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Addressing the Challenges of Igbo Computational Morphological Studies Using Frequent Pattern-Based Induction

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Transactions on Engineering Technologies (WCECS 2017)

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Abstract

Computational studies of Igbo language are constrained by non-availability of large electronic corpora of Igbo text, a prerequisite for data-driven morphological induction. Existing unsupervised models, which are frequent-segment based, do not sufficiently address non-concatenative morphology and cascaded affixation prevalent in Igbo morphology , as well achieving affix labelling. This study devised a data-driven model that could induce non-concatenative aspects of Igbo morphology , cascaded affixation and affix labelling using frequent pattern-based induction . Ten-fold Cross Validation (TCV) test was used to validate the propositions using percentages. An average accuracy measure of 88% was returned for the developed model. Ten purposively selected Igbo first speakers also evaluated samples of 100 model-analysed words each and the mean accuracy score of 82% was recorded. We conclude that morphology induction can be realized with a modestly sized corpus, demonstrating that electronic corpora scarcity does not constrain computational morphology studies as it would other higher levels of linguistic analysis.

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Acknowledgements

I acknowledge the entire management and staff of Covenant University, Ota, Nigeria for financing the publication of this material.

The tests and results presented here are those contained in an unpublished thesis of Iheanetu (2015). I acknowledge all my supervisors for their immense contributions to this study.

The Catholic Arch Bishop of Owerri, His Grace, Dr. Amarachi Obinna is highly appreciated for the release and permission to use electronic prints of Odenigbo lecture series. Finally, I acknowledge the management and staff of Africana-Fep publishers for the permission to use Baibụl Nsọ Nhazi Katọlik for the purposes of this study.

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Authors and Affiliations

  1. Department of Computer and Information Sciences, Covenant University, Ota, Nigeria

    Olamma U. Iheanetu

  2. Faculty of Arts, Department of English, University of Ibadan, Ibadan, Nigeria

    Obododimma Oha

Authors
  1. Olamma U. Iheanetu
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  2. Obododimma Oha
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Corresponding author

Correspondence to Olamma U. Iheanetu .

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Editors and Affiliations

  1. International Association of Engineers, Hong Kong, Hong Kong

    Sio-Iong Ao

  2. Department of Computer and Communication, Daegu Catholic University, Daegu, Korea (Republic of)

    Haeng Kon Kim

  3. University of New Orleans, New Orleans, LA, USA

    Mahyar A. Amouzegar

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Iheanetu, O.U., Oha, O. (2019). Addressing the Challenges of Igbo Computational Morphological Studies Using Frequent Pattern-Based Induction. In: Ao, SI., Kim, H., Amouzegar, M. (eds) Transactions on Engineering Technologies. WCECS 2017. Springer, Singapore. https://doi.org/10.1007/978-981-13-2191-7_11

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  • DOI: https://doi.org/10.1007/978-981-13-2191-7_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2190-0

  • Online ISBN: 978-981-13-2191-7

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