Brazil

Most misinformation corpora are composed of explicitly real and fake news content. This results from the idea that misinformation can be approached as a binary classification problem. However, such approach oversimplifies the diversity of properties usually associated with credibility of different textual genres and types. To address this problem, we created MINT, a comprehensive annotated corpus of online articles collected from mainstream and independent Portuguese media sources, over a full year period. The collected articles include: hard news, opinion articles, soft news, satirical news, and conspiracy theories. This paper describes the main linguistic properties underlying each category, and provides some insights based on the analysis of an annotation initiative performed by online readers. The results show that (i) conspiracy theories and opinion articles present similar levels of subjectivity, and make use of fallacious arguments; (ii) irony and sarcasm are not only prevalent in satirical news, but also in conspiracy and opinion articles; and (iii) hard news differ from soft news by resorting to more sources of information, and presenting a higher degree of objectivity.

PROPOR 2022

Mainstream and Independent News Text Corpus

conspiracy

satire

corpus

opinion

misinformation

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician. The meeting has been a very rich forum for the exchange of ideas and partnerships for the research and industry communities dedicated to the automated processing of Portuguese, promoting the development of methodologies, resources and projects that can be shared among researchers and practitioners in the field.

Find more information and pay the registration fee on [event organizer’s website](https://sites.universidadedefortaleza.com/propor2022/?page_id=401)

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician.

poster

We propose a methodology to populate the verb type hierarchy of a deep computational grammar in the HPSG formalism using syntactic and morphological information from Universal Dependencies (UD) treebanks. It is exemplified by means of the UD Bosque corpus and PorGram, a computational grammar for Portuguese constructed in the LinGO Grammar Matrix framework, but it can be applied to analogous grammars of other languages using other UD treebanks. The main component of the methodology is a Python module that extracts from the annotated sentences the core arguments and other features that are relevant to determine verb valence. This module enables the creation of a Python dictionary that maps valence frames to verb objects. This dictionary facilitates not only determining which frames occur with which verbs, but also detecting annotation errors. The potential of the module for rapid expansion of the lexical coverage of PorGram and corpus annotation error detection is illustrated with concrete examples.

Extracting valences from a dependency treebank for populating the verb lexicon of a Portuguese HPSG grammar

Fake news has become a research topic of great importance in Natural Language Processing due to its negative impact on our society. Although its pertinence, there are few datasets available in Brazilian Portuguese and mostly comprise few samples. Therefore, this paper proposes creating a new fake news dataset named FakeRecogna that contains a greater number of samples, more up-to-date news, and covering a few of the most important categories. We perform a toy evaluation over the created dataset using traditional classifiers such as Naive Bayes, Optimum-Path Forest, and Support Vector Machines. A Convolutional Neural Network is also evaluated in the context of fake news detection in the proposed dataset.

A New Brazilian Corpus for Fake News Detection

Abstract Meaning Representation (AMR) is a semantic formalism that has been widely adopted in the area of semantic parsing. We present in this paper our contribution to the task for Portuguese. We investigated semantic parsing methods of different paradigms, producing state-of-the-art results for this language. We also introduced the first AMR-annotated corpus for Portuguese, a novel and better semantic parsing evaluation measure, and a new AMR-based alignment method.

Abstract Meaning Representation Parsing for the Brazilian Portuguese Language

Languages evolve internally and externally in relation to themselves and to others. It is for this reason that the automatic calculation of the historical and current distance between languages from corpora is an interesting challenge. In this thesis we have developed a methodology for calculating this distance from perplexity, a metric that has also proved to be suitable for automatic identification between languages. We have also tested the role that orthography plays in the distance between languages. Finally, we have created an adhoc corpus called Carvalho in orthographies as close as possible to the original ones, where we have carried out our experiments.

Corpus-based measures of distance between languages and variants. Application to the historical linguistics of Galician, Portuguese, Spanish and English

In the legal domain, documents have peculiarities related to their length, specialized use, and vocabulary. Recent neural network-based approaches can generate high-quality summaries. However, these approaches have been used mostly for creating concise abstracts for news articles in English and suffer from the "hallucination", i.e. the inclusion of extraneous material that is not supported by the original text. 
In this work, we propose LegalSumm, a method for summarizing legal texts in Portuguese. Our solution relies on Transformers and textual entailment is used to reduce hallucinations. To cope with long documents, we propose different chunking strategies. We compared LegalSumm to strong extractive and abstractive summarization baselines and collected the opinion of legal experts on the automatically generated summaries. The results show that LegalSumm outperforms existing summarization methods.

Summarizing Legal Rulings

In the hospital environment, the incidence of adverse events (unforeseen incidents that cause harm to patients) is the primary concern of risk management teams. The use of machine learning techniques may help healthcare professional to identify and mitigate adverse events. This thesis develops two experiments to evaluate machine learning approaches to identify two major adverse events in electronic health records. In this extended abstract we cover the algorithm created to identify fall events in clinical notes, as they are closely related to the processing of portuguese language. We annotated 1,402 clinical sentences with fall events to train a Token Classifier (TkC) to detect words within the context of falls. The TkC was able to correctly identify 85% of the sentences with fall events.

Applying Machine Learning to Electronic Health Records: a Study on Two Adverse Events

Tackling Multilingual Media Monitoring with Natural Language Processing

From (Our) Past to (Our) Future with Natural Language Processing (NLP): Linguistic and Terminological Approaches to Digital Historical Epidemiology in Portuguese

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Extracting valences from a dependency treebank for populating the verb lexicon of a Portuguese HPSG grammar

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