Brazil

In recent years, researchers in the area of Computational Creativity have studied the human creative process proposing different approaches to reproduce it with a formal procedure. We introduce a model for the generation of literary rhymes in Spanish, combining structures of language and neural network models Word2vec, into a structure for semantic assimilation. The results obtained with a manual evaluation of the texts generated by our algorithm are encouraging

BRACIS 2021

A Preliminary Study for Literary Rhyme Generation based on Neuronal Representation, Semantics Resources and Shallow Parsing

semantic analysis

word2vec

text generation

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

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

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Organized by C4AI, the 10th Brazilian Conference on Intelligent Systems (BRACIS) will take place from November 29th to December 3rd, 2021.

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.

Audio MFCC-gram Transformers for respiratory insufficiency detection in COVID-19 disease

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.

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

A depressão é uma das questões de saúde mental mais preocupantes da atualidade. No Brasil, em 2019, 10,2% da população adulta relatou ter sido diagnosticada com depressão segundo dados da Pesquisa Nacional de Saúde. Identificar pessoas com perfil possivelmente depressivo permite um acompanhamento adequado por parte dos profissionais de saúde mental. Nesse sentido, as redes sociais online, como o Twitter, podem ser importantes aliadas. Este artigo apresenta experimentos realizados para a classificação automática de postagens (e não usuários) do Twitter contendo conteúdo que denota algum sintoma de depressão. A classificação com regressão logística apresentou os melhores resultados (F1 média de 57\%) entre os algoritmos investigados.

Identificando sintomas de depressão em postagens do Twitter em português do Brasil

Os danos causados por notícias falsas ou enganosas têm se potencializado graças à facilidade com que as informações são disseminadas em redes sociais. Durante a pandemia do Covid-19, iniciada em 2020, tais notícias foram capazes de gerar pânico na população, além de instruir erroneamente as pessoas sobre a prevenção da doença. O presente trabalho introduz um novo corpus a partir de postagens no Twitter na língua portuguesa com desinformações sobre a Covid-19. Além do novo corpus, o trabalho avalia diferentes abordagens de representações textuais e algoritmos de aprendizagem na tarefa de detecção de mensagens contendo desinformação. O melhor resultado obtido alcançou F1-score de 89% no modelo de classificação SVM com a representação textual TF-IDF.

Detecção de desinformação sobre Covid-19 no Twitter

Automatic text summarization aims at condensing the contents of a text into a simple and descriptive summary. Summarization techniques drastically benefited from the recent advances in Deep Learning. Nevertheless, these techniques are still unable to properly deal with long texts. In this work, we investigate whether the combination of summaries extracted from multiple sections of long scientific texts may enhance the quality of the summary for the whole document. We conduct experiments on real world corpus to assess the effectiveness of our proposal. The results show that our multi-section proposal are as good as summaries generated using the entire text as input and twice as good as single section.

A Preliminary Study for Literary Rhyme Generation based on Neuronal Representation, Semantics Resources and Shallow Parsing

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Constituintes frasais com Função de Sujeito em Sentenças Judiciais

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