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International Conference on Soft Computing and Pattern Recognition

SoCPaR 2017: Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017) pp 161–170Cite as

Particle Swarm Optimization as a New Measure of Machine Translation Efficiency

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 737)

Abstract

The present work proposes a new approach to measuring efficiency of evolutionary algorithm-based Machine Translation. We implement some attributes of evolutionary algorithms performing cosine similarity objective function of a Particle Swarm Optimization (PSO) algorithm then, we evaluate an English text set for translation precision into the Spanish text as a simulated benchmark, and explore the backward process. Our results show that PSO algorithm can be used for translation of multiple language sentences with one identifier only, in other words the technology presented is language-pair independent. Specifically, we indicate that our cosine similarity objective function improves the velocity attribute of the PSO algorithm, making the complex cost functions unnecessary.

Keywords

  • Evolutionary algorithms
  • Machine Translation
  • Cosine similarity

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  • DOI: 10.1007/978-3-319-76357-6_16
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Acknowledgements

The project is supported by a research grant No. DSA/103.5/16/10473 awarded by PRODEP and by Autonomous University of Ciudad Juarez in Mexico. Title - Detection of Cardiac Arrhythmia Patterns through Adaptive Analysis.

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Authors and Affiliations

  1. Instituto Tecnológico Superior Zacatecas Norte, Río Grande, Zacatecas, Mexico

    José Angel Montes Olguín

  2. Opole University, pl. Kopernika 11a, 45-040, Opole, Poland

    Jolanta Mizera-Pietraszko

  3. Universidad Autonoma de Ciudad Juarez, Av. Del Charro #450 norte, Partido Romero, Ciudad Juarez, 32310, Chihuahua, Mexico

    Ricardo Rodriguez Jorge & Edgar Alonso Martínez García

Authors
  1. José Angel Montes Olguín
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  2. Jolanta Mizera-Pietraszko
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  3. Ricardo Rodriguez Jorge
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  4. Edgar Alonso Martínez García
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Corresponding author

Correspondence to José Angel Montes Olguín .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Prof. Dr. Ajith Abraham

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Dr. Abdelkrim Haqiq

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Dr. Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Dr. Niketa Gandhi

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Montes Olguín, J.A., Mizera-Pietraszko, J., Rodriguez Jorge, R., Martínez García, E.A. (2018). Particle Swarm Optimization as a New Measure of Machine Translation Efficiency. In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). SoCPaR 2017. Advances in Intelligent Systems and Computing, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-76357-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-76357-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76356-9

  • Online ISBN: 978-3-319-76357-6

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