Edilson Anselmo Corrêa Júnior, V. Q. Marinho, L. B. D. Santos, Thales Bertaglia, Marcos Vinícius Treviso, H. Brum
{"title":"PELESent: Cross-Domain Polarity Classification Using Distant Supervision","authors":"Edilson Anselmo Corrêa Júnior, V. Q. Marinho, L. B. D. Santos, Thales Bertaglia, Marcos Vinícius Treviso, H. Brum","doi":"10.1109/BRACIS.2017.45","DOIUrl":"https://doi.org/10.1109/BRACIS.2017.45","url":null,"abstract":"The enormous amount of texts published daily by Internet users has fostered the development of methods to analyze this content in several natural language processing areas, such as sentiment analysis. The main goal of this task is to classify the polarity of a message. Even though many approaches have been proposed for sentiment analysis, some of the most successful ones rely on the availability of large annotated corpus, which is an expensive and time-consuming process. In recent years, distant supervision has been used to obtain larger datasets. So, inspired by these techniques, in this paper we extend such approaches to incorporate popular graphic symbols used in electronic messages, the emojis, in order to create a large sentiment corpus for Portuguese. Trained on almost one million tweets, several models were tested in both same domain and cross-domain corpora. Our methods obtained very competitive results in five annotated corpora from mixed domains (Twitter and product reviews), which proves the domain-independent property of such approach. In addition, our results suggest that the combination of emoticons and emojis is able to properly capture the sentiment of a message.","PeriodicalId":202240,"journal":{"name":"2017 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126143841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Efficient Gaussian Process-Based Inference for Modelling Spatio-Temporal Dengue Fever","authors":"J. Albinati, Wagner Meira Jr, G. Pappa, A. Wilson","doi":"10.1109/BRACIS.2017.13","DOIUrl":"https://doi.org/10.1109/BRACIS.2017.13","url":null,"abstract":"Dengue fever is a disease that affects hundreds of millions of people every year worldwide. Despite its wide presence around the world, it still requires accurate early warning systems. In this paper, we propose an accurate model to forecast dengue fever incidence at hundreds of Brazilian cities simultaneously. In order to assure efficiency, we devise two strategies to reduce computational effort required for inference under the proposed model. As a result, we not only reduce the computational effort that would be required to fit each model per city, but also increase the accuracy by inducing spatial dependences between cities. These dependences do not require human specification and are learned from data, leading to more accurate predictions than using typical neighborhood or distance-based methods.","PeriodicalId":202240,"journal":{"name":"2017 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121935461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Acoustic Modeling Using a Shallow CNN-HTSVM Architecture","authors":"C. Shulby, M. D. Ferreira, R. Mello, S. Aluísio","doi":"10.1109/BRACIS.2017.62","DOIUrl":"https://doi.org/10.1109/BRACIS.2017.62","url":null,"abstract":"High-accuracy speech recognition is especially challenging when large datasets are not available. It is possible to bridge this gap with careful and knowledge-driven parsing combined with the biologically inspired CNN and the learning guarantees of the Vapnik Chervonenkis (VC) theory. This work presents a Shallow-CNN-HTSVM (Hierarchical Tree Support Vector Machine classifier) architecture which uses a predefined knowledge-based set of rules with statistical machine learning techniques. Here we show that gross errors present even in state-of-the-art systems can be avoided and that an accurate acoustic model can be built in a hierarchical fashion. The CNNHTSVM acoustic model outperforms traditional GMM-HMM (Gaussian Mixture Model - Hidden Markov Model) models and the HTSVM structure outperforms a MLP multi-class classifier. More importantly we isolate the performance of the acoustic model and provide results on both the frame and phoneme level, considering the true robustness of the model. We show that even with a small amount of data, accurate and robust recognition rates can be obtained.","PeriodicalId":202240,"journal":{"name":"2017 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131715915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}