Automatic Text Simplification (ATS) has played a significant role in the Natural Language Processing (NLP) field.
ATS is a sequence-to-sequence problem aiming to create a new version of the original text removing complex and domain-specific words. 
It can improve communication and understanding of documents from specific domains, as well as support second language learning. 
This paper presents an empirical study on the use of state-of-the-art ATS methods to simplify texts in Portuguese. It is important to remark that the literature reports the challenge in analyzing Portuguese texts due to the lack of resources compared to other languages (i.e., English).
More specifically, this work evaluated different Neural Machine Translation (NMT) techniques for ATS in Portuguese. The experiments showed that NMT achieved promising results in Portuguese texts, obtaining 40.89 BLEU score using multiple parallel corpora and raising the overall readability score by more than 5 points.

Portuguese Neural Text Simplification using Machine Translation

The judicial system is composed of numerous court documents. These documents may contain relevant information that supports decision-making in future processes. However, collecting this information is not a trivial task. This article proposes the use of clustering techniques to group similar court lawsuits and facilitate the collection of information. In this way, different approaches were evaluated for identifying the most appropriate to perform this task. The approaches were applied to a database composed of 1515 facts of initial petitions. These approaches were evaluated using internal metrics and texts of the grouped court lawsuits. The results showed that the best approach to grouping court lawsuits is composed of the K-Means algorithm and the TF-IDF representation technique in combination with the PCA technique.

Assessment of text clustering approaches for legal documents

In Brazil, several minority languages suffer a serious risk of extinction. The appropriate documentation of such languages is a fundamental step to avoid that. However, for some of those languages, only a small amount of text corpora is digitally accessible.
Meanwhile, there are many issues related to the identification of indigenous languages, which may help to identify key similarities among them, as well as to connect related languages and dialects.
Therefore, this paper proposes to study and automatically classify 26
neglected Brazilian native languages, considering a small amount of training data, under a supervised and unsupervised setting. Our findings indicate that the use of machine learning models in the analysis of Brazilian Indigenous corpora is very promising, and we hope this work encourage more research on this topic in the next years.

Analysis of a Brazilian Indigenous corpus using machine learning methods

Over the past few years, information technology has been transforming the legal world, automating processes and, consequently, reducing the time
needed to create and analyse digital legal documents. One of the most studied
problems in this area is the recognition of named entities (NER) in unstructured
texts. Previous work has not addressed the detection of legal entities through the
application of models based on neural networks available in natural language
processing libraries. In this article, the use of the libraries Spacy and FLAIR
were analyzed in the context of NER in initial petitions. The models were trained with pre-defined architectures and evaluated in two corpora, one of them
developed in the scope of this work. The results obtained with these experiments
demonstrated good results with both platforms Spacy and FLAIR, with superior
performance when adopting BiLSTM-CRF with FLAIR embeddings.


Named entity recognition in Portuguese legal documents using deep learning

The initial petition is one of the most important components of a civil litigation process. Automating the analysis of these documents might reduce the time necessary for the postulatory phase's conclusion. The parties qualification body is the section in which are exposed the informations about the entities involved in the process. This paper suggests the employment of named entity extraction techniques on the problem of information extraction and recognition on initial petitions. With this in mind, was created a part description body corpora extracted from Brazilian courts. Seven BiLSTM-CRF models with distinct combinations of vector representations of words were trained, evaluated, and compared to investigate their effects on the performance of an algorithm with that architecture and, in this way, improve the recognition of legal entities in legal texts. Unlike other works based on BiLSTM-CRF for NER tasks in the legal domain, this research emphasizes not the architectures employed, but rather the text representation methods used. The experiments performed with the developed corpus show that the stacking of character, word, and pooled FLAIR embeddings is the preferred combination to extract the best possible performance from BiLSTM-CRF hybrid models.

A comparative analysis of text embedding approach to extract named entities in Portuguese legal documents

Policy Search (PS) methods have been used to learn optimization algorithms, obtaining encouraging results. In this work, we used PS methods to learn optimization algorithms for global optimization problems, considering a little-studied scenario: high-dimensional functions and the optimization algorithms do not have access to the derivatives of the function to be optimized. The results indicate that, despite the difficulties, the learned optimization algorithms have promising performance in the studied scenario.

Zeroth Order Policy Search Methods for Global Optimization Problems: An Experimental Study

O envio de feedback é uma etapa fundamental do processo de ensino aprendizagem. É através do feedback que o aluno percebe os seus erros e acertos o que o leva a refletir sobre a aprendizagem e continuar buscando novos caminhos para desenvolver as habilidades propostas durante a disciplina. Apesar de ser um processo importante, enviar feedback é bastante custoso e trabalhoso. Por isso, muitas vezes os professores não são capazes de conciliar essa atividade com as outras demandas das associadas a docência. Diante deste contexto o projeto feedback bot propõe a utilização de inteligência artificial para auxiliar o professor no envio de mensagens de feedback. Este projeto busca propor um produto que responda a seguinte pergunta: “Como melhorar o feedback enviado aos estudantes de modo que ele seja de qualidade, personalizado e escalável?”.


GT-FeedbackBot

Convolutional Neural Networks (CNNs) is the main solution for image classification tasks in different applications. However, selecting the most suitable CNN and its parameters for a given image dataset is usually performed by trial and error, which may take much time and computational cost. This paper proposes the Dataset2Vec method and employs Meta-Learning (MtL) to select CNN architectures for image classification. Dataset2Vec adopts a deep neural network to extract features from images datasets, embedding them in a single feature vector. To evaluate the proposed solution, it was adopted to select among six CNN algorithms for 45 two-classes image datasets. The results showed that the MtL using Dataset2Vec outperformed by different baseline methods in all performance measures evaluated, indicating the proposal was able to extract representative features from datasets of images for CNN selection.

Meta-Characteristics Extraction from Image Datasets for Selection of Convolutional Neural Networks

The classification and separation of documents is a crucial step in the
analysis of court lawsuits. Deep learning algorithms have achieved promising
results in this task, extracting relevant information from the texts of these documents. However, court documents have become increasingly heterogeneous,
i.e., photos, receipts, text documents, etc., directly impacting classification accuracy. This work investigates the use of multimodal convolutional networks,
combining characteristics extracted from texts and images to classify court lawsuits pages. Two multimodal approaches were compared with four single-mode.
In terms of accuracy and kappa, all algorithms were evaluated in a database
composed of 117 lawsuits. The results showed that the approach that achieved
the best performance is multimodal, presenting effectiveness and efficiency in
the classification of court lawsuits pages.


Classification of Court Lawsuits Pages using MultimodalConvolution Neural Networks


**Chairs:** Marlo Vieira Dos Santos e Souza,	Karina Valdivia and	Andre Britto



Text Mining and Natural Language Processing II

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). <br>**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. <br>**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**.<br><br><br>

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 2021

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


 **Chairs:** Nádia Félix Felipe da Silva, Elaine Faria and Paulo T. Guerra 
<br>



Natural Language Processing II


 **Chair:** Helena de Medeiros Caseli



Natural Language Processing in Portuguese

**Chairs:** Anna Helena Reali Costa, Elaine Faria and Paulo T. Guerra 



Deep Learning

WRNP 2020 Poster Exhibition

poster

The Brazilian National Research and Education Network (RNP) holds the RNP Workshop (WRNP) annually. Promoted since 1999, the event aims to bring the public closer to technological advances and the main topics under discussion in the area of ​​research and development of Information and Communication Technologies (ICT) inside and outside Brazil.

**See some of the subjects that will be covered:**

Experimental research on computer networks and the Internet of the Future.

Virtual labs and platforms for experimentation

Software-defined infrastructures (SDI / SDN)

Virtualization of network functions (NFVs)

Edge and Cloud Computing

Telco clouds

Digital Identity Management

Computing and intensive data transmission for e-science applications

Network measurement and monitoring

Applications for 100Gbp / s networks

Technologies for optical networks

Elastic and open optical networks (breakdown and whiteboxes)

5G networks

Internet of Things (IoT) and smart Campus applications

Internet of Things (IoT) and applications for industry 4.0

Quantum networking

Cybersecurity

Blockchain

Telemedicine

Telepresence and video collaboration

Open data in computer networks and computer science

Innovation and entrepreneurship in internet applications


<p># PARTNERS

Support

[![](https://assets.underline.io/uploads/markdown_image/1/image/6411117e65695b990b130c992e8b9d3d.png)](https://cgi.br/)
<p><iframe width="560" height="315" src="https://www.youtube.com/embed/-0_A_BExNTQ" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>



WRNP 2020

The Brazilian National Research and Education Network (RNP) holds the RNP Workshop (WRNP) annually. Promoted since 1999, the event aims to bring the public closer to technological advances and the main topics under discussion in the area of ••research and development of Information and Communication Technologies (ICT) inside and outside Brazil.
​

ENIAC: Technical Session 4

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

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. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

Rafael Mello

9

4

SHORT BIO

Presentations

Portuguese Neural Text Simplification using Machine Translation

Assessment of text clustering approaches for legal documents

Analysis of a Brazilian Indigenous corpus using machine learning methods

Named entity recognition in Portuguese legal documents using deep learning

A comparative analysis of text embedding approach to extract named entities in Portuguese legal documents

Zeroth Order Policy Search Methods for Global Optimization Problems: An Experimental Study

GT-FeedbackBot

Meta-Characteristics Extraction from Image Datasets for Selection of Convolutional Neural Networks

Classification of Court Lawsuits Pages using MultimodalConvolution Neural Networks

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES