Co-reference Analysis Through Descriptor Combination
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Coorientador
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Springer
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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.
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Never-ending language learning, Meta-heuristics, Descriptor combination
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Inglês
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Vipimage 2017. Cham: Springer International Publishing Ag, v. 27, p. 525-534, 2018.
