Brazil

This work investigates the syntactic-semantic proprieties of the verb “falar” (to speak) of Brazilian Portuguese (BP) in a journalistic corpus and three BP verbal databases (VerbNet.Br, VerboWeb and Verbo-Brasil). The data demonstrate a polyvalence and complexity of “falar”. In addition, concerning the analysis of the databases, it was verified a need for manual reviews, made out by linguists, and/or expansions of the linguistic descriptions, especially when considering the relevance of constructions with the verb &quot;falar&quot; for different applications in the Natural Language Processing area

BRACIS 2021

A propósito do verbo falar no português brasileiro: uma análise em corpus e em bases de dados verbais

lexicology and lexicography

corpus linguistics

syntax

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

This work investigates the syntactic-semantic proprieties of the verb “falar” (to speak) of Brazilian Portuguese (BP) in a journalistic corpus and three BP verbal databases (VerbNet.Br, VerboWeb and Verbo-Brasil). The data demonstrate a polyvalence and complexity of “falar”. In addition, concerning the analysis of the databases, it was verified a need for manual reviews, made out by linguists, and/or expansions of the linguistic descriptions, especially when considering the relevance of constructions with the verb "falar" for different applications in the Natural Language Processing area

technical paper

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

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BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

A pesquisa “Gêneros, tipologias textuais e análise linguística: constituição de recursos didáticos para o trabalho contextualizado dos conhecimentos linguísticos em uma abordagem orientada pelos gêneros textuais” objetiva desenvolver uma ferramenta linguístico-computacional de auxílio ao professor para a abordagem de conteúdos de gramática pautados em gêneros textuais, já que a relação entre gêneros textuais e sua construção linguístico-discursiva ainda se constitui em um desafio para o ensino de língua. Esta ferramenta é construída com base em anotações da FrameNet Brasil. Por meio dela, espera-se tornar possível ao usuário a busca por gêneros textuais mais propícios para o ensino de determinados tópicos gramaticais, tendo em vista sua proeminência nesses gêneros.

Ferramenta linguístico-computacional como facilitadora para o ensino de gramática na escola

O maior acesso a celulares conectados à internet e a popularização das redes sociais levaram a um aumento significativo na geração e no compartilhamento de notícias falsas.
Estudos disponíveis na literatura, baseados em pistas linguísticas, sugerem que os autores de conteúdo enganoso exibem comportamento verbal e não verbal diferente dos autores de conteúdo verdadeiro. Desta forma, neste artigo apresentamos a ferramenta LC-Tool, a qual extrai 29 pistas linguísticas de textos.
Ainda, testamos a ferramenta em três corpus sobre conteúdo enganoso disponíveis na Internet. Por fim, percebemos que algumas pistas linguísticas podem ser extensíveis para o idioma português (por exemplo: média do número de verbos e média de pausalidade) e que em outras pistas linguísticas precisam ser validadas, pois são afetadas pelo contexto e domínio das mensagens.

Utilizando Pistas Linguísticas para Detectar Notícias Falsas em Português

O campo da política no Brasil é um dos mais movimentados e mais polêmicos da última década. Com o advento das redes sociais, um novo canal de comunicação entre os eleitores e os políticos foi criado, com os usuários tendo um espaço para publicar suas opiniões e crenças. Neste contexto, este trabalho mostra como modelos BERT se saem na análise de polaridade (positiva,negativa ou neutra) e de tópicos de grandes quantidades de tweets relacionados à política do Brasil. Experimentos foram realizados com tweets relacionados às eleições de 2018, e melhores resultados (F1 = 96%) foram obtidos para a classificação de polaridade em comparação com trabalhos anteriores. A avaliação qualitativa dos tópicos também mostrou resultados promissores.

Análise de polaridade e de tópicos em tweets no domínio da política no Brasil

Apresentamos nosso trabalho no qual realizamos o fine-tuning dos modelos BERTimbau-base e BERTimbau-large na tarefa de classificação de 27 tipos de emoções em sentenças, baseado no dataset GoEmotions traduzido para a língua portuguesa, por meio de ferramentas de traducão automática. Comparamos os resultados de nossos experimentos com os resultados disponibilizados pelos autores do dataset GoEmotions e obtivemos um ganho de desempenho ao qual atribuímos ao algoritmo de balanceamento utilizado. Além disso avaliamos o modelo BERTimbau em um dataset de tweets em português.

Utilizando BERTimbau para a Classificação de Emoções em Português

This study introduces novel data and models for the task of Sentiment
Analysis in Portuguese texts about Diabetes Mellitus. The corpus contains
1290 posts retrieved from online health community forums in Portuguese and
annotated by two annotators according to 3 sentiment categories (e.g. Positive,
Neutral and Negative). Evaluation of traditional (Support Vector Machine,
Decision Tree, Random Forest and Logistic Regression classifiers) and state-ofthe-
art (BERT-based models) machine learning classifiers for the task showed
the advantage in performance of the latter models as expected. Data and models
are available to the community upon request.

Sentiment Analysis in Portuguese Texts from Online Health Community Forums: Data, Model and Evaluation

Affective Computing is the study of how computers can recognize, interpret and simulate human affects. Sentiment Analysis is a common task inNLP related to this topic, but it focuses only on emotion valence (positive, negative, neutral). An emerging approach in NLP is Emotion Recognition, which relies on fined-grained classification. This research describes an approach to create a lexical-based weakly supervised corpus for fine-grained emotion in Portuguese. We evaluated our dataset by fine-tuning a transformer-based language model (BERT) and validating it on a Gold Standard annotated validation set. Our results (F1-score=.64) suggest lexical-based weak supervision as an appropriate strategy for initial work in low resourced environment.

A Weakly Supervised Dataset of Fine-Grained Emotions in Portuguese

Bullying and cyberbullying are words commonly seen in today's news. Although the scientific community has evaluated text mining techniques for cyberbullying detection, few studies have targeted Brazilian Portuguese datasets. Our study aims to assess the text mining application to detect cyberbullying messages written in Brazilian Portuguese. We gathered posts and comments from Reddit communities and extracted several text features. We then processed these features using Naïve Bayes and SVM classifiers to uncover cyberbullying activity. The outcomes of this experiment may not be used solo for cyberbullying detection; however, they can aid moderators in prioritizing content reviews and acting faster on real cyberbullying cases.

Text Mining for Cyberbullying Detection: a Brazilian Portuguese Evaluation

This study presents a new version of the lexical decision test designed to capture the social appreciation of variable phonological phenomena [Freitag and Souza 2019]. The effects of lexical decision were tested in standard and non-standard variants of the phenomena of monophthongtion descending and increasing, denasalization of final unstressed nasal diphthong and palatalization of alveolar stops, in progressive and regressive environment, in a sample of 25 university students from Sergipe. The results reinforce what has already been identified in the previous study: stereotype and marker type variants are significantly associated with non-word; while the indicator variants do not show this relation.

Efeitos da variação linguística na decisão lexical

Apresentamos uma análise comparativa acerca da palatalização de
/t/ e /d/ em duas amostras independentes, e com tamanho distinto, compostas por dados de fala (n = 8.850) e de leitura em voz alta (n = 831) de estudantes da Universidade Federal de Sergipe. Controlamos as variáveis sociais deslocamento e tempo no curso, e as variáveis linguísticas contexto anterior, contexto posterior, tonicidade e sonoridade, com o objetivo de identificar se os efeitos condicionantes da palatalização da fala atuam na leitura em voz alta. Os resultados sinalizam que, mesmo em amostras distintas, o comportamento dos fatores sociais e linguísticos apresentam estabilidade na mudança e propagação da palatalização na comunidade.


Palatalização na fala e na leitura de universitários sergipanos

This sentiment analysis work is focused on the task of identifying aspects, emphasizing the so-called implicit aspects, i.e., those that are not explicitly mentioned in the texts. For this, we analyzed frequency-based methods, adapted rules from the English language to Portuguese, and developed a method that learns new rules through corpus analysis.

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