Luis Gregorio Moyano

GraPhys: Understanding Health Care Insurance Data through Graph Analytics.

Healthcare insurance data represent a rich source of information and has the potential to contribute significantly in guiding business decision making. In this work we present GraPhys, a Graph Analysis platform designed for exploration, visualization and analysis of healthcare insurance data and its corresponding metadata. By taking advantage of relationships contained in healthcare claims data, we are able to apply Graph Analytics methods and algorithms in order to devise useful business metrics to guide data analysis and exploration. Our tool focuses in better understanding physicians, patients and their practices. We illustrate our approach by demonstrating two use cases where we show how graph analytics metrics, combined with other data, may lead to useful insights not directly available to traditional Business Analytics.

A Multiple Classifier System for Classifying Life Events on Social Media

In this work we present a Conversation Classifierbased on Multiple Classifiers, to detect Life Events on SocialMedia. In one hand, conversations can provide more contextand help disambiguate life event detection, compared with single posts. On the other hand, the increase in number of messages and the way they interact with each other within the conversation cannot be trivially modeled by a classifier. To tackle this problem, we focus on creating a set of classifiers from different feature sets, and combining their classification outputs to improve accuracy. The experiments show that multiple classifiers are promising for this problem, being able to present an increase of about 45% in the F-Score.

Building a Question-Answering Corpus Using Social Media and News Articles

Is it possible to develop a reliable QA-Corpus using social media data? What are the challenges faced when attempting such a task? In this paper, we discuss these questions and present our findings when developing a QA-Corpus on the topic of Brazilian finance. In order to populate our corpus, we relied on opinions from experts on Brazilian finance that are active on the Twitter application. From these experts, we extracted information from news websites that are used as answers in the corpus. Moreover, to effectively provide rankings of answers to questions, we employ novel word vector based similarity measures between short sentences (that accounts for both questions and Tweets). We validated our methods on a recently released dataset of similarity between short Portuguese sentences. Finally, we also discuss the effectiveness of our approach when used to rank answers to questions from real users.