in Brazilian Portuguese. This study includes from classical (Bag-of-Words) to state-of-the-art (Transformer-based) NLP models. The methods are evaluated with five open-source databases with pre-defined data partitions made available in an open digital repository to encourage reproducibility. The Fine-tuned TLMs achieved the best results for all cases, being followed by the Feature-based TLM, LSTM, and CNN, with alternate ranks, depending on the database under analysis.

Transformer-based models for ICD-10 coding of death certificates with Portuguese text

Natural Language Processing (NLP) can offer important tools for unlocking relevant information from clinical narratives. Although Transformer-based models can achieve remarkable results in several different NLP tasks, these models have been less used in clinical NLP, and particularly in low resource languages, of which Portuguese is one example. It is still not entirely clear whether pre-trained Transformer models are useful for clinical tasks, without further architecture engineering or particular training strategies. In this work, we propose a BERT model to assign ICD-10 codes for causes of death, by analyzing free-text descriptions in death certificates, together with the associated autopsy reports and clinical bulletins, from the Portuguese Ministry of Health. We used a novel pre-training procedure that incorporates in-domain knowledge, and also a fine-tuning method to address the class imbalance issue. Experimental results show that, in this particular clinical task that requires the processing of relatively short documents, Transformer-based models can achieve very strong results, significantly outperforming tailored approaches based on recurrent neural networks.

Analysis and Research Based on the Crowdsourcing Corpus System in Guangdong-Hong Kong-Macao Greater Bay Area (GBA)

Objective. To sort out the application and status quo of some domestic crowdsourcing models, explore the factors affecting multilingual manual annotation through experiments and offer suggestions. Methodology. Crawling the government news texts in Mandarin, Cantonese, English, and Portuguese in Guangdong, Hong Kong, and Macao, and enter them into the database. Combine it with corpus tagging uses the established web platform to practice crowdsourcing and collect a large number of annotation results and behavior data. Results. Made assumptions about factors that may affect the quality of manual annotation, used SPSS and other data analysis software to evaluate the degree of interpretation of the assumptions, provided a regression formula for calculating the accuracy, and provided constructive suggestions for the corpus annotation quality assurance projects. Limitations. More corpus information in more languages and more professional annotators are needed. Conclusions. The study found that the accuracy of annotation is strongly related to the attributes of the corpus itself, such as the total number of vocabularies, the number of rare words, the complexity of parts of speech, etc., and the condition of languages are different from each other.