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Ibero-American Conference on Artificial Intelligence

IBERAMIA 2016: Advances in Artificial Intelligence - IBERAMIA 2016 pp 115–126Cite as

Enhancing Semi-supevised Text Classification Using Document Summaries

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10022)

Abstract

The vast amount of electronic documents available on the Internet demands for automatic tools that help people finding, organizing and easily accessing to all this information. Although current text classification methods have alleviated some of the above problems, such strategies depend on having a large and reliable set of labeled data. In order to overcome such limitation, this work proposes an alternative approach for semi-supervised text classification, which is based on a new strategy for diminishing the sensitivity to the noise contained on labeled data by means of automatic text summarization. Experimental results showed that our proposed approach outperforms traditional semi-supervised text classification techniques; additionally, our results also indicate that our approach is suitable for learning from only one labeled example per category.

Keywords

  • Text classification
  • Text summarization
  • Semi-supervised learning
  • Self-training
  • Feature selection

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Fig. 1.
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Fig. 3.

Notes

  1. 1.

    Normally the direction of the edges is determined by the order of the sentences in the original document.

  2. 2.

    The parameter that defines the length of a summary is also known as the compression rate parameter, and represents a number that indicates the percentage of the information that we are requiring to preserve from the original document.

  3. 3.

    One disadvantage of self-training is that mistakes reinforce/strengthen themselves; it is well known that accuracies lower than random at the beginning tend to conduct to worst results in subsequent iterations.

  4. 4.

    http://www.cs.waikato.ac.nz/ml/weka/.

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Acknowledgments

This work was partially funded by CONACyT, project number 247870 and 258588. We appreciate the support provided by the Thematic Networks program (Language Technologies Thematic Network projects 260178 and 271622). We thank to UAM Cuajimalpa and SNI for their support.

Author information

Authors and Affiliations

  1. Language Technologies Laboratory, Computational Sciences Department, Instituto Nacional de Astrofísica Óptica y Electrónica (INAOE), Puebla, Mexico

    Emmanuel Anguiano, Manuel Montes-y-Gómez & Luis Villaseñor-Pineda

  2. Language and Reasoning Research Group, Information Technologies Department, Universidad Autónoma Metropolitana (UAM) Unidad Cuajimalpa, Mexico City, Mexico

    Esaú Villatoro-Tello & Gabriela Ramírez-de-la-Rosa

Authors
  1. Esaú Villatoro-Tello
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  2. Emmanuel Anguiano
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  3. Manuel Montes-y-Gómez
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  4. Luis Villaseñor-Pineda
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  5. Gabriela Ramírez-de-la-Rosa
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Corresponding author

Correspondence to Esaú Villatoro-Tello .

Editor information

Editors and Affiliations

  1. INAOE , Tonantzintla, Mexico

    Manuel Montes y Gómez

  2. Astrofisica Optica y Electronica, INAOE , Puebla, Mexico

    Hugo Jair Escalante

  3. Universidad Nacional de Costa Rica , Heredia, Costa Rica

    Alberto Segura

  4. Universidad Nacional de Costa Rica , Heredia, Costa Rica

    Juan de Dios Murillo

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Villatoro-Tello, E., Anguiano, E., Montes-y-Gómez, M., Villaseñor-Pineda, L., Ramírez-de-la-Rosa, G. (2016). Enhancing Semi-supevised Text Classification Using Document Summaries. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham. https://doi.org/10.1007/978-3-319-47955-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-47955-2_10

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