Brazil

Depression is a psychological disorder that affects millions of people in the world, regardless of their age, social class, or nationality. Different techniques have been studied to analyze and recognize this disease in the literature, such as Natural Language Processing, Sentiment Analysis, and Machine Learning. However, in Brazilian Portuguese, only a few studies have been focused on creating a dataset to classify symptoms of depression. In this paper, we propose a novel text dataset composed of sentences that refer to the main symptoms of depression: behavioral, physiological, and psychological. Initial experiments exploring different machine learning algorithms in the dataset obtained high accurate performance values, which shows that the study is promising to encourage research in this area.

BRACIS 2021

DP-Symptom-Identifier: uma estratégia para classificar sintomas de depressão utilizando um conjunto de dados textuais na língua portuguesa

applications of natural language processing

corpus linguistics

machine learning

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.

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**.

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/)

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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

In this study, linguistic features were listed for classification of triples,based on a parallel corpus in Galician, Brazilian Portuguese (PT-BR) and European Spanish (EE). In the experiments, at the syntactic level, a relevant per-formance of features that offer greater difficulty to extract valid triples was ob-served, such as the corelated null object/subject and the verb-subject inversion,as well as relational triples that form ungrammatical sub-sentences. At the mor-phological level, it was observed that the grammatical class of the initial wordof each sub-sentence, especially when they are prepositions, were relevant forthe classification of the triples


Engenharia de features linguísticas para classificação de triplas relacionais

This article describes a methodology that was developed to review Emocionário, a lexicon of emotion words in Portugese from Linguateca's AC/DC project. This first portion of the review was carried out in seven of the 24 groups of Emocionário, being them “Desespero”, “Esperança”, “Humildade”, “Pena”, “Satisfação”, “Saudade” and “Surpresa”. The methodology is then illustrated, making use of examples taken from the corpus itself for each of the steps. Some of the main changes are documented and discussed at the end.

Descrição de uma metodologia desenvolvida para revisão semiautomática de um léxico de palavras de emoção

Um estudo exploratório de exposição de participantes às variantes de uma variável linguística saliente do ponto de vista social, a palatalização progressiva, foi realizado com o uso de rastreamento ocular, para examinar o processamento da variação linguística. Os resultados mostram que exposição à variante estigmatizada captou a atenção e aumentou a dilatação da pupila dos participantes, o que pode ser interpretado como evidência de uma resposta emocional.

Respostas emocionais da variação linguística: Análise exploratória de rastreio ocular

This article presents an analysis of the textual complexity of satirical and true news for Brazilian Portuguese. The Fake has been a big problem nowadays. Satirical content is an important point in the automatic detection of false news as its use can cause underlying confusion in the analysis. To carry out this research, the NILC-Metrix tool was applied, and 16 measures were evaluated, including descriptive, syntactic and semantic aspects, noting a greater complexity for real texts.

Complexidade textual em notícias satíricas: uma análise para o português do Brasil

This work describes the syntactic organization of a set of Judgments of the Special Civil Courts, whose documents must be accessible to the lay citizen without the assistance of a lawyer. The phrasal constituents with subject function in 110 Judgments are highlighted in the light of Terminology studies, Corpus Linguistics and research on reading comprehension. The study found that most of these constituents assume forms that tend to demand greater burden in reading and to exhibit traits that hardly coincide with patterns of written daily language, which makes their intelligibility difficult for the lay public.

Constituintes frasais com Função de Sujeito em Sentenças Judiciais

As large amounts of unstructured data are generated on a regular basis, expressing or storing knowledge in a way that is useful remains a challenge. In this context, Relation Extraction (RE) is the task of automatically identifying relationships in unstructured textual data. Thus, we investigated the relation extraction on unstructured e-commerce data from the smartphone domain, using a BERT model fine-tuned for this task. We conducted two experiments to acknowledge how much relational information it is possible to extract from product sheets (structured data) and product titles (unstructured data), and a third experiment to compare both. Analysis shows that extracting relations within a title can retrieve correct relations that are not evident on the related sheet.

Relation extraction in structured and unstructured data:a comparative investigation on smartphone titles in thee-commerce domain

O aprendizado multimodal visa explorar as características das diversas modalidades (texto, imagem, áudio) para gerar modelos computacionais. No comercio eletrônico, devido à grande variedade das características dos produtos e a ausência ou inconsistência de informações, a combinação de informações de modos diferentes vem a ser bastante adequada. Neste trabalho são apresentados alguns experimentos para a classificação multimodal (texto e imagem) de produtos (produtos adultos) que não podem ser vendidos no marketplace da empresa parceira. Nesses experimentos, redes neurais foram usadas para treinar classificadores uni e multimodal. O classificador multimodal atingiu 99% de F1 contra 98% do modelo textual e 94% do visual.

Classificacão multimodal de produtos que não podem ser vendidos no marketplace

Textual similarity deals with determining how similar two pieces of texts are, considering the lexical (surface forms) or semantic (meaning) close-ness. In this paper we applied word embeddings for measuring e-commerce product title similarity in Brazilian Portuguese. We generated some domain-specific word embeddings (using Word2Vec, FastText and GloVe) and compared them with general-domain models (word embeddings and BERT models). We concluded that the cosine similarity calculated using the domain-specific word embeddings was a good approach to distinguish between similar and non-similar products, but the multilingual BERT pre-trained model proved to be thebest one.

Measuring Brazilian Portuguese Product Titles Similarity using Embeddings

In this paper, we show how a Portuguese BERT model can be combined with structured data in order to deploy a chatbot based on a finite state machine to create a conversational AI system that helps a real-estate company to predict its client’s contact motivation. The model achieves human level results in a dataset that containts 235 unbalanced labels. Then, we also show its benefits considering the business impact comparing it against classical NLP methods.

Augmenting Customer Support with an NLP-based Receptionist

This work explores speech as a biomarker and investigates the detection of respiratory insufficiency (RI) by analyzing speech samples. 
Previous work constructed a dataset of respiratory insufficiency COVID-19 patient utterances and analyzed it by means of a convolutional neural network achieving an accuracy of $87.04\%$, validating the hypothesis that one can detect RI through speech. 
Here, we study how Transformer neural network architectures can improve the performance on RI detection. 
This approach enables construction of an acoustic model. By choosing the correct pretraining technique, we generate a self-supervised acoustic model, leading to improved performance ($96.53\%$) of Transformers for RI detection.

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