NELL (Never-Ending Language Learning) is the first never-ending learning system presented in the literature. It has been modeled to create a knowledge based on an autonomous way, reading the web 24 hours per day, 7 days per week. As such, the co-reference analysis has a crucial role in NELL’s learning paradigm. In this paper, we approach a method to combining different feature vectors in order to solve the coreference resolution problem. In order to fulfill this work, an optimization task is devised by meta-heuristic techniques in order to maximize the separability of samples in the feature space, being the optimization process guided by the accuracy of Optimum Path Forest in a validation set. The experiments showed the proposed methodology can obtain much better results when compared to the performance of individual feature extraction algorithms.
CITATION STYLE
Mansano, A. F., Hrushcka, E. R., & Papa, J. P. (2018). Co-reference analysis through descriptor combination. Lecture Notes in Computational Vision and Biomechanics, 27, 525–534. https://doi.org/10.1007/978-3-319-68195-5_57
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