Methodology and Results for the Competition on Semantic Similarity Evaluation and Entailment Recognition for PROPOR 2016

In this paper, we present the methodology and the results obtained by our teams, dubbed Blue Man Group, in the ASSIN (from the Portuguese {\it Avalia\c{c}\~ao de Similaridade Sem\^antica e Infer\^encia Textual}) competition, held at PROPOR 2016\footnote{International Conference on the Computational Processing of the Portuguese Language - http://propor2016.di.fc.ul.pt/}. Our team's strategy consisted of evaluating methods based on semantic word vectors, following two distinct directions: 1) to make use of low-dimensional, compact, feature sets, and 2) deep learning-based strategies dealing with high-dimensional feature vectors. Evaluation results demonstrated that the first strategy was more promising, so that the results from the second strategy have been discarded. As a result, by considering the best run of each of the six teams, we have been able to achieve the best accuracy and F1 values in entailment recognition, in the Brazilian Portuguese set, and the best F1 score overall. In the semantic similarity task, our team was ranked second in the Brazilian Portuguese set, and third considering both sets.

Multi-scale Forest Species Recognition Systems for Reduced Cost

This work focuses on cost reduction methods for forest species recognition systems. Current state-of-the-art shows that the accuracy of these systems have increased considerably in the past years, but the cost in time to perform the recognition of input samples has also increased proportionally. For this reason, in this work we focus on investigating methods for cost reduction locally (at either feature extraction or classification level individually) and globally (at both levels combined), and evaluate two main aspects: 1) the impact in cost reduction, given the proposed measures for it; and 2) the impact in recognition accuracy. The experimental evaluation conducted on two forest species datasets demonstrated that, with global cost reduction, the cost of the system can be reduced to less than 1/20 and recognition rates that are better than those of the original system can be achieved.