Brazil

Com o crescente interesse e necessidade na criação de ferramentas (semi)automáticas que auxiliem o processo de letramento em gêneros textuais acadêmicos, apresentamos neste trabalho o corpus de resumos científicos em ciências sociais aplicadas. Nosso objetivo foi estudar, a nível sentencial, a estrutura retórica dos resumos das áreas de Administração Pública, Contabilidade e Economia, com base na tipologia proposta na literatura, por meio da ferramenta de anotação de corpus WebAnno. Tivemos como resultado, (i) a organização de um conjunto de 200 textos, (ii) o estudo preliminar da estrutura retórica das referidas áreas e (iii) a produção de um manual de anotação com diretrizes específicas sobre a identificação da estrutura retórica dos resumos científicos.

BRACIS 2021

Criação e Anotação do corpus de resumos científicos de Ciências Sociais Aplicadas

letramento acadêmico

anotação de corpus

linguística de corpus

**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**.

To access the 10th Brazilian Conference on Intelligent Systems (**BRACIS**), you need to register for the conference.  Please visit the Conference official site to register

[Register now](http://c4ai.inova.usp.br/bracis2021/)

Register now

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

poster

This work proposes a machine learning approach to predict the prognosis
of patients with COVID-19. To assist in this task, a descriptive analysis
and relative risk estimation were performed. In addition, the importance
of variables in the perspective of machine learning algorithms was computed
and discussed. The experiments were performed with large-scale nation-wide
dataset from Brazil. The results reveal that the model developed was able to
predict the patient’s prognosis with an AUC = 0.8382. The results also point
out that the chance of death is greater among patients over 60 years old, with
comorbidities, and symptoms such as dyspnea and Oxygen saturation (< 95%),
confirming results observed in other regions of the world.

Machine Learning for Prognosis of Patients with COVID-19: An Early Days Analysis

The COVID-19 pandemic declared in March 2020 by the World Health Organization (WHO) challenged the health system of several countries with the growing number of infected people. During the pandemic's peak in Europe, the low incidence of infection in South Korea drew the international community's attention, since not long ago that country was considered the epicenter of the pandemic outside its origin, in China. The mass testing protocol and tracing policies were pointed out as the formula for South Korean success, however, in view of the high demand and little supply of diagnostic tests for COVID-19 in the market, this strategy proved to be unfeasible to be implemented mainly in countries with large populations and with few financial resources, such as Brazil. There is also the aggravating factor regarding the effectiveness of the tests currently available, especially the rapid serology test with a high rate of false negatives. In order to offer a screening method for the application of tests, this work aims to develop a predictive model for assisting the identification of COVID-19 infection in suspected patients based on data from clinical laboratory examinations, such as blood count and urine tests. The data used comes from three sources in Sao Paulo and are hosted in the COVID-19 Data Sharing/BR Repository, a shared database of Sao Paulo Research Foundation (FAPESP). The work also proposes a comparison between balanced x imbalanced dataset and traditional x ensemble algorithms for this problem.

Applying machine learning to assist the diagnosis of COVID-19 from blood and urine exams

According to the World Health Organization, 8 million deaths are counted due to gastrointestinal diseases annually. Automatic detection of gastrointestinal landmarks is a task that can further help medical professionals reducing cost and time in exploratory exams. Computer-aided detection and diagnosis systems have been widely explored in the scientific field. However, it takes a lot of processing power to achieve satisfactory results. In order to overcome this problem, this work uses a simple Convolutional Neural Network together with the \textit{Triplet Loss} cost function to extract image characteristics of 3 gastrointestinal anatomical landmarks (z-line, pylorus, and cecum) to classify those images.For the training it's used the dataset Kvasir-v2, obtaining 96,60% of Precision, 97,7\% of Accuracy, 96,91% of Specificity, 98,61% of Recall 97,59% of F1-score.

Classification of anatomical landmarks in the gastrointestinal tract with endoscopy images utilizing convolutional neural networks and triplet loss

This presentation introduces the project of a large multi-genre treebank for Brazilian Portuguese, called Porttinari. We address relevant research questions in its construction and annotation, reporting the work already done. The treebank is affiliated with the “Universal Dependencies” international model, widely adopted in the area, and must be the basis for the development of state of the art tagging and parsing systems for Portuguese, as well as for conducting linguistic studies on morphosyntax and syntax for this language.

PorTinari - a Large Multi-genre Treebank for Brazilian Portuguese

Ampla cobertura lexical constitui um dos pré-requisitos para a robustez de uma gramática computacional. Propomos uma metodologia para povoar a tabela de formas verbais irregulares da PorGram (gramática do português no formalismo HPSG) utilizando os dados do dicionário MorphoBr. Nós implementamos um algoritmo que classifica as formas verbais do MorphoBr em regulares e irregulares, aplicando as regras flexionais da PorGram. Avaliamos o algoritmo com numa amostra de 38 verbos, regulares e irregulares, obtendo os resultados esperados. Uma contribuição adicional foi o melhoramento do MorphoBr, com a eliminação de mais de 270.000 entradas erradas e a inclusão de quase 13.000 entradas faltantes.

Utilizando um Dicionário Morfológico para expandir a cobertura lexical de uma Gramática do Português no formalismo HPSG

Language resources, such as corpora, are fundamental for the development of text processing tools. A resource currently considered fundamental for NLP in Portuguese is the corpus UD Bosque, part of the corpora collection in the Universal Dependencies (UD) project. Despite UD Bosque being originated from a manually revised (golden) corpus, several annotation consistency problems are encountered in its current version. In this work, we present the methodology to correct the problems of morphological annotations in the corpus; in particular, we correct morphological agreements of adjectives, determinants, and nouns. We discuss the errors, exceptions, or non-trivial cases, corrections that we made, and the impact of changes on the corpus on the training of statistical parsers.

Explorando a revisão de corpora por meio da comparação de regras gramaticais em padrões sintáticos

Este trabalho descreve a criação do PetroGold, um treebank padrão ouro para o domínio do óleo & gás. O material é composto por teses, dissertações e monografias, contém 9.127 frases (253.640 tokens) e conta com anotação morfossintática de dependências segundo a abordagem Universal Dependencies. Detalhamos alguns dos desafios linguísticos do domínio para a anotação sintática e verificamos a qualidade do material produzido por meio de uma avaliação intrínseca: utilizando um modelo criado pela ferramenta UDPipe, o corpus leva a 90,65%, 88,53% e 82,88% de acertos conforme as medidas UAS, LAS e CLAS, respectivamente.

PetroGold - Corpus padrão ouro para o domínio do petróleo

Na primeira parte da apresentação apresento a pergunta de pesquisa, assim como a hipótese inicial adotada a partir das medidas de densidade (DeL) e diversidade lexical (DiL), logo em seguida falo sobre o corpus Covid-19 UFABC, trago os resultados para as métricas iniciais adotadas na hipótese e por fim falo sobre o novo indicador Lex-BioMed e seus resultados, por fim apersento as conclusões e trabalhos futuros

Lexicalidade biomédica e sua mensuração em um corpus sobre COVID-19 em língua portuguesa

Este artigo descreve o estudo do desempenho de dois parsers conhecidos para o português com base nas diretrizes do modelo internacional “Universal Dependencies”. Visando mapear o estado da arte na área, os parsers foram avaliados com relação à detecção de algumas relações essenciais do modelo que indicam os argumentos principais dos verbos. Mostramos que o parser UDPipe se destaca entre os parsers avaliados, mas que ainda há muito a avançar na área.

Avaliação de parsers na detecção de relações essenciais do modelo Universal Dependencies para o português

This work presents the stages of compilation of a corpus of the Amazonian Lingua Franca (LGA), or nheengatu, developed for the implementation of a part-of-speech tagger for the noun phrase of this language. The study represents an advance in the construction of a database for indigenous languages and the inclusion of these minority languages in the current scientific and technological context. The results confirm the applicability of the compiled corpus to POS taggers and other natural
language processing algorithms.

Downloads

Next from BRACIS 2021

Machine Learning for Prognosis of Patients with COVID-19: An Early Days Analysis

Similar lecture

Constituintes frasais com Função de Sujeito em Sentenças Judiciais

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES