Abstract
Text classifiers that extract their features with pure statistical methods are not very useful when there is an extended range of types to classify. They also lack a deeper understanding of the classified data. The use of some semantic methods can improve the efficiency and effectiveness of the purely quantitative approach. This work explores the use of a semantic approach based on a similarity measure to build a vector model containing some semantic evidence. This vector model is used to improve a Maximum Entropy-based text classifier. Experiments show that the F-measures obtained using this approach are competitive. One may conclude that the use of semantic analysis is an excellent complement to statistical approaches and produces better performance and high-grade results.
L. M. Escobar-Vega—The authors would like to thank the Instituto Tecnológico y de Estudios Superiores de Occidente (ITESO) of Mexico for the resources provided for this research. Also, the main author would like to thank the National Council of Science and Technology (CONACYT) of Mexico for the sponsoring of this research by the scholarship number 399053.
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Escobar-Vega, L.M., Zaldívar-Carrillo, V.H., Villalon-Turrubiates, I. (2018). Comparative Analysis and Implementation of Semantic-Based Classifiers. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Computational Intelligence. MICAI 2018. Lecture Notes in Computer Science(), vol 11289. Springer, Cham. https://doi.org/10.1007/978-3-030-04497-8_7
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