Machine translation is intrinsically associated with the study and development of computerized methodologies for idiomatic translations' production. The most common approaches are the statistical and methods based on neural networks. One of the deficiencies pointed out by these methods is the possible lack of coherence between the translated sentences. In this project, we propose using techniques based on Graph Theory to preserve the coherence in the translation of texts from English to Portuguese. The studied method presents large performance variability; however, some translations produce sentences 90% better evaluated than the statistical translator Moses and 10% superior to Google Translate.

Post-processing of machine translation texts based on graph theory

This study presents an algorithmic strategy to analyze a small set of social network information to monitor the dengue disease. Previous studies have achieved similar results based on large datasets of Twitter microblogs. In this study, we successfully map dengue cases using a small data collection of tweets from a medium-size city. A set of modules were constructed to collect, categorize, and display dengue-related tweets. We compared the collected tweets with real data from confirmed dengue cases. We showed a significant correlation between the number of confirmed dengue cases and the number of dengue-related tweets, even considering such a small dataset. The results of this approach may be relevant in public health policies.

Statistical analysis of small twitter data collection to identify dengue outbreaks

ENIAC: Technical Session 7

technical paper

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.

Lucas Porto

2

Presentations

Post-processing of machine translation texts based on graph theory

Statistical analysis of small twitter data collection to identify dengue outbreaks

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