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

Iheanetu, O.U. and Oha, O. (2017) Addressing the Challenges of Igbo Computational Morphological Studies Using Frequent Pattern-Based Induction. In: The World Congress on Engineering and Computer Science, 25 October 2018, Online.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Computational morphology Frequent pattern-based morphology Igbo computational morphology Igbo morphology Rule-based learning Morphology induction
Subjects: P Language and Literature > P Philology. Linguistics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Patricia Nwokealisi
Date Deposited: 06 Mar 2020 07:54
Last Modified: 06 Mar 2020 07:54

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