Co-reference Analysis Through Descriptor Combination

Author(s)

Mansano, A. F. ; Hrushcka, E. R., Jr. ; Papa, J. P. ; Tavares, JMRS ; Jorge, RMN

• Document type: Journal article
• Source: VIPIMAGE 2017; v. 27, p. 10-pg., 2018-01-01.

Abstract

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. (AU)

• FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC